Agenda

Additively Manufactured & AI Optimized Millimetre-Wave Waveguide Filter Design for Space Applications - The Journey from Design to Manufacturing

Presented By
Laila Salman (Ansys Canada Ltd.)

Chaired By
Chair Satheesh Kandasamy

Authored By
Laila Salman (Ansys Canada Ltd.)
Sunil Acharya (Ansys Inc)
Koen Huybrechts
Loren Vancleef (3D Systems)
Diamond Liu (SynMatrix Technologies Inc.)
Gada Saad (Scientific Microwave Corp.)

High power light weight RF passive components are critical for satellite communications payloads and are required to handle increasingly larger peak/average powers over the frequency range in the transmit path of the communication link. Powder-bed fusion metal Additive Manufacturing (AM) process has matured as a breakthrough technology for the development of arbitrary shape RF and microwave components where conventional machining can be complex or impossible to use. Thus, allows the exploration of unique and optimized parts and assembly consolidation. When it comes to space applications, industries rely on testing to mitigate system failure. Reproducing space conditions for validation in laboratory setting is costly, time-consuming, and ultimately unreliable. Hence, leveraging the use of Artificial Intelligence (AI) optimization techniques in the design process of RF components is impactful. With next generation of satellites, built from fifth generation (5G) architecture, communication signals will beam down from space supporting terrestrial 5G infrastructure on Earth. The presented work demonstrates the use of physics-based simulation technologies from modelling/synthesize to manufacturing process of an optimized millimetre-wave band pass filter that can be safely launched to space for satellite communication. The proposed design manufacturing workflow can be generalized to a wide variety of radio frequency applications such as phased array antennas with complex feeding networks or multi-beam active antenna array with polarizers, couplers, and power dividers. Advanced AI optimization techniques are applied to optimize the proposed 28GHz waveguide band pass filter and maintain standard design specifications. The process simulation methodologies for the selective laser sintering allows for reducing waste and build time while maintaining adequate support structure during the manufacturing process of assembly. Internal mechanical support requirements can generate additional postprocessing challenges. This is where CAE based support generation tools can be well utilized to find the optimum build time while minimizing the distortion tendencies and the support volume. Once the optimum supports structural topology and build orientations are obtained, a coupled thermal-structural FEA simulation can be used to simulate the AM build process and estimate the residual stresses in the build for a specific build orientation and support strategy. This is important in satellite communications technology where any reduction in payload weight is considered advantageous. Measurements were obtained for the additively manufactured parts with different conducting material properties and compared to simulated results.

Acceleration of Stamping Simulation using an Advanced Artificial Intelligence Framework

Authored & Presented By
Dylan Budnick (Xccelerate AI)

Chaired By
Chair Satheesh Kandasamy

Complex simulations in engineering traditionally demands extensive computational power and time, sometimes taking days to weeks to complete, having a negative impact on project schedules and budgets. Xccelerate AI has developed an AI-based framework to swiftly predict simulation results, dramatically speeding up complex engineering simulations. The framework operates on a straightforward concept: it learns from existing simulation data to predict results for new designs. Many organizations already possess the requisite simulation data for model training, enabling fast implementation of a bespoke surrogate model tailored to an application. We will present an application of this framework related to automotive sheet metal stamping. In modern automotive engineering, vehicles are primarily designed in the virtual space to enable a rapid vehicle design process. This can lead to rapid improvements in vehicle design as more design cycles may be performed. However, this process is heavily constrained by the time and computational requirements necessary to generate the vast number of simulations conducted for vehicle design. Fortunately, modern artificial intelligence (AI) techniques may be used to drastically accelerate the generation of new simulation results. To demonstrate the approach, a cross shaped punch/die geometry is created including the punch, binder, die, and blank. A non-linear explicit dynamic finite element package is used to generate 2500 simulations with 13 parameterized input variables. These serve as the data set to train our machine learning model. In a commercial setting, existing prior simulation datasets would instead be used. This work presents for the first time an advanced recurrent deep learning framework capable of predicting the entire simulated stamping response for new geometries, materials, and process parameters. The predicted response is compared to simulated finite element results demonstrating a prediction time reduction of 740,000 times while maintaining high accuracy in key performance indicators of the stamping process. The AI-based framework thereby offers a significant opportunity to save development time and cost, for example, by enabling a more rapid evaluation of different conceptual designs. Finally, this work also highlights how this framework may be leveraged to further improve metal forming processes.

AI-Surrogates and Concept Engineering: Navigating Challenges and Solutions in Accelerated Product Development

Presented By
Ashish Aggarwal (DS Americas Corp.)

Chaired By
Chair Satheesh Kandasamy

Authored By
Ashish Aggarwal (DS Americas Corp.)
Anna Liang (Dassault Systemes)

Accelerated product development cycles present both opportunities and challenges in today's dynamic business landscape. This technical presentation explores the intricate challenges associated with compressing product development timelines and emphasizes the pivotal role of concept engineering and surrogate modeling in addressing these issues. In the fast-paced world of product development, businesses are under constant pressure to bring innovative solutions to market swiftly. However, this urgency often leads to challenges such as increased risk of design flaws, compromised quality, and higher costs. The concept engineering phase emerges as a critical component in mitigating these challenges. By focusing on ideation, feasibility analysis, and rapid prototyping, concept engineering lays the foundation for a robust and well-informed product development process. One of the primary challenges in accelerated product development is the need for efficient decision-making. The concept engineering phase acts as a decision-support mechanism by enabling teams to explore multiple design alternatives rapidly. This iterative process allows for early identification of potential pitfalls, ensuring that only the most viable concepts proceed to the subsequent stages. Another critical challenge in accelerated product development is the increasing complexity of designs. As products become more intricate, traditional methods struggle to keep pace with the demands for precision and efficiency. Concept engineering, coupled with surrogate modeling, provides a systematic approach to manage this complexity. Through the exploration of simplified prototypes and efficient modeling techniques, teams can navigate intricate design spaces with greater ease. Physics-based simulations play a crucial role in science-based design processes by providing high-accuracy simulation results. However, the high execution times often limit the design exploration capabilities. To retain comprehensive 3D simulation results while reducing executing time, we present an approach utilizing neural networks for 3D interactive design exploration. Fast executing neural networks are trained as 3D surrogates using parametric design data generated from physics simulations, and then deployed in a collaborative design environment for design exploration, allowing designers to evaluate a high number of parameters within the design space. By delivering high-fidelity results that covers both transient physical responses and 3D fields, this proposed method can accelerate the design iteration process, and enhance the efficiency and effectiveness of science-based product design approaches. In conclusion, this technical presentation delves into the challenges associated with accelerated product development cycles and underscores the significance of concept engineering and surrogate modeling in addressing these challenges. By embracing a systematic and collaborative approach in the concept-engineering phase, coupled with the efficiency of AI-empowered surrogate modeling, businesses can navigate the complexities of accelerated timelines while ensuring the delivery of high quality, innovative products to the market.

A Seamless Bridge: Integrating Cameo Systems Modeler into PLM

Authored & Presented By
Brian Schouten (Prostep Inc.)

Chaired By
Chair Max Monn

Complex systems design demand innovate solutions. Harmonizing Cameo Systems Modeler with your PLM is no easy task, but when effectively done can unlock a wealth of benefits. Fusing these two systems lays the foundation of data interoperbility, fostering a more collaborative and agile environment. Additionally, benefits of enhanced traceability, reduced redundancy, and improved descision making result. This session will explore three core strategies for integrating Cameo with PLM: cloud PLM integration, Cameo client plugin, and Cameo datahub OSLC integration. We'll examine the unique advantages of each approach and discuss which use cases are best suited for each method. Key Takeaways: 1. An understanding of the 3 strategies for integrating Cameo into a PLM platform 2. An introduction to the flexibility and compresensive tool, OpenPDM which facilitates each strategy for integration 3. Insights into best practice and methodologies for integrating Cameo into your PLM

Aligning Data Flow Between MBSE and PLM Systems at a Leading American Aerospace Company

Authored & Presented By
Brian Schouten (Prostep Inc.)

Chaired By
Chair Max Monn

Although integrating MBSE tools with PLM and other systems often pose a significant challenge, the benefits of doing so make it worthwhile. Experts at PROSTEP embark on a solution for a leading American aerospace manufacturer enabling automated, bi-directional data flow between MBSE and PLM platforms with PROSTEP's integration platform, OpenPDM. This presentation highlights the integration of 3DEXPERIENCE and SIEMENS Teamcenter with synchronization of BOM, simulation data and parameters, and documents. Additionally, the solution will provide traceability of up to 10 object types across tools use OSLC links and showcase event triggers to push and pull data from Teamcenter to 3DEXPERIENCE. Key Takeaways: 1. Discover the tool for automated synchronization between 3DEXPERIENCE and Siemens Teamcenter. 2. See how OSLC links can provide traceability between object types 3. Explore the strategies, methodologies, and best practices employed to successfully execute MBSE systems integration"

A Methodology for Configurable System Simulation Models: Integration of SysML, SSP, and FMI

Presented By
Juho Könnö (University of Oulu)

Chaired By
Chair Max Monn

Authored By
Juho Könnö (University of Oulu)
Isa Banagar
Aapo Hölsä (University of Oulu)
Tero Frondelius (Wärtsilä Finland Oy)

This paper introduces a comprehensive methodology for the rapid generation of configurable system simulation models by seamlessly integrating the Systems Modeling Language (SysML), the System Structure and Parameterization (SSP) standard, and the Functional Mock-up Interface (FMI). Our approach leverages SysML as the foundational framework for system architecture, employing carefully defined diagrams that facilitate the automated creation of simulation models. We target the powertrain field as this involves the integration of several disciplines, such as electrical, mechanical, and thermal domain. Hence, the need for an easy and consistent way of defining system simulation models across domains is important for virtual validation of the whole powertrain. The proposed methodology initiates the simulation model creation process using specific SysML diagrams chosen for this purpose. These diagrams capture the structural and behavioral aspects of the system, providing a clear and visual representation of the underlying architecture. To streamline the generation process, we employ the SSP standard, translating SysML constructs into simulation-ready models. The SSP standard serves as a bridge between the SysML system architecture and the simulation environment, offering a standardized framework system simulation templates. The SysML model defines the boundaries between sub-systems of the system. Based on those boundaries, required simulations and their interfaces are defined in the SysML model. After definition of required interfaces and the scope of each simulation, they are prepared using functional mock-up units (FMUs). This unique integration ensures a seamless transition from SysML-based system architecture to a simulation-ready format. Furthermore, a direct link to the underlying system architecture allows linking of product requirements and the simulation validating them. Another key advantage of our methodology lies in its configurability. By populating the FMUs with suitable models, we can tailor the system simulation to different product configurations or desired levels of simulation accuracy. This adaptability enhances the versatility and applicability of the generated simulation models, facilitating rapid prototyping, design exploration, and performance analysis. Through a practical application of the technology in a vehicle setting, we demonstrate the efficacy of our methodology in expediting the creation of configurable system simulation models. The seamless integration of SysML, SSP, and FMI empowers engineers to efficiently navigate from system architecture to simulation-ready models.

The Development of a Virtual Prototyping Review Process Using a Collaborative Immersive Approach to Enhance Processes, Awareness, and Delivery

Authored & Presented By
Alanzo Granville (SAIC)

Chaired By
Chair Todd Depauw

Immersive technology has stimulated and incentivized innovation throughout multiple industries as the technology continues to improve and open new doors to discovery. Unfortunately, the attraction of immersive development including augmented, virtual, and mixed realities gain attention, but if they are not effectively used or understood they may generate a lackluster experience within projects and leave the users or leadership thinking is this really useful. Possibly even deterring the future use or consideration of its use on projects. This leads to the big question of how do we properly use an immersive technology approach to promote progression and generate positive benefits and outcomes for our daily work. The truth is that the use of immersive technology has shown benefits, but its use and results can vary significantly among different applications. This document will serve as a guide to developing a virtual prototype review process using immersive technology and digital engineering. Providing a way to facilitate an initial connection between work and immersive technology use and enhancement capabilities. This will demonstrate a proven process utilized in previous and on current projects that has assisted in outlining how to establish a meaningful use case for immersive technology in the construction of facilities, vehicles, and other hardware to support beneficial outcomes that can boost productivity and performance through purpose and goal identification, collaboration, awareness of users capabilities and resources, and developing a virtual prototype review process that supports quick iterations and turnaround time. Both specific and generalized examples and guidance will be used to support the integration of an immersive virtual prototype review process into various applications and projects to help understand the capabilities of using immersive technology as an enhancement tool. The process will help to identify the requirements needed to solidify innovation and future use cases supporting exploration within a virtual medium for concepts but not limited to digital twin, environment design and creation, manufacturing, and more to support critical decision-making efforts utilizing a custom fast feedback loop for many projects.

Managing Key Parameters from Simulation Inputs and Results

Presented By
Leonel Garategaray (INENSIA LLC)

Chaired By
Chair Todd Depauw

Authored By
Leonel Garategaray (INENSIA LLC)
Leonel Garategaray (Inensia)

In the realm of modern engineering practices, the effective management of parameters within the Simulation Process and Data Management (SPDM) framework stands as a critical challenge. Today, organizations are faced with different and often disconnected parameter management approaches, hindering the flow of information in the simulation process. In this context, this paper delineates a strategic initiative to redefine parameter management within the SPDM paradigm. Presenting a comprehensive methodology, we aim to close existing gaps and improve parameter management in simulation activities. We believe this is essential to achieve a more agile, efficient, and integrated approach to recurring engineering and optimization techniques.. An integral aspect is how SPDM facilitates parameter management within the context of Model-Based Systems Engineering (MBSE), and its architectural model, where variables are commonly parameterized. Integrating these parameterized variables into SPDM introduces flexibility and adaptability, allowing for adjustments to analyze their holistic impact on the system, such as modifying attributes like size, weight, or performance. This capability enables the evaluation of diverse system scenarios or configurations without the need to overhaul the entire model. By adjusting these parameters, the specific effects of changes on system performance or functionalities come to light, empowering engineers with informed decision-making throughout the project's lifecycle. When we talk about parameters, we refer to input parameters, necessary to perform any simulation, and output parameters, obtained from simulation results which could be compared with product requirements. These parameters could be extracted from file content, consolidating them in a singular repository for traceability and offering a holistic view to assess design efficacy. In a typical Development Lifecycle, design process begins with an envelope and requirements, and then models that fulfill the needs. Then the simulation tth CAD designs, accompanied by design parameters, boundary conditions, and materials properties, all interpreted as Input parameters. Afterwards, the simulation process, which consists of interlinked simulation activities with various simulation branches, yield results or reports containing key information like performance values, all these are considered as Output parameters. The challenge lies in this flow, since the information is usually disconnected, due to its nature of being documentation-based, which prevents a more pragmatic use to interpret the quality of the design. The objective of this paper will be to: Demonstrate how an SPDM platform streamlines parameter extraction and programmatic management from various files. Emphasize the dual centralization of both simulation data and parameters, ensuring traceability and change control. Illustrate how this approach accelerates analysts' understanding of designs and results, fostering a more efficient workflow. Exemplify, through a practical use case, the creation of parameter repositories from an organized files repository. Highlight how this initiative ensures a Single Source of Truth for simulation data, promoting clarity and reliability. We conclude that harmonizing simulation inputs and outputs through streamlined parameter management in the SPDM platform, enhance the efficiency and reliability of decision-making.

Start Fast, Start Smart: How to Build a Solid Foundation for your SPDM Journey

Presented By
Leonel Garategaray (INENSIA LLC)

Chaired By
Chair Todd Depauw

Authored By
Leonel Garategaray (INENSIA LLC)
Thomas Lehnhaeuser (ANSYS)

In today's dynamic and competitive landscape, the need for efficient product development is more crucial than ever. Having an efficient simulation process that support lean product development, is paramount in reducing time to market of new products. That's where Simulation Process and Data Management (SPDM) solutions come in, playing a vital role in achieving reusability, agility, accuracy, and efficiency in the development cycle. As we shared in previous NAFEMS presentations, the implementation of an SPDM solution is a multi-year journey, where one of the most challenging phase is commonly the first one: What is the added value of SPDM within the organization, and how to reach the objective? To simplify and expedite the roadmap, Ansys and Inensia partnered to create a combination of tools, methodology and training, that accelerates the process. The approach we will present facilitates the initial steps, empowers key user groups, and lays the groundwork for an effective SPDM implementation plan, while also delivering a working SDM solution within two months. This comprehensive approach combines training, methodology, and workshops to expedite the deployment of a Pilot System of Record (SoR) and create a personalized SPDM Phased Roadmap within a condensed timeframe of just 8 weeks. A key advantage of this strategy lies in the rapid availability of the system, enabling users in addition to grasping theoretical concepts, tangibly experiencing its benefits.. Through this hands-on engagement, participants were empowered to contribute actively in crafting a tailored implementation roadmap, aligning with their unique organizational needs and goals. In our presentation, we'll dive into the method we developed and showcase examples from clients where we've successfully implemented it. Based on our experience, we'll cover: - Building Effective Teams: How to create productive and efficient work teams and maximizing their productivity within the stipulated timeline. - Simplified Input Gathering: Templates designed to simplify and expedite the gathering of inputs from users, ensuring a smoother transition into the SPDM ecosystem. - Project Monitoring for Speedy Execution: The importance of keeping a close eye on the project for a swift and successful implementation of the SPDM initiative. - Effective Reporting: Developing user-friendly dashboards to guide and motivate users in workshops and help them express the company's needs that could benefit from SPDM implementation. In addition to explaining the theory; we'll also share real-world experiences from our clients, enriching the audience with actionable insights gained through practical application In conclusion, the approach of streamlining the establishment of a Pilot System of Record (SoR) and developing customized SPDM plans within an 8-week timeframe reflects a commitment to industry best practices. This methodology is designed to facilitate a quick and practical learning experiences for users, empowering them to actively define a roadmap that aligns with their unique organizational objectives. Through sharing these insights, we aim to contribute valuable strategies that organizations can consider in their pursuit of an effective SPDM journey.

Multiphysics time-dependent electro-mechanical simulations for dielectric elastomers using Abaqus User Elements (UEL) & FEniCSx

Presented By
Kamalendu Ghosh (KLA)

Chaired By
Chair Tim Morris

Authored By
Kamalendu Ghosh (KLA)
Bhavesh Shrimali (Kimberly Clark)

There has been increasing interest in the constitutive modeling of dielectric elastomers due to their potential in enabling new technologies such as soft robotics, actuators, and haptic devices. Under realistic time dependent loadings, dielectric elastomers are inherently dissipative. They dissipate energy both through viscous deformation and through friction in their electric polarization process. However, majority of constitutive models and the Finite Element implementation for such models considers only mechanical dissipation. The motivation for this bias is that the mechanical relaxation time of dielectric elastomers is much larger that their electric relaxation time. Yet, Accounting for electric dissipation, in addition to mechanical dissipation is crucial when dealing with applied alternating electric fields. A fully coupled 3-D constitutive model for isotropic and incompressible dielectric elastomers was proposed by Ghosh and Lopez-Pamies [1]. In the first part of this talk, the governing initial-boundary-value-problem and the constitutive model will be introduced. Following this, a detailed step-by-step procedure of a FEniCSx and Abaqus UEL implementation of the mixed-FE formulation of the governing equations will be spelled out. The scheme makes use of a conforming CrouzeixœRaviart FE discretization of space and a high-order accurate explicit RungeœKutta discretization of time. The combination of these two types of discretizations results into a robust scheme that is capable of handling finite deformations, the incompressibility constraint of the rubber and general time-dependent loading conditions. In the last part, the Abaqus UEL would be deployed to fully describe the electromechanical behavior of VHB 4910 (from 3M) under a complex time-dependent electromechanical load. References 1. Ghosh, K., Lopez-Pamies, O. 2021. On the two-potential constitutive modeling of dielectric elastomers. Meccanica 56, 1505œ1521.

Enhanced Implicit-Iterative Coupling Algorithms for Strong-Coupled Multiphysics

Authored & Presented By
Sethu Subbarayalu (Dassault Systèmes SIMULIA Corp)

Chaired By
Chair Tim Morris

Note: This work was presented in European NAFEMS Multiphysics conference held in Munich in November 2023. I would like the work to reach the audience in Americas. Abstract: In a co-simulation [FEL01], the engineering problem is decomposed into two or more subdomains, with each subdomain solved individually while continually exchanging data on their boundaries. A common use case is fluid-structure interaction, in which computational structural and fluid mechanics solvers are used over respective regions. This partitioned treatment has many merits but also poses stability and accuracy challenges when the physics coupling is strong; for example, when the fluid and structure have similar densities [CAU04], such as in the case of blood flow in an artery or a compliant structure subjected to forces imposed by a heavy and incompressible fluid. In recent years, numerical techniques, called accelerator methods, have been developed [DEG10, UEK16]. These accelerators enhance the coupling by enlarging the convergence radius from a stability point of view; in other words, the accelerators allow us to solve strongly-coupled physics problems that we could not solve before. Furthermore, the accelerators can significantly reduce the computational cost of these simulations. The SIMULIA Co-Simulation Services is an open architecture for coupling two or more solvers for multiphysics and multiscale simulations. Accelerator methods, implemented as Co-Simulation Services, are available for partners and customers. We present convergence studies of several fluid-structure benchmarks [TUR06, FER05], and industrial use cases and guide on using these numerical methods. References: [CAU04] CAUSIN P., GERBEAU J.-F., NOBILE F., Added-mass effect in the design of partitioned algorithms for fluid-structure problems. Computer Methods in Applied Mechanics and Engineering, 2005; 194(42-44):4506-4527. [DEG10] DEGROOTE J., HAELTERMAN R., ANNEREL S., BRUGGEMAN P., VIERENDEELS J., Performance of partitioned procedures in fluid-structure interaction. Computers & Structures, 2010; 88(7-8):446-457. [FEL01] FELIPPA C. A., PARK K.C., FARHAT C., Partitioned analysis of coupled mechanical systems. Computer Methods in Applied Mechanics and Engineering, 2001; 190(24-25):3247-3270. [FER05] FERNANDEZ M. A., MOUBACHIR M., A Newton method using exact Jacobians for solving fluid-structure coupling. Computers & Structures, 2005; 83(2):127-142. [TUR06] TUREK S., HRON J., Proposal for numerical benchmarking of fluid-structure interaction between an elastic object and laminar incompressible flow. In BUNGARTZ H.-J., MEHL M., and SCHAFER M., editors, Fluid Structure Interaction II: Modelling, Simulation, Optimization. Springer, 2006. [UEK16] UEKERMANN B. W., Partitioned Fluid-Structure Interaction on Massively Parallel Systems. PhD thesis, Institut für Informatik, Technische Universität München, 2016.

Integrated Thermal Fluid Simulation Workflows for Electronic Cooling Applications

Authored & Presented By
Devadatta Mukutmoni (Dassault Systèmes SIMULIA Corp)

Chaired By
Chair Tim Morris

A Guide to Digitally Transform Product Innovation with High-Performance Computing

Authored & Presented By
Rod Mach (TotalCAE)

Chaired By
Chair Alana Duma

Predictive Modeling to Enable Innovation in Textile Bonding Adhesives

Authored & Presented By
Liangkai Ma (The Dow Chemical Company)

Chaired By
Chair Steve Arnold

Polymeric adhesives are critical to the textile industry and essential to our daily life. They are gaining in popularity in textile bonding for many applications such as flexible packaging, safe and sustainable mobility, protective garments, sports and recreation equipment. They are advantageous because of optimal performance, ability to achieve higher level of automation, higher throughput, and lower total cost of ownership, when compared to other textile joining alternatives such as traditional sewing. The requirements of adhesives for adhesive textile bonding vary depending on targeted applications. Predictive modeling can be used to gain insight and accelerate new adhesive material development to deliver desired performance for specific applications. In this presentation, a systematic approach is presented to develop and validate material models to ensure prediction accuracy of predictive modeling of real-world applications of textile bonding adhesives. The characterization testing and constitutive modeling of both textile substrate and polymeric adhesive materials are first discussed. Three finite element analysis (FEA) models are then shared in the order of increased complexity: The first model is a single element uniaxial tensile test model developed to validate the adhesive hyperelastic material model and a user subroutine developed for material failure modeling. The second model is a quasi-static model developed for simulating the 180° peeling of bonded fabric samples to validate the accuracy of both the fabric and adhesive material models. The third model is a dynamic model for simulating the pressurization of an inflatable structure constructed by adhesively bonded fabric pieces in real-world applications. The insights gained from the analyses are discussed to demonstrate how predictive modeling can be leveraged to innovate differentiated joining solutions faster based on specific applications needs.

Establishing and Maintaining the Digital Thread of Additively Manufactured Materials and Applications

Authored & Presented By
Brandon Hearley (NASA Glenn Research Center)

Chaired By
Chair Steve Arnold

Robustness and Reliability Studies for Weld Simulation

Authored & Presented By
Gaurish Sharma (ESTECO North America, Inc.)

Chaired By
Chair Steve Arnold

Nonlinear Analysis

Authored & Presented By
Tony Abbey (FETraining)

Convergence of CAE, Data Science and HPC

Authored & Presented By
Fatma Kocer (Altair Engineering, Inc.)

Chaired By
Chair Gregory Westwater

Can I send the abstract after the EDS WG event? I want to see how relevant that talk was before submitting one for Americas event.

Demystifying AI for CAE

Authored & Presented By
Ian Pegler (NVIDIA)

Chaired By
Chair Gregory Westwater

Given the rise in interest in AI there has been a corresponding increase in interest in the applicability of AI approaches for Computer Aided Engineering (CAE). AI offers the prospect of being able to give faster insights into product performance earlier in the design resulting in better products and reduced time to market. Despite the emergence of AI-enhanced techniques, engineering organizations struggle to integrate these capabilities into production settings due to a lack of openness and adaptability in existing tools. The shift towards Engineering Intelligence emphasizes AI as a central paradigm in the design process, with advanced Deep Learning algorithms enabling the transformation from simulation-enabled to AI-enabled design. These algorithms create predictive models that provide post-processed simulation results and optimization suggestions directly from CAD designs. The goal is to simplify processes, emulate simulation engineer expertise, and transfer complex interactions to computers. One of the challenges of applying machine learning techniques is understanding which approach to use and the limitations of that approach. Approaches can vary from completely data driven to fully physics informed. Other challenges include effectively implementing this technology in production, requiring an open and flexible platform that facilitates the synergistic interaction of various engineering intelligence enablers, including new technologies, skilled data science engineers, Large Language Models, cloud HPC services, and other facilitators. In this presentation we will discuss the basics of physics-informed deep learning and data-driven deep learning applied to problems having roots in physics and understand the techniques that help apply deep learning to modeling multi-physics simulations systems. We will discuss these principles in the context of NVIDIA's open-source framework Modulus for building, training, and fine-tuning Physics-ML models using a Python interface. We explore the building blocks provided by the framework, including different architectures like neural operators, GNNs, PINNS, diffusion models, etc. One of NVIDIA's partners Neural Concepts will also discuss real-world use cases and challenges where AI approaches have been applied to industrial scale problems. In summary, this presentation aims to demystify AI approaches in CAE, offering attendees a comprehensive understanding of both physics-informed and data-driven deep learning techniques. Through discussions on Modulus and real-world applications, we hope to empower attendees with the knowledge and tools necessary to leverage AI effectively in the realm of engineering simulation and design optimization.

AI in Design Exploration

Authored & Presented By
Mike Sheh (INTES GmbH)

Chaired By
Chair Gregory Westwater

Abstract Can AI/ML find a design outside of the dataset that is used to train the model? Is an AI-generated digital twin better than a conventional surrogate model such as a response surface, kriging, or RBF model? Will we begin to see AI being implemented in existing commercial CAD/CAM/CAE/PLM software that dramatically increases productivity... and collaboration? Do I need to learn Python to "stay in the game"? As AI/ML gains warp speed recognition that could significantly change how we do things, including engineering design, companies are eager to find their strategy to embrace the new generation technologies. In this presentation, we will discuss our past R&D in the domain of data analytics, surrogate modeling, and optimization. In particular, our attention will be placed in the following two areas: AI-powered "What-if" tools for designers, and an AI-powered exploration algorithm that largely replaces the past generation algorithms. Current real time What-if tools have promised to give designers fast answers to their design changes. The math behind them is typically some type of surrogate model. Historically, this could be a response surface model, Kriging, Radial Basis Function, and many others. These approaches typically suffer when the surrogate model does not correctly predict, within an acceptable level of error, the responses due to design changes. The success rate over the past decades has been marginal, especially when those surrogate models are developed based on a set of simulation data. However, the latest AI/ML approach has greatly improved the ability of these tools to predict responses accurately and consistently. This will be discussed in our presentation. The other aspect is the use of AI in design exploration/optimization, particularly when using simulation in search of better designs. The past generation of exploration tools are mostly math-based. The result is that without the user knowing enough of the underlying mathematical characteristics of the problem, it is hard to pick the suitable algorithm(s) that can derive the best answers in the least amount of time. This is what we used to face in the past: "What algorithm should I pick?" The latest development in using AI in learning-based searches has dramatically changed that. The next generation OASIS.AI Optimizer has shown its promise to bring robustness, versatility, simplicity, and speed to design exploration. We will discuss this advancement and illustrate the new technology's effectiveness using benchmark data.

Deploying an AI Simulation Stack Across Engineering Teams - A Case Study

Presented By
Matt Bemis (SimScale)

Chaired By
Chair Fatma Kocer

Authored By
Matt Bemis (SimScale)
David Heiny (SimScale GmbH)

The advent of artificial intelligence (AI) in engineering simulation begins a transformative era in product development processes. This abstract outlines the substantial impact of AI adoption in engineering simulation, focusing on how AI models, trained past engineering data , can significantly accelerate or even automate future engineering workflows. AI expedites design and innovation by dramatically reducing simulation lead and run times, thereby enabling engineering teams to explore design spaces broader, deeper and faster in pursuit of higher product performance. The influence of AI extends beyond this, however; it has the potential to accelerate every step of the Computer-Aided Engineering (CAE) workflow: -AI's integration begins at the CAD generation phase with generative design, followed by CAD preparation involving simplification and cleanup. -It further streamlines mesh generation by optimizing grid setup based on geometry and physics -Accelerates simulation setup through pre-generated templates, automatic sanity checks, and guidance dependent on the problem -Revolutionizes the numerical solve process with AI-accelerated solutions -Finally, can aid in result interpretation and design optimization, enabling more efficient and effective decision-making. The success of AI models hinges on the availability of consistent, accessible, and large data sets. An integrated solution that combines AI with traditional CAE methods and data management lowers the barriers to AI adoption and enables engineering teams to gradually train, use and improve AI models in their workflows. This approach involves data curation, AI model generation and evaluation, and continual enhancement with newly generated data, all consolidated in a single platform. Such integration facilitates seamless access and collaboration among designers and engineers across all project stages, significantly bolstered by AI capabilities. SimScale is developing a pioneering platform in this domain, offering a solution that amalgamates AI with traditional CAE methods. This platform provides accessible tools for both designers and simulation experts, capturing the entire spectrum of AI-enhanced simulation processes and ensures AI future readiness. This paper presents a case study of deploying AI through a cloud-native engineering simulation platform with global engineering teams in the automotive and manufacturing industries.

Removing the Data science barriers to Deep Learning surrogates

Presented By
Hoss Habib (Key Ward GmbH)

Chaired By
Chair Fatma Kocer

Authored By
Hoss Habib (Key Ward GmbH)
Andrew Fine (Fine Physics)

The convergence of Artificial Intelligence (AI) and engineering disciplines heralds a transformative potential for optimizing design processes, enhancing analytical methodologies, and advancing simulation technologies. Despite these prospects, the integration of AI into engineering practices is impeded by significant challenges, primarily stemming from the handling of vast, unstructured engineering datasets and the inherent interdisciplinary gap between engineering and data science expertise. This paper introduces an automated, engineering-focused framework, Keyward, designed to bridge this divide by facilitating the efficient preparation and utilization of engineering data for AI applications. Leveraging advanced algorithms for data sorting and preprocessing, Keyward enables the seamless conversion of heterogeneous engineering data into AI-ready formats, addressing compatibility issues across various software tools and file formats prevalent in engineering workflows. Furthermore, the framework incorporates intuitive, no-code tools tailored for engineers, alongside comprehensive guidelines and validation checks for data scientists, thereby ensuring the generation of coherent, high-quality datasets for AI modeling. The efficacy of the Keyward framework is demonstrated through a case study within the OpenFOAM community, highlighting its capabilities in significantly reducing the time and expertise required to prepare engineering simulation data for deep learning applications. The results underscore Keyward's potential to not only streamline the AI integration process but also to foster a synergistic collaboration between engineering and data science disciplines, thus accelerating the adoption of AI technologies in engineering practices.

Megacasting 2.0: Transforming the Industry with AI Integration

Authored & Presented By
Milan Raval (Altair Engineering, Inc.)

Chaired By
Chair Liviu Georgescu

Metal casting has its roots in ancient civilization. Starting from the bronze age (around 3300 BCE to 1200 BCE) till the industrialization in the 18th and late 19th centuries, the metal casting process evolved significantly. In the 20th and 21st centuries, metal casting adopted advanced materials, technology, and various casting processes. Computer-aided design (CAD) and simulation (CAE) tools for predictive approaches helped industries to produce more complex castings efficiently and with higher accuracies. Today, metal casting plays a vital role in various industries, including automotive, aerospace, and heavy equipment manufacturing. In the automotive industry, the emerging need for sustainable mobility solutions has inspired designers to create exciting new vehicle concepts and rethink the ways they can minimize weight, lower manufacturing costs, and meet modern performance targets. In particular, megacasting - and its evolution into gigacasting - refers to a new approach for the serial manufacture of large, complex geometry, light alloy metal parts using high-pressure die casting (HPDC). Megacasting is of particular interest for large structures within the body in white (BIW) of electric vehicles (EVs). HPDC is a well-established industrial process for making aluminum- and magnesium-alloy-based automotive parts such as drivetrain casings, smaller precision parts, or other weight-sensitive structures. By increasing cast component size, teams can reduce the amount of required individual parts – which are often stamped and welded and made of different materials – thus saving time and resources. This does not come without risk, because the transition from using wrought products to casting at this scale needs considerable investment in new plants and machinery, along with expertise in optimizing the design and assuring high-quality serial manufacturing of these relatively recent megacastings. Furthermore, such technology advancements came with their own design challenges: manufacturing megacasting parts is not easy and can require a different approach to CAD and CAE product development to overcome additional hurdles like longer computational time, extensive model validation, complex mesh generation, etc. As the whole world is diving deeper into utilizing Artificial Intelligence (AI) algorithms in the most diverse applications, AI can be leveraged to streamline megacasting design manufacturing as well. In one example of an application for battery electric vehicles (BEV) design, we take a deeper dive into a bespoke megacasting workflow to illustrate the symbiosis of AI and simulation in the design of chassis components. Specifically, AI has been used to easily identify the best part designs given specific design and manufacturing criteria, out of an enormous amount of simulation data by applying machine learning clustering, known as AI-driven expert emulation. Furthermore, AI-powered engineering tools can predict faster and make design decisions quicker than traditional casting simulations. This tool can not only save computational time taken by traditional casting simulations but also provide better result predictions on new designs without running the simulation itself. These AI-powered generative design workflows combine AI, generative design, and the latest CAE technologies, providing a proven approach that enables multidisciplinary variant analyses for efficient, production-oriented, and performance-driven megacasting structures.

AI-based optimization of sheet metal stamping for automotive applications

Presented By
Seokpum Kim (Oak Ridge National Laboratory)

Chaired By
Chair Liviu Georgescu

Authored By
Seokpum Kim (Oak Ridge National Laboratory)
Rich Davies
William Halsey
Seokpum Kim
Vincent Paquit (ORNL)
Sathya Dev
Kaiping Li (Stellantis)
Liang Huang
Andrey Ilinich (Ford)
Lu Huang
Krishna Murali
Weidong Wu (GM)
Kidambi Kannan (AutoFORM)

Sheet metal stamping processes are widely used in various industries due to their cost-effectiveness, rapid cycle times, mass production capabilities, and relatively high precision. The quality and efficiency of these processes hinge on several critical factors, including material properties, blank dimension and uniformity, die geometry, and processing parameters such as binder force, lubricants, and spacer utilization. To enhance production efficiency, process simulation tools are commonly employed. A typical optimization procedure for manufacturing requires an iterative process involving parameter setting, execution of computational simulations, and modifying the parameters. The entire process demands substantial computational time, making it impractical for real-time feedback towards rapid corrective actions required for in-line control for running production processes. To overcome this challenge, artificial intelligence (AI) can be leveraged to determine optimal manufacturing parameters within a single manufacturing cycle time. This research proposes an in-line optimization framework incorporating a trained AI model to predict kidney-shaped die forming. Preliminary results indicate that the AI framework can accurately predict draw-in values based on a given parameter set, a process referred to as forward prediction. Furthermore, the AI framework can also predict the optimal parameter set that leads to the desired draw-in values, referred to as backward prediction. This research is performed in collaborations with AutoFORM and USCAR (US Council for Automotive Research). The members of USCAR are Ford, GM, and Stellantis.

Artificial Intelligence to Empower Engineering Organizations: Technology & Applications

Authored & Presented By
Frederick Reis (Neural Concept)

Case Study of Seamless Modeling and Simulation for Electric Drive Engineering

Presented By
Satheesh Kandasamy (Dassault Systèmes SIMULIA Corp)

Authored By
Satheesh Kandasamy (Dassault Systè
mes SIMULIA Corp)
Young-Chang Cho

System Modeling Bridging the Gap with Smart Systems Modeling (SSM)

Authored & Presented By
Alejandro Ayala (Estrateg IA)

Chaired By
Chair Dipak Patil

The main purpose of modeling is to translate physics into a simplified representation, including just the significant variables that determine most of the emergence output. Systems Modeling has been a useful tool to develop complex systems and new technologies. On the other hand, systems modeling has constraints to generate representative models with multiple dynamic and nonlinear variables, therefore there is an incremental gap between the models' capabilities and the growing rate of systems' complexity. This gap is generated by the lack of smart modeling tools to identify the critical factors that generate most of the system's results, under increasing disturbing environments. This work presents the development of Smart Systems Modeling (SSM), powered by Artificial Intelligence (AI), as an evolution of the Smart Model Based Systems Engineering (SMBSE)* approach, to bridge the gap between modeling and the increasingly complex systems. SMBSE presented in 2018, the development of Systems Engineering models, enhanced by automation, optimization, reusability and dynamic simulation functionalities. The AI available tools allow the transformation of SMBSE into more powerful models, enhanced with simultaneous modeling and virtual simulation capabilities, as well as regression techniques to predict results and the required flexibility to adequate the core model for new functions and interfaces. Finally, AI enhances SSM with smart functionalities to determine patterns of relationship and the significant variables across complex big data and non-structured inputs. AI-Machine Learning (ML) will support the development of SSM with learning abilities to improve the models' fidelity until an acceptable correlation level is achieved. This is just the beginning of a promising growing solution to develop powerful design and research models, in conjunction with AI's decision-making processes and learning capabilities. The SSM progress will provide benefits for faster replacement of physical tests by robust virtual model simulation, for new products development, as well as agile research experiments. *"Smart Model Based Systems Engineering", Alejandro Ayala, August 7th 2018, Michigan USA.

Leveraging MODSIM Techniques for enhanced engineering analysis: integration of SOLIDWORKS models with the 3DEXPERIENCE Platform

Authored & Presented By
Omar Zohni (Dassault Systèmes SIMULIA Corp)

Chaired By
Chair Dipak Patil

This paper presents an innovative approach to engineering analysis by harnessing the power of Model Simulation (MODSIM) methodologies in conjunction with industry-leading software tools such as SOLIDWORKS, SIMULIA and the 3DEXPERIENCE Platform. In the contemporary engineering landscape, the demand for efficient and comprehensive simulation techniques has become paramount. By amalgamating MODSIM principles with the capabilities offered by SOLIDWORKS and the 3DEXPERIENCE Platform, engineers can achieve unparalleled insights into complex designs and processes. Through this integration, engineers can seamlessly transfer SOLIDWORKS models into the 3DEXPERIENCE Platform environment, facilitating a streamlined simulation workflow. This transition not only enhances the fidelity of simulations but also enables engineers to leverage advanced simulation functionalities offered by the 3DEXPERIENCE Platform, including multiphysics simulations, optimization algorithms, and collaborative features. The utilization of MODSIM techniques within this integrated framework empowers engineers to conduct thorough analyses across various domains, ranging from structural mechanics and fluid dynamics to electromagnetics and thermal simulations. By capturing the intricate interactions within systems, engineers can make informed design decisions, optimize performance, and mitigate potential risks. Furthermore, the integration of SOLIDWORKS models with the 3DEXPERIENCE Platform fosters collaboration and knowledge sharing among interdisciplinary teams. Engineers can collaborate in real-time, exchange insights, and iterate on designs rapidly, thereby accelerating the product development lifecycle. This paper showcases practical applications of MODSIM techniques integrated with SOLIDWORKS models within the 3DEXPERIENCE Platform utilizing the simulation tools provided by SIMULIA. Case studies will illustrate how this integrated approach has revolutionized engineering analysis, leading to improved product quality, reduced development costs, and shortened time-to-market. In conclusion, the integration of MODSIM methodologies with SOLIDWORKS models and the 3DEXPERIENCE Platform represents a paradigm shift in engineering analysis, enabling engineers to unlock new realms of innovation and efficiency. This paper advocates for the adoption of this integrated approach as a cornerstone in modern engineering practices, driving advancements across industries and shaping the future of product development and design.

Democratizing Simulation Methods to Collaborate Efficiently using the 3DEXPERIENCE Platform

Presented By
Gopi Lanka (Dassault Systèmes UK)

Authored By
Gopi Lanka (Dassault Systè
mes UK)
Dominick LAUZON
Chris OLSON (Dassault Systèmes)

Both CAD and CAE applications used by designers, analysts and engineers have evolved independently over time, introducing their own 3D modelling capabilities, user interfaces, data formats and security mechanisms. These disconnected technology silos can slow the design process, introduce errors and allow for data loss. The situation gets worse with non-existent or poor data management practices while preventing the ability to capture and deploy best practices across the organization. The 3DEXPERIENCE Platform offers a unified model-based architecture, which eliminates inefficiencies in the design process while propelling virtual engineering to a completely new level. The architecture lays a foundation for a modelling and simulation (MODSIM) approach that seamlessly combines best-in-class design & modelling technologies from CATIA along with superior multi-physics simulation modelling portfolio of SIMULIA. The parametric nature of these models allows for building automated simulation processes that capture the best practices. Simulation processes can later be democratized to CAE non-experts via simulation templates. Process standardization helps in reducing rework while increasing simulation throughput and driving innovation. This presentation will highlight simulation process use cases from different industries and demonstrate the values delivered to individuals and engineering teams. It will show how Designers and CAE engineers can leverage expert simulation methods and perform simulations more efficiently. CAE Managers will observe increased cross-discipline collaboration between teams, increased simulation throughput that helps generate evidence to make better decisions. Finally, it will demonstrate the ability to capture all the generated simulation data in a digital thread, managed seamlessly within the platform, that is easy to search and trace.

Machine Learning for Physics-Accelerated Design of Mixing Tank Processes

Presented By
Mustafa Kaddoura (Ansys, Inc.)

Authored By
Mustafa Kaddoura (Ansys
Inc.)
Matthew Adams
Marc Horner
Sreedevi Krishnan
Hossam Metwally
Ryan O'Connor (Ansys)

With the escalating prominence of Machine Learning, the convergence of these technologies with physics-simulation methodologies opens unprecedented opportunities to enhance the effectiveness and accuracy of predictive models, particularly evident in the realm of mixing applications within industrial processes and bioreactors. Among the promising machine learning approaches is the data fusion, also known as residual modeling, in which few high-fidelity data points (simulation or experimental) are used to increase the accuracy of low-fidelity simulation data by learning the error trend between the two data sets. Augmenting physics-based models with data-driven models improves solution fidelity without increasing the complexity of the mathematical model form. In this study, we showcase two industrial application case studies for applying the Fusion modeling concept. The first case study aims to enable blend time prediction in mixing tanks efficiently and accurately at distinct design variables. We create a fusion model by augmenting high-fidelity intensive sliding mesh CFD simulation data with the less-accurate Multiple Reference Frame (MRF) CFD simulation data to enhance model fidelity. The design space of the mixing tank in terms of material properties (density and viscosity) as well as operating conditions (agitation speed) was explored using both the high-fidelity approach and the Fusion modeling approach. We then build a system-level fusion-reduced order model (Fusion-ROM) to achieve instantaneous, accurate blend time prediction. Finally, we build a custom user-friendly app on the top of the developed workflow to enable non-experts for running mixing tank simulations. This hybrid approach reduced the root mean square error (RMSE) in MRF model from 32% to 6%, while maintaining quick/instantaneous prediction, and reduced the CPU hours required to construct the design space by approximately 50% versus the purely sliding mesh approach. The second case study aims to build an accurate digital twin for sparged bioreactor applications by augmenting experimental data with CFD simulation data to enhance the prediction of mass transfer coefficient (Kla) and gas hold-up in bioreactors operating at various agitation speeds and flow rates (VVM). Generated fusion models were used on top of fluid flow ROMs as part of a system-level model to design the bioreactor control system and for online/offline digital twin.

Building Digital Twins of Additive Manufacturing Parts for Fatigue Life Prediction

Presented By
Xueyong (Kevin) Qu (The Aerospace Corporation)

Authored By
Xueyong (Kevin) Qu (The Aerospace Corporation)
Vinay Goyal
Jacob Rome
Leland Shimizu (The Aerospace Corporation)

Additive Manufacturing (AM) parts are widely adopted in various industries due to design freedom, manufacturing flexibility, and potential time and cost savings. Porosities produced by AM processes, such as keyholes or lack of fusions, can be crack initiators and can propagate to failure under fatigue environments. While AM has been adopted widely in aerospace applications, predicting failure of AM parts continues to be a challenge. Digital Twins of the AM part can simulate actual defects and can be updated over the service life of the part to support real-time fatigue assessment. This paper employs fracture mechanics to assess fatigue life of a printed component. The number of pores, their sizes and locations in AM parts can be characterized with computed tomography (CT) scans. Pore data, fatigue load spectrum, and material properties are variables considered in deterministic fracture mechanics Digital Twins to predict fatigue life. In parallel, a probabilistic fracture mechanics model is also executed to account for spatial variabilities in the properties. The Digital Twins are validated by simulating fatigue life of an axial fatigue test coupon and then compare to test results. The applications of Digital Twins are demonstrated by in-situ prediction of fatigue life of an AM part during the printing. The influence of pores and build heights on AM part fatigue life are demonstrated by simulating the printing process from bottom to top. First, deterministic pores up to the current built layer are simulated in the model, as if they were detected by in-situ measurements. These deterministic pores are then combined with a probabilistic pore distribution in the unprinted portion characterized by ex-situ CT scans of similar builds to provide a failure life prognosis for the full part. The numerical approach and examples will be presented in the final presentation.

Coupling Manufacturing and Component Design with Systems Engineering

Presented By
Tero Frondelius (Wärtsilä Finland Oy)

Authored By
Tero Frondelius (Wä
rtsilä
Finland Oy)
Isa Banagar
Aapo Hölsä
Juho Könnö (University of Oulu)
Jarkko Laine
Hannu Tienhaara (Wartsila)

In the dynamic landscape of modern engineering, the integration of manufacturing processes, component design, and systems engineering emerges as a critical paradigm for fostering innovation, efficiency, and sustainability. This abstract explores the multifaceted constructive collaboration between manufacturing, component design, and systems engineering, emphasizing the transformative impact on product development and industrial ecosystems. The traditional silos between manufacturing and design are dissipating as collaborative frameworks gain traction. This shift is propelled by advancements in computational tools, simulation techniques, and data-driven approaches. The coupling of manufacturing and component design within the broader context of systems engineering presents an integrated approach to product realization. The integration begins with the co-evolution of design and manufacturing considerations from the project's inception. Early collaboration enables concurrent engineering, allowing for real-time feedback loops between design iterations and manufacturing feasibility assessments. This iterative process optimizes both the product's functionality and manufacturability, streamlining the entire development lifecycle. Systems engineering plays a pivotal role in orchestrating this collaborative dance. It provides the overarching framework to align diverse elements such as requirements, functions, and constraints, ensuring a unified approach. By holistically addressing the interdependencies between design, manufacturing, and broader system requirements, systems engineering enhances product quality, reliability, and performance. The abstract delves into specific case studies illustrating successful implementations of this integrated approach across diverse industries. These cases highlight not only improvements in time-to-market but also cost-effectiveness and sustainability metrics. The integration facilitates the design of components that are inherently manufacturable, reducing waste and optimizing resource utilization. The role of advanced analytics and artificial intelligence in this integration is also examined, highlighting their capacity to analyse vast datasets generated throughout the product lifecycle. These technologies empower engineers to make data-driven decisions, fine-tuning both the design and manufacturing processes iteratively. In conclusion, the abstract underscores the imperative for a change in thinking in engineering methodologies. The coupling of manufacturing and component design with systems engineering heralds a new era of collaborative innovation. Embracing this integrated approach promises not only enhanced product quality and efficiency but also a sustainable and resilient foundation for the future of engineering.

Simulation driven design of a BESS cooling system using CFD and parametric optimization

Authored & Presented By
Benjamin Beckelynck (Optimec Consultants Inc)

Battery Energy Storage System (BESS) utilizes rechargeable batteries such as lithium ion, to store electrical energy in the form of chemical energy. BESS contributes to grid stability by providing services such as frequency regulation, peak shaving, and grid balancing. BESS is often employed to address the intermittent nature of renewable energy sources like solar and wind. By storing excess energy when generation exceeds demand and releasing stored energy during peak demand periods, BESS helps integrate renewables into the grid more effectively. Additionally, portable BESS systems can be used in remote locations or emergency situations where a stable power source is needed. Battery Energy Storage Systems (BESS) require effective heat management to ensure the safe and efficient operation of the battery cells. Heat management is crucial in maintaining the performance, lifespan, and safety of the batteries. Batteries have an optimal operating temperature range for efficient performance. High temperatures can accelerate chemical reactions within the cells, leading to faster degradation and reduced lifespan. Conversely, low temperatures can decrease the efficiency of the battery. Many BESS incorporates active cooling systems, such as liquid cooling or air cooling, to maintain the batteries within the desired temperature range. The study involves an analysis of a BESS comprised of 204 battery modules. The system is arranged in two rows of 6 racks, each containing 17 modules, resulting in a total of 204 modules. Each module generates 126 Watts of power. To cool the batteries, a system of fans is employed to push and direct airflow inside the trays, with each module having its own dedicated fan, totaling 204 fans. Additionally, an HVAC unit with 2 large blower fans and a heat exchanger is utilized to circulate air inside the enclosure and cool it down to a specific temperature of 24°C. A plenum is used to ensure uniform distribution of air to each module, with its design optimized through parametric optimization to maximize airflow uniformity and minimize the pressure drop in the system. To streamline analysis and reduce solve time, porous media are calibrated to represent internal components such as the modules and the heat exchanger, facilitating accurate representation of pressure drops. Thermal analysis is then conducted to estimate the cooling capacity of the HVAC unit and determine the operational temperature of each module at full load. In summary, the work involves conducting a Computational Fluid Dynamics (CFD) analysis on the BESS system to optimize airflow and cooling performance using the powerful tool set of parametric design study and have a simulation driven design.

Advanced Modeling of Filtration Processes: Integrating High-resolution Filter Matrix into CFD-DEM Simulation

Authored & Presented By
Ahmadreza Haghnegahdar (Ansys, Inc.)

Gas flow simulation generated for Internal Arc Fault based on Digital Twins

Authored & Presented By
Mariana S. Bretado de los Ríos (Schneider Electric)

The development of "Digital Twins" models has become popular for describing solutions and optimizing processes and products. With the development of new technologies, digital models can be generated to act as a mirror of a physical system or product, allowing the optimization of designs, processes, and complex industrial systems. The most complete digital twins share three elements: context and data characteristics, real-time operational data, and an information model used to integrate them. The ideal scenario for "Digital twins" is to transfer data in real time between the physical and digital models to generate continuous feedback and optimize the system. However, the complexity of certain physical phenomena makes the synchronization of real and digital data extremely difficult. This is the case in electrical applications to prevent failures due to short circuit or Internal Arc Faults (IAF). This work focuses on the phenomenon of internal arc failure in electrical equipment, for which obtaining experimental data is very difficult, and consequently, comparing them with digital models is challenging. IAF energy is transferred through the air and is generated between two electrified components due to a lack of maintenance or insulation losses. When an IAF occurs in electrical equipment, two important goals must be achieved: 1) the structure and enclosure walls of the equipment must withstand the overpressure, gas blast, and stress waves; 2) the heat flow must be contained and released gradually to avoid thermal damages that could threaten human safety. To prevent IAF damages, experimental tests must be performed according to the standards for electrical equipment. Due to the complexity of IAF tests, simulations become a solution to make the analysis of IAF effects more efficient, saving costs and time. The challenges that IAF digital twin face are mainly: 1) understanding physics, 2) solutions and coupling, 3) technology availability, and 4) validation and verification. This work focuses on characterize the physics behind the gas flow behavior due to the arc fault phenomenon and highlights the importance of understand the model to achieve accurate simulation results. The principal parameters study in this work are energy input, vaporized mass, grills, flaps, or wire meshes. This first step leads to move forward in the development of a IAF digital twins model and consequently, perform a wider analysis to validate and correlate simulation results with experimental tests. A simplified medium-voltage unit is considered as a case study. The results show the gas flow thermal and structural effects with different parameters on the equipment. The validation and verification are made through experimental tests, obtaining an accurate approximation with the simulation. Moreover, parametric assessment is conducted to analyze the effects of vaporized mass, type of grills, and input energy on the gas flow of the IAF. The methodology presented in this work provides accurate results that improve the efficiency of simulations for the development of new products to guarantee the human safety of electrical equipment through the IAF digital twins.

Automating Multi-disciplinary Workflow Collaboration and Simulation Data Management to Accelerate R&D

Authored & Presented By
Bernardo Mendez (Rescale Headquarters)

Achieving Large Scale Detailed Component Level Structural Simulations

Authored & Presented By
Mark Taylor (The Boeing Company)

Chaired By
Chair Bob Tickel

Scale and democratize reduced order modeling techniques within an SPDM framework

Authored & Presented By
Nachiket Kasarekar (ESTECO North America, Inc.)

Chaired By
Chair Bob Tickel

Solving Industry's Data and Process Challenges of Digital Engineering with R&D Clouds

Presented By
Edward Hsu (Rescale)

Chaired By
Chair Bob Tickel

Authored By
Edward Hsu (Rescale)
Alex Kachkovsky
Manager of Virtual Engineering at Daikin Applied Americas

Innovators across industries are embracing new digital engineering methods throughout the R&D process to expedite product development, enhance quality, and mitigate operational risks. With the proliferation of these methods, engineering computing technologies such as CAE and AI-enhanced simulation are generating vast amounts of data that drive product decisions. Concurrently, the development process for new products has become increasingly complex and collaborative, involving more teams and analyses. Companies like Kairos Power and Lear Automotive are competing in highly complex and regulated industries by leveraging purpose-built R&D clouds to optimize their engineering processes with the latest technologies. Today, innovation-focused companies prioritize user workflows and seek purpose-built solutions that meet their specific needs. In the past, engineering computing stacks were built from the ground up, requiring specialized IT expertise and long lead times. As companies look to integrate new use cases such as AI-driven design and hardware-accelerated computing, R&D clouds offer pre-built ecosystem configurations and low-code tools to customize and integrate the latest technologies. This enables R&D teams to advance their product engineering and digital transformation initiatives without requiring expertise in building and managing sophisticated compute and storage infrastructure. A key benefit of purpose-built R&D clouds is their ability to capture and extract critical insights throughout the product design, optimization, and validation process. Engineering teams that can effectively manage large datasets with rich domain and institutional context across all their digital activities (simulation, modeling, AI/ML, etc.) can improve the speed and quality of their product decisions. As data volumes and variety explode with greater usage of digital tools, teams that can easily capture their industry and company's historical information will be able to share, search, and understand context to extract new insights that lead to new breakthroughs in R&D. Additionally, R&D clouds enable organizations to harness cutting-edge technologies to remain competitive. While new domain-specific software and hardware architecture options have multiplied, increased choice and specialization of tools and resulting datasets bring new challenges. R&D Cloud or industry cloud platforms aim to eliminate complexity by simplifying and tailoring the tool stack to empower digital engineers, computational scientists, and researchers. As R&D teams connect multiple tools and build multi-stage processes, effective management of metadata ensures processes can be mapped for reproducibility and optimized for efficiency. Companies leverage Rescale as a composable and customizable R&D cloud to quickly adopt modern engineering use cases including model-based collaboration, knowledge management, product analytics and visualization, and process automation and optimization. Choosing an R&D cloud that is flexible to fit seamlessly with existing workflows ensures engineering and scientific leaders can successfully adopt these use cases in a user-centric experience. These capabilities deliver the agility and control needed to accelerate product decisions and reduce risk and cost across digital engineering activities. Kairos Power, a nuclear company disrupting the energy sector, utilizes Recale to accelerate product delivery timelines with tight collaboration and context-based data management across all simulation activities. Lear Corporation, a leading automotive supplier, orchestrates large-scale engineering computing on Rescale, ensuring their multi-disciplinary teams have shared access to critical context needed to make timely product decisions with confidence. Companies like Kairos and Lear capture institutional knowledge to optimize engineering productivity and process efficiency throughout compute-driven R&D stages. As industry leaders and practitioners pursue their product innovation and digital transformation objectives, R&D cloud platforms and domain-specific engineering solutions will be increasingly differentiated from DIY point solutions. R&D clouds empower companies to balance speed and control with policy-based automation and low-code composability, while focusing on user experiences that drive innovation.

Leveraging Virtual Engineering for Faster Product Development and Cost Efficiency

Authored & Presented By
Prasad Mandava (Visual Collaboration Technologies, Inc.)

Chaired By
Chair VamsiKrishna Pateel

In the field of engineering, where we're always striving to innovate and cut costs, the use of virtual engineering is gaining prominence. Our paper, titled "Leveraging Virtual Engineering for Faster Product Development and Cost Efficiency," explores how virtual engineering can drive innovation, speed up product development, improve quality through design robustness, save cost and help companies launch products faster to the market. Traditionally, building and testing physical prototypes consume a lot of time and cost in product development, hence delaying product launches. But now, we're seeing a shift towards using virtual tools instead. This shift is essential because market demands are pushing for faster, cheaper, and smarter simulation solutions. This paper investigates the evolution of digital technology in product development processes and highlights a critical gap - the underutilization of digital tools in post-processing, reporting, reviewing & communicating phases, such as results interpretation, results reporting, and rapid results reviews. The emergence of a new class of simulation tools addresses this limitation, offering solutions such as a common data structure for integrated multi physics results, intelligent data mining for identifying potential failure modes, advanced 3D visualization, highly compressed simulation results, automated templates for results processing, reporting and the creation of 3D Digital Slides for rapid results reviews/communicating with stake holders. This presentation will focus on the key aspects of democratizing the virtual engineering process in specifics the process of analysis results review. Will highlight automation solutions that are used to quickly extract and visualize analysis results from a broad range of multi-physics solvers, so design decisions can be made faster and with more confidence. Some of the benefits include: 1. Smart automation & efficient multi-format graphic displays (Digital Slides) 2. Ease of sharing graphical results data with the engineering teams using HTML capabilities 3. Considerable reduction of output file sizes so output can be stored for future access. 4. Automatic digital report generation By embracing these new classes of virtual engineering tools, we can speed up product development, launch products faster to the market and save money, all while staying at the forefront of engineering innovation.

Quick and reliable simulation process automation to reduce product development time and cost

Presented By
Karlheinz Peters (Novus Nexus)

Chaired By
Chair VamsiKrishna Pateel

Authored By
Karlheinz Peters (Novus Nexus)
Davis Evans
Bruce Webster (Novus Nexus
Inc)
Mario Felice (virsolTech Engineering Consulting
LLC)

Wide-range use of simulations through all phases of product development is becoming increasingly important to meet the growing challenges of global competition. Simulation not only helps develop competitive products faster but also helps achieve cost efficiencies by reducing the number of necessary prototypes and related physical testing. However, to realize all the achievable benefits of simulation, it needs to be performed systematically, consistently, and efficiently. In an optimal scenario, simulation is directly available to a broader group of users, not just simulation specialists, with designers and analysts collaborating closely. Automated simulation processes for analysts, designers, and product developers are essential for realizing this systematic and efficient use of simulation. Historically, such broader use of automated processes often fails due to the task's difficulty. Automation via common approaches like scripting (Python, etc.) quickly results in a prohibitive effort for implementation and upkeep, especially when complex or vastly differing geometries are involved. Not only is the initial setup complex here, but extensions or modifications down the line can become exceedingly complicated, too often resulting in a complete rebuild of the processes. Therefore, new approaches are needed to overcome the current challenges and secure a fuller range of benefits from simulation, including methods that enable fast, easy, and robust process automation. This presentation explores how a combination of simulation-specific no-code tools (based on abstract modeling), together with universal "low-code" tools, can drastically simplify automation, even and especially when complex products are involved. The impact of systematically automated simulation processes on the overall development cycle is also analyzed. The ability to automate simulation processes without penalty, which means creating an automated process with a similar effort as setting up a traditional one-off simulation, opens more opportunities to benefit from the known advantages of automation, including the ability for non-CAE specialists to initiate reliable simulations, the relief for analysts from unproductive routine work, the preservation of simulation knowledge, and the continuous use of best practices for future simulations.

Parametric modeling of SOEC System in a Grid Energy Storage Application and Democratization for Wider Adoption

Authored & Presented By
Apurva Gokhale (ESTECO North America, Inc.)

Chaired By
Chair VamsiKrishna Pateel

CAE CAD and CAE Abstract Models: two sides to the new Product Development Coin

Presented By
Bruce Webster (Novus Nexus)

Chaired By
Chair Jeff Weirsma

Authored By
Bruce Webster (Novus Nexus)
Davis Evans
Karlheinz Peters (Novus Nexus
Inc.)
Mario Felice (virsolTech Engineering Consulting
LLC.)

Wide-range use of simulations through all phases of product development (PD) is becoming increasingly important to meet the growing challenges of global competition. Simulation not only helps develop competitive products faster but also helps reduce the number of necessary prototypes and related physical testing. There is a valuable product development 'coin' that has on one side CAE CAD and on the other side CAE Abstract models. Others have called this 'coin', the fundamental 'black box' or 'digital thread' connecting a 'product variant shape' all the way through the CAE process delivering 'product fitness' for PD design review considerations. Using a detailed hypothetical example of current typical PD process for an internal combustion engine (ICE) powertrain will be compared with a PD process that uses CAE CAD and CAE Abstract Models to show how valuable it is. In addition, the paper and presentation will define what CAE CAD is in context with Manufacturing CAD that all companies proficiently produce today. In particular how CAD data management software like Team Center, WindChill, 3Dexperience and/or SPDM products with CAD software like NX, Creo and CATIA are used to generate CAE CAD design variants rapidly. The paper will also describe how CAE abstract models combined with CAE CAD design variants can be evaluated automatically delivering great product performance or fitness information including html based visualization results for Design Reviews using VCollab. Using the detailed realistic example of PD for internal combustion engine, PD leaders should easily identify their current PD process in the 'as is' description and imagine the extraordinary benefit of transforming to a PD process that uses the new PD coin that has CAE CAD and CAE Abstract Models. For this paper and presentation, this product development 'coin' has further value for enabling the 'democratization' of CAE simulations because the PD work for delivering Design Review ready 'fitness' assessments for various design variants is reduced to manageable CAD work only. A corporation's computers, including HPC capabilities, and software to process the 'coins' are all that is needed. As a result, a corporation's CAE expertise can be leveraged by their CAD employees.

Aerospace Structures Simulation Analysis Process and Data Management

Presented By
Kenneth Dang (The Boeing Company)

Chaired By
Chair Jeff Weirsma

Authored By
Kenneth Dang (The Boeing Company)
John Dong (Boeing)

Structural modeling, simulation and analysis generates a large amount of models/data during product development, production and product support. Configurationally managing and maintaining the traceability of those analysis artifacts are critical to aerospace product qualification and certification (PQ&C). Simulation/Analysis Process and Data Management (SPDM/APDM) are critical to Boeings effort in digital transformation. In this presentation, we will discuss Structural Analysis Process and Data Management (APDM) where a digital thread of analyzing aerospace structural integrity will be demonstrated by capturing CATIA geometry, generating finite element models at various levels, performing margins of safety calculation, generating Strength Check Notes (SCNs), all of which are critical artifacts for airplane certification and fleet support. Presentation demo is focused on the Interior Responsibility Center (IRC) Integrated Design Engineering and Analysis System (IDEAAS) digital thread systems in 3DExperience (3DX): from capturing an IRC assembly CATIA V6 model to performing analysis of the assembly to creating SCN through automation and storing SCN in 3DX PLM which leverages 3DX PLM capability for data traceability and data synchronization. Details of product lifecycle management of analysis SCNs which include the use of 3DExperience (3DX) process composer to link and automate SCN analysis process, store/manage SCNs based on 3DX product structure, and configuration control of SCNs through 3DX impact graph, etc. will also be discussed. We will also discuss the implementation and deployment of APDM for Boeing enterprise programs and demonstrate the benefits: Reduction in conceptual design-analysis cycle time, cost savings and enabling of first-pass quality.

Next Generation Workflow: A Graphical Workflow Interface to Increase Analysis Credibility and Integrate Expertise

Presented By
John Mersch (Sandia National Laboratories)

Chaired By
Chair Jeff Weirsma

Authored By
John Mersch (Sandia National Laboratories)
Ernest Friedman-Hill
Kevin Olson (Sandia National Laboratories)

Sandia is an ever-evolving research institution, continually developing innovative solutions to solve complex national security problems. Our diverse workforce leverages emerging tools in novel ways across the spectrum of engineering disciplines, creating unique, cross-cutting workflows to meet the challenges we face. As our problem space continues to grow in complexity and answers are demanded more rapidly than ever before, we need unified tools which can ensure credibility, integrate our diverse areas of expertise, and reduce time to solution. The realities of the current environment necessitate a paradigm shift to be truly successful. Next Generation Workflow (NGW), a graphical interface for workflow construction, provides an excellent solution which reimagines our approaches to conventional computational simulation and analysis. NGW provides an easy-to-use interface for defining, understanding, sharing, monitoring, and explaining workflows, enabling teams to seamlessly collaborate and leading to solutions that were previously challenging to obtain. NGWs graphical interface, coupled with the underlying data management repository, increases analysis credibility and leads to more immediate results due to the increased confidence it offers analysts. This confidence growth enables analysts to balance many projects more easily, spending less time getting spun up when reengaging with current work. This presentation will highlight multiple project examples which leverage Sandia Analysis Workbench (SAW) and NGW to illustrate the added value of using the toolset. The main case study is a project investigating the viability of machine-learned surrogate models for use in component- and system-level finite element models to increase modeling accuracy and maintain tractability of the larger analysis. In this project, a diverse team of subject matter experts (SMEs) need to collaborate and execute their own workflows, including training data generation, ML model training, and finite-element analysis validation. These workflows would historically need to be executed by the specific SME, slowing the progress of the project as the time demands of everyones calendar delay execution of their individual tasks. However, NGWs ease of execution democratizes the workflows and enables other members of the team to create, edit, and execute workflows without intercession from the SME.

Leveraging AI to Boost Computation-Intensive Simulations with Ansys and NVIDIA

Authored & Presented By
Mustafa Kaddoura (Ansys, Inc.)

AI computing, leveraging advanced machine learning techniques, transforms the extraction of insights from large datasets, revealing patterns beyond traditional methods. In this workshop, we will first explore how AI is applied to engineering simulations, particularly in physics-based numerical simulations across industries. We will discuss the challenges due to high computational demands and how physics-informed machine learning (Physics-ML) models can significantly speed up workflows with comparable accuracy. The AI model is efficiently trained on Nvidia GPUs with large datasets and provides valuable insights even for non-specialists. In the second part, we will demonstrate the application of AI in CFD with a practical industrial use case using Ansys SimAI, a cloud-enabled generative AI platform. SimAI rapidly assesses new design performance and allows extensive testing of design alternatives. We will show how to simplify geometry generation and evaluate mixing tank performance using SimAI predictions, trained on diverse 3D Fluent simulations. This streamlined process saves time and resources, making it easier to investigate the effects of different geometries and operational factors on mixing processes.

Simulation Techniques to Improve Productivity and Sustainability in Glass Fiber Manufacturing

Authored & Presented By
Bruno Purnode (Owens Corning Science & Technology)

Chaired By
Chair Bruce Webster

With the focus on decarbonization, automation and the use of big data, there are major opportunities to increase production efficiency in the glass manufacturing industry. But a first step in developing such initiatives starts with a good understanding of the process and the need to provide timely responses. This includes the development and installation of new sensors and controls schemes that add new insights to the process, but also the development of modeling techniques for fundamental understanding. With ever increasing computing power and more sophisticated mathematical methods, numerical simulation has become a daily use in many processing industries. The glass industry has used those techniques and this paper reviews the practices involved in the mathematical modeling of transport processes in the glass fiber making sector. The processes considered include various forms of glass processing techniques, with a focus on glass melting and conditioning. Besides, other process steps benefit from modeling as well, such as forming and drying, that are critical to product quality. Mathematical modeling is nowadays used extensively for applying technical expertise to glass fiber manufacturing. This includes troubleshooting, adjusting operational settings for improving performance, changing production levels, and for screening concepts for improved process design. Balancing model quality and its cost is a key choice that needs to be made early in the process and the limitations of every approach needs to be well understood. This involves decisions on the material properties, physical/chemical phenomena present, process conditions and the levels of geometrical details. Validation of the techniques often rely on sensors and the development of such new sensors are critical. Those models also allow for simulation-based Digital Twins to be developed. Those provide real-time insights in the process through virtual sensors. A selection of results is presented to illustrate practical applications of modeling in design and operations.

Validating Ansys Ceramic Chip Resistor Solder Fatigue Model

Authored & Presented By
Arvind Purushotaman (Ansys, Inc.)

Every day, it becomes increasingly important to rely on software for predictions. Yet a common challenge with simulation is being able to trust the accuracy of the predictions. This becomes even more challenging when there is sparse literature that can be used for validation, such is the case for Ceramic Chip Resistor Solder Fatigue Models. Solder Fatigue is a major cause of failure in electronic assemblies over time. A wide variety of factors influence solder joint reliability, and any one of these factors can drastically decrease solder joint life. As such, properly predicting as well as mitigating potential causes of solder joint failure during the design and manufacturing process can prevent future costly problems. Ansys generates fatigue life predictions with the Blattau model. The Blattau model is an energy-based model that shows fatigue lifetime is highly dependent on temperature range, dwell time, and temperature ramp rate. The goal of this presentation is to highlight an extensive and transparent validation process conducted so that users confidently utilize software during all phases of the design and production process. Due to lack of transparent ceramic chip resistor data available online, from 2020 to 2022, Ansys thermal cycled boards containing 2512 and 1206 resistors at four temperature conditions. -40-125C, -15-125C, 10-125C, and 35-125C until greater than 50% of each resistor type failed. These four temperature conditions were selected to accurately reflect the common temperature ranges that these resistors would be utilized in as well as varying temperature differentials. The goal of this validation effort was to supplement sparse data available online, as well as provide completely transparent test data that users could validate themselves. As such, the resistors were cross sectioned to confirm failure, DIC and DMA were conducted to determine board CTE and modulus respectively, and all the parameters were determined and input into Ansys to provide a life prediction. By aggregating literature as well as in house test data, we were able to have great correlation between simulation results and experimental data.

Electromagnetic Heating Simulation of Medical Treatment for Infections After Knee Replacement Surgery

Authored & Presented By
Josh Thomas (AltaSim Technologies LLC)

Digital Twin for Operations - How to Enable Intelligent Operations

Authored & Presented By
Dheeraj Vemula (Altair Engineering)

Chaired By
Chair David Quinn

Simulating the viscoplastic-damage response for semicrystalline polymers.

Authored & Presented By
Jeff Wiersma (MillerKnoll)

Chaired By
Chair David Quinn

Connecting MBSE to Spacecraft Engineering Modeling & Simulation

Presented By
Joshua Reicher (Ansys, Inc.)

Authored By
Joshua Reicher (Ansys
Inc.)
Alexander Busch (Ansys)

Model-Based System Engineering (MBSE) is gaining widespread adoption in the aerospace industry and with this adoption there is an increasing need to extend the MBSE models to allow for connections with complex engineering analysis by means of modeling and simulation. We will showcase an MBSE analysis framework featuring an end-to-end digital workflow for a spacecraft constellation system. The framework incorporates an example MBSE model (requirements and architecture modeled with a SysML 2.0 editor) of a satellite constellation and the corresponding subsystems of this constellation, such as satellites, solar panels and antennas onboard the satellites. The MBSE showcases how to incorporate attributes of various subsystems, for this workflow we will showcase impacts of changing solar panel sizing and the corresponding impact to requirements by connecting to engineering modeling and simulation. The framework expands on the MBSE model by incorporating system-level modeling and simulation of the space mission (Digital Mission Engineering), including an analysis of the spacecraft's injection into a geostationary orbit as well as corresponding sensing and communication metrics for the mission in geostationary orbit. The mission model of the spacecraft is extended to include the thermal impacts by executing an orbit thermal analysis to understand the temperature impacts to onboard systems during the mission. Upon completing the engineering analysis, the solar panel sizing impact on cost can be assessed with spreadsheets for calculations on payload cost. From these analyses, we demonstrate how this MBSE framework allows for collaborative system design, analysis, and verification by means of a trade study example and virtual verification of system requirements.

Unleash Creativity with Design Simulation and Neural Networks

Presented By
Suman Sudhakaran (TriMech)

Chaired By
Chair Leanne Campbell

Authored By
Suman Sudhakaran (TriMech)
Chris Duchaine (Trimech)

Our ability to perform high-end analysis without worrying about upfront hardware infrastructure costs and our ability to share data and collaborate with globally dispersed teams across the organization has become a reality using the latest simulation tools, such as those available on Dassault Systèmes' 3DEXPERIENCE platform. We have also come a long way since the advent of Machine learning and AI functionality. Machine learning is focused on getting computers to learn and perform tasks that require human intelligence. By letting computers handle the heavy lifting of design simulation, we can have more time for creative problem-solving. A well-trained neural network model for predicting outcome is not new in the world of manufacturing or systems engineering. Now that AI, specifically machine learning, has become a general-purpose technology with widespread use across many industries, it would help to look at how product design engineers can further benefit from combining the power of upfront design simulation and AI in a more streamlined approach. Machine learning is already embedded in many of the features available in Dassault Systemes design solution. Why Machine Learning and AI in the design process? Our fast-paced, digitized world presents us with more complex physics problems to tackle. Design simulation can involve many iterations tying up computational and human resources. A well-trained neural network model can be a game changer as it learns from existing designs, discerns patterns, and predicts optimal solutions. AI tools help Aspire and Inspire us to further our creativity and not replace design engineers. This approach saves time and frees up the design engineers to work on their next engineering feat. Of course, it is up to the design engineers /simulation users to verify the results as these predictive models are never meant to entirely replace physical testing. In this presentation, we will use SOLIDWORKS and 3DEXPERIENCE Simulation app to run a parametric design study that varies assembly model dimensions to evaluate equivalent displacement or stresses. The design variables and results from this parametric (or what-if scenario study) simulation study will be used to build a neural network. Supervised learning method or a combination of supervised and unsupervised learning methods might be employed to build the deep learning model that aims to predict the outcome of simulation. Although this approach has already been proven, this specific example will illustrate its ease of use using the 3DEXPERIENCE platform for design simulation. In our vast digital landscape where creativity and precision converge, design simulation and neural network models can further architect and accelerate the pace of product innovation.

Enhancing CAE Modeling Efficiency and Predictive Analytics in Automotive Design

Authored & Presented By
Ravi Nimbalkar (BETA CAE Systems USA, Inc.)

How to Effectively Communicate Innovative Ideas

Authored & Presented By
Brian O'Keefe (Articulus LLC)

Considering uncertainty in the development of a bulk material simulation

Authored & Presented By
Ian Paulson (Rockford Engineering Works Ltd.)

The discrete element method (DEM) has become an industry standard tool in the development of material handling equipment for agricultural, aggregate, and mining applications. In particular, recent advances in the use of graphical processing units (GPUs) have made the DEM a suitable approach for solving industry-scale problems in a timely fashion. When developing a granular material simulation in bulk material handling applications such as mining, the creation of a suitable material model is often required. Typically, a calibration process is performed using measured macro-scale bulk material data such as a particle size distribution, bulk density, internal friction angle, wall friction angle, and potentially others. These data all contain inherent uncertainty. Once suitable measurement data have been identified or collected, an inverse calibration procedure is typically performed to identify the values of the micro-scale parameters of the chosen DEM contact model. Due to the non-linear nature of particle systems and the calibration process often being underdetermined (i.e., the DEM contact model has more micro-scale input parameters than available unique test data), suitable parameters are often non-unique and strong parameter interactions are present. In this work, the uncertainty in a DEM calibration workflow is investigated for a typical but hypothetical dry, low-cohesion bulk material. Sources of uncertainty including typical instrumentation error, data fitting, sampling uncertainty from sampling a heterogenous bulk material, and inverse calibration are considered. Following this, the impact of material model uncertainty is demonstrated in two example simulations of common mining and material handling equipment.

DEM and iDEM Modeling of Triaxial Tests on Ottawa Sand

Presented By
Travis Shoemaker (University of Illinois Urbana-Champaign)

Authored By
Travis Shoemaker (University of Illinois Urbana-Champaign)
Shamontee Aziz
Daniela Gurruchaga Renjel
Youssef Hashash
Scott Olson (University of Illinois Urbana-Champaign)

The mechanical behavior of soils mixed with nanomaterial additives is of interest for civil infrastructure and military applications. Several studies have performed physical laboratory experiments on soil mixtures; however, these experiments are labor intensive. Numerical models offer the potential for digital prototyping of soil mixtures. This presentation will demonstrate how discrete element modeling (DEM) and its impulse-based alternative (iDEM) have been calibrated to a series of physical laboratory experiments on Ottawa sand and Ottawa sand-nanomaterial mixtures. A uniformly graded F-65 Ottawa sand with subrounded to rounded particles was chosen as the baseline material. The goal of this study is to select DEM/iDEM micro-mechanical parameters for simulations to reproduce the physical experiment results. Isotropically consolidated drained (CD) and undrained (CŪ) triaxial tests were performed with confining pressures ranging from 50 to 200 kPa. Triaxial tests were chosen as the type of test because they document the strength, stiffness, and stress path of the material. DEM was selected as the numerical modeling method because DEM represents the granular nature of sands by explicitly modeling individual sand particles and interparticle collisions. Prior studies have found that particle shape significantly impacts the strength and volumetric response of sands, and therefore, DEM polyhedral particle shapes were used to represent the subrounded to rounded F-65 sand. Although DEM can capture the granular mechanical behavior of soils, it is computationally expensive and a simulation of a single triaxial test can take on the order of hours using an engineering workstation. In the present study, IDEM is used as a faster numerical method for simulating triaxial testing. Unlike DEM, which operates on interparticle forces and integrates accelerations, iDEM operates on interparticle impulses and integrates velocity. IDEM can use larger simulation time steps, and consequently perform simulations more quickly because it has greater numerical stability than conventional DEM. A parametric study of the micro-mechanical parameters was performed for baseline sand and sand-nanomaterial mixtures to determine the parameters that best represent the laboratory results. The micro-mechanical parameters include contact stiffnesses, the interparticle coefficient of friction, and the contact damping ratio. The stress path, deviatoric stress, and shear induced pore water pressures calculated from each simulation are compared to those measured by the physical tests. The simulations show that a single set of micro-mechanical parameters can be used to reasonably predict the mechanical behavior of the F-65 sand over a range of confining stresses for CD and CŪ triaxial tests.

Developing Efficient Dust Removal Systems for Solar Panel Installations Using EM-DEM Coupled Simulations Approach

Presented By
Ahmadreza Haghnegahdar (Ansys, Inc.)

Authored By
Ahmadreza Haghnegahdar (Ansys
Inc.)
Sunil Acharya (Ansys)

As demand for sustainable energy rises, large-scale solar installations are expected to contribute nearly 10% of global power by 2030. Yet, environmental factors, such as dust accumulation, significantly hinder solar panel efficiency. Prolonged dust buildup can slash efficiency by up to 30% monthly, translating to substantial revenue losses. A mere 1% power decrease can mean nearly $200,000 in annual losses for a 150-megawatt installation. Globally, a 3 to 4% reduction in solar plant output results in revenue losses ranging from $3.3 billion to $5.5 billion. To address this challenge, innovative technologies such as electrostatic repulsion have emerged as promising solutions for mitigating the adverse effects of dust accumulation on solar panels. This study explores the design and optimization of efficient dust removal systems tailored to the specific environmental conditions of solar installations, within the framework of sustainable engineering principles. Factors influencing dust accumulation, including local wind patterns, panel orientation, and the adhesive properties of panel coatings, are analyzed to develop effective dust management strategies. The statistical distribution of dust particle size and shape, which varies by geographical location, highlights the necessity for robust optimization techniques to ensure peak performance across various accumulation scenarios. When electrically charged sand particles are placed in an electric field, they are subject to an electric force, which is the mechanism for dust removal systems hereby studied. Researchers aim to assess the effectiveness of different particle concentrations, electromagnetic field voltages, and particle charges to improve dust removal efficiency through simulation-based evaluations. The present work uses a comprehensive suite of simulation tools, including discrete element method (DEM), low-frequency electromagnetic (EM), which are used for modeling dust removal systems. By analyzing detailed simulation data, researchers can develop equipment that enhances the overall performance of solar installations. Designing efficient dust removal systems for solar panel installations represents a crucial endeavor in maximizing the potential of solar energy. Through the integration of simulation-based models for particle dynamics and electrostatic fields, this work aims to analyze the dust removal efficiency for distinct operating conditions and sand particles.

How to Effectively Communicate Innovative Ideas

Authored & Presented By
Brian O'Keefe (Articulus LLC)

VCollab's Smart Automation & Rapid Results Review

Authored & Presented By
Sumit Singh (Visual Collaboration Technologies, Inc.)

FE Simulations for Medical Devices: Applying Solution Verification Procedures to Nitinol and PLLA Stents

Presented By
Sean Teller (Veryst Engineering LLC)

Authored By
Sean Teller (Veryst Engineering LLC)
Ehsan Osloub (Veryst Engineering)

Numerical simulation models, and in particular solid mechanics Finite Element (FE) models, are used extensively in the medical device industry to support device design, reduce prototype iterations and testing, reduce design times and cost, and provide evidence for patient safety. The US Food and Drug Administration requires regulatory submissions that utilize numerical models to follow rigorous verification and validation procedures prescribed by ASME-VVUQ 40 to assess the credibility of these models. These verification and validation methods de-risk the process by excluding incorrect or inaccurate FE simulations that may lead to patient harm or undesirable performance. In this work we will focus on FE solution verification, which relates to ensuring that the mathematical model in the simulation is accurately computed and the error due to model discretization is negligible. Model validation is also required and involves comparing simulation results to test data to ensure that the model accurately predicts the real-world performance. In this presentation, we simulate the radial crimping of two separate stent designs: a stent made of the superelastic material Nitinol, and a stent made from the polymer Poly L-lactic Acid (PLLA), as both materials are widely used in stents and other implantable medical devices. Both stent designs are reference designs available in the literature. We model both materials with advanced constitutive models to accurately capture the complex behavior of each material. For the Nitinol material, we use an superelastic material model that captures the phase change behavior of the Nitinol material. For the PLLA material, we use a viscoplastic material model, the Abaqus Parallel Rheological Framework model, based on PLLA test data and advanced material modeling techniques. We simulate the crimping of the stents, extracting the crimp force and the maximum principal stress as our quantities of interest. We simulate each model with three different mesh configurations and use the Grid Convergence Index (GCI) approach to estimate the discretization error and approximate the exact solution to the problem. We demonstrate that both models are in the asymptotic range, thus the models are verified. We share learnings from this exercise and guidelines for others that use computational modeling and simulation to support medical device development and regulatory submissions.

Constitutive modeling of Human Sigmoid Colons using Neural Networks

Presented By
Aashish Ahuja (BD Technologies and Innovation)

Authored By
Aashish Ahuja (BD Technologies and Innovation)
Boaz Assaf (Colospan)
Marcus Rademacher (BD)

The human colon has been subject to many diseases but has been understudied, leading to several gaps in therapy. The severity of the disease could vary from something simple, such as constipation, to more complex and life-threatening, such as colorectal cancer. In fact, according to the American Cancer Society, colorectal cancer is the second most common cause of cancer deaths in the USA. To create accurate computational simulations of surgery procedures and design effective therapies that include interaction with medical devices, it is crucial to develop appropriate constitutive models that can accurately characterize the mechanical behavior of colon tissue. Several researchers have captured the mechanical response of the swine colon or estimated elastic properties of the colon over small strains to model therapies in treating some colon-related diseases. Building upon the work published previously, we develop constitutive models that utilize the flexibility of neural networks while accounting for the human colon's complete non-linear elastic and anisotropic behavior. The developed model would have the capability to predict colon behavior under large deformations and support the design and assessment of novel medical devices using in silico analysis. We leverage the test data on the distal part of the human colon, called the sigmoid colon, from a previously published study. The mechanical response of the sigmoid colon samples harvested from multiple patients was recorded using uniaxial tensile testing in circumferential and axial directions. The data encompasses structural contributions from all the layers (namely mucosa, submucosa, muscularis externa, and serosa) of this colon region. Next, to process the test data and fit it to a robust anisotropic hyperelastic model, we perform constitutive fitting considering data from both directions of the colon. The constitutive model utilizes the capabilities of a recently proposed neural network and assigns appropriate weights to the different activation terms in the network model. The implemented algorithm suggests a constitutive model that is polyconvex, thermodynamically stable, and provides robust predictions. We also simulate a colon interacting with a Barostat using the developed constitutive model, which will be validated against bench experiments.

Evaluation of heterogeneity and load dependence of mechanical properties of pharmaceutical tablets

Authored & Presented By
Alex Fok (Minnesota Dental Research Center for Biomaterials and Biomechanics)

Using the Latest 3D Printing Technology to Accelerate the Digital-First Process

Authored & Presented By
Brent Vorst (Kinetic Vision)

Chaired By
Chair Tyler London

The consumer packaging industry is facing a critical crossroads between the way it traditionally packages goods and the need to move to a more sustainable solution. The need to transform consumer packaging by way of lightweighting, selecting biodegradable alternatives, and moving to a circular model has become very real and urgent. Companies find themselves constantly looking for a better way to promote sustainability while still offering convenience to the end customer. This transformation will require an innovative approach and collaboration across the entire supply chain. Driven by this need to swiftly improve and adapt, Kinetic Vision will present a study that demonstrates the efficiency and effectiveness of a novel, streamlined product development process that integrates computer-aided design (CAD), digital thickness prediction, design of experiments (DOE), 3D printing, and manufacturing line testing. By leveraging these tools, we were able to rapidly design, optimize, and manufacture a new bottle design. The process began by creating a parametric CAD model of the bottle, which allows for granular control over the dimensions and overall shape of the product. Next, a digital thickness prediction method was used to generate accurate thickness models for all CAD designs. To identify the most promising designs, a DOE was executed covering a design space of geometry and material properties. The top three bottle designs were selected for further evaluation. Physical prototypes of each top design were 3D printed with realistic thickness distributions. The 3D printing technology used creates bottles that physically behave similarly to the mass-produced EBM bottles. The 3D printed bottles were physically tested on the manufacturing line to ensure their compatibility with the production process. The integration of CAD, digital thickness prediction, DOE, 3D printing, and manufacturing line testing proved to be an efficient and effective approach to product development. This digital-first method reduced the time to market by roughly 50% and the prototyping cost by 90%.

Aseptic Fill Package Development using Bipath® and FEA

Authored & Presented By
Jay Yuan (Stress Engineering Services)

Chaired By
Chair Tyler London

Aseptic fill package design and development becomes increasingly challenging due to the growing sustainability demand of minimum packaging. BIPATH® is an ideal tool to address this issue. The program is designed for the early evaluation of package effectiveness against vacuum-induced failures, such as paneling, ovalization, or base buckling, without needing any physical samples of the package. The program includes two parts. The first part is to use finite element analysis (FEA) to model the package performance under pressure or vacuum loading conditions. The second part of the program takes the input from the FEA and solves a series of equations related to the package headspace and product through an iterative approach using visual basic programming in Excel. BIPATH® is originally developed for hot-fill PET package development in 2006, it has continuously evolved to encompass a diverse array of packaging types and pressure/vacuum-prone filling, processing, and distribution systems. The packaging types include injection/extrusion blow-molded plastic bottles, injection-molded or thermoformed tubs and cups, as well as aluminum and steel cans. The various pressure/vacuum-prone scenarios that the program can address include hot fill, retort, aseptic fill, high-pressure process (HPP), carbonation, nitrogen dosing, steam flushing, altitude and temperature variations in distribution, air shipping, product out-gas or oxygen consumption, oxygen/CO2 ingress or egress, water vapor transmission, and plastic creep deformation over time. BIPATH® outputs the package pressure safety factor, defined as the ratio of package pressure allowable to pressure residual inside the package, provides container/closure suppliers and brand owners with a straightforward metric for gauging package performance in the early stages of development, eliminating the need for physical samples. This program leveraging finite element method empowers stakeholders to proactively manage and optimize pressure or vacuum through strategic adjustments in container and closure design, product content, process conditions (pressure, temperature, and duration profiles), and shelf-life commitments. The paper substantiates the theoretical underpinnings of the BIPATH® program and finite element modeling, along with its real-world applicability through a compelling case study, showcasing its validity and versatility. A combination of BIPATH® program and FEA emerges as an indispensable tool for packaging and process design and optimization, revolutionizing the industry's approach to the aseptic fill package development and beyond.

Line-Convey and Vending Modeling in PepsiCo: Beverage Packaging R&D

Presented By
Muhammed Imam (Pepsi Co Beverage R&D)

Chaired By
Chair Tyler London

Authored By
Muhammed Imam (Pepsi Co Beverage R&
D)
Advait Bhat
Chao-Cheng (Joe) Shiau
Lei Zhao (PepsiCo)

Maintaining high efficiency, in PepsiCos manufacturing line is very important. Our bottles need to run at optimum speed without causing line jams. Line jams happen when bottles fall-over and restrict throughput at a certain section of the line. Therefore, whenever we design a new bottle geometry, we need to design, keeping its line stability in mind. Apart from line-performance, a new bottle needs to perform well in all the PepsiCo vending machines. To overcome these challenges in new bottle development, we took a modeling and simulation-based approach. Our line-convey and vend models are developed using discrete element method, computational fluid dynamic and rigid-body dynamics tools. The models can accurately predict behavior of our bottles at various sections of the manufacturing plant (i.e., incline-decline, 90 degrees turn, entry to palletizer). Performance of our bottles in different vending machines can also be evaluated. These tools are leveraged, very early in a packaging development process. Early feedbacks are provided to packaging developers on how a particular bottle is going to perform in real-life scenarios. Therefore, major concerns are addressed early in the packaging development process. When physical samples are available from our blow molder, they are sent to our line-convey testbeds. On the test bed, physical samples are run at various speeds. Conducting physical experiments at a later stage, helps us generate data for model validation. Positive outcome from trials in the testbed, also gives us confidence that the bottles will run at desired speeds on all our lines.

Leveraging Simulation for R&D, Innovation and Sustainability in Consumer Packaged Goods

Presented By
Tyler London (Reckitt (Hull))

Chaired By
Chair Stacie Tibos

Authored By
Tyler London (Reckitt (Hull))
Emma Proctor
Cameron Taylor (Reckitt)

Reckitt, a global leader in consumer health, hygiene, and nutrition, has been adopting a Digital First approach to R&D and innovation. As part of this transformation, there has been a significant growth in the application of modelling and simulation. This presentation will discuss a number of initiatives where finite element analysis (FEA), computational fluid dynamics (CFD), and discrete element methods (DEM) have been used to drive speed-of-innovation, consumer-centric product design, and sustainability. The examples will cover a virtual lab initiative whereby DEM models of key equipment used for the blending, mixing, and characterisation of powder feedstocks (for health and nutrition products) have been automated and democratised to enable deep insights to be made from design-of-experiment programmes that integrate both physical and virtual test data. Other examples will demonstrate the use of simulation throughout the packaging development process. From blow moulding simulations predicting bottle thickness distributions, to top load, panelling, and hydrostatic load simulations assessing package integrity, a comprehensive suite of tools are used to ensure optimal and sustainable packaging solutions. As packaging materials, especially plastics, rise to the top of the sustainability agenda, Reckitt has set ambitious targets to reduce its overall packaging footprint, and simulation is helping to deliver these goals.

Food and Beverage Discussion panel

Authored & Presented By
Stacie Tibos (Pepsi Co)

Chaired By
Chair Stacie Tibos

eDrive Engineering - A connected end-to-end design process

Authored & Presented By
Sudhi Uppuluri (Siemens Digital Industries Software)

Unleash innovation with AI/ML and Simcenter

Authored & Presented By
Mayank Vaish (Siemens Digital Industries Software)

Mentoring Workshop: How to be an Effective Mentor and Mentee - Hosted by the WISE Subcommittee

Authored & Presented By
Erin Berry (Collins Aerospace)

NAFEMS Membership Information Session

Authored & Presented By
Kathy Elliott (NAFEMS Americas)

Chaired By
NAFEMS Staff Member

PSE Subcommittee

Authored & Presented By
Jo Potts (NAFEMS UK)

Chaired By
Jo Potts

Innovative Applications of Multiphysics Simulations in Assembly and Fluid Dynamics

Authored & Presented By
Brant Ross (EnginSoft USA)

Dynamic Analysis

Authored & Presented By
Tony Abbey (FETraining)

Chaired By
Chair David Quinn

Making the most of small data in an age of big data: the epistemic uncertainty challenge problems

Presented By
David Riha (Southwest Research Institute)

Chaired By
Chair Alexander Karl

Authored By
David Riha (Southwest Research Institute)
Alexander Karl (Rolls-Royce Corporation)

The focus of the NAFEMS' Stochastics Working Group (SWG) is to champion and improve best practices that relate to stochastic engineering analysis and simulation methods and tools. It promotes the extension of current engineering analysis and simulation practices to include stochastic methods and tools to enable the virtual product development processes to be closer to the real-world behavior of the modelled systems and components. Over the last decade the SWG challenged the engineering community with benchmark problems in the field of uncertainty, and the most recent set of problems encompasses epistemic uncertainty. This means that the uncertainty can be reduced by gathering more information, however, the participant in the challenge is provided with just a small data set. The solutions received show a large scatter in the predicted probability of failure. The SWG will publish a Benchmark article to document different assumptions and solution approaches to better understand the differences in the solutions. Several of the solution approaches and solutions will be presented.

Stochastics Working Group Discussion Panel

Authored & Presented By
Alexander Karl (Rolls-Royce Corporation)

Fatigue and Fracture Analysis

Authored & Presented By
Tony Abbey (FETraining)

Next Generation CAE for Faster Design Cycles

Presented By
Fatma Kocer (Altair Engineering, Inc.)

Authored By
Fatma Kocer (Altair Engineering
Inc.)
Milan Raval and Shubhamkar Kulkarni

In this workshop, we will demonstrate: 1.Altair SimSolid, structural, and thermal solution based on meshless technology for large, complex assemblies 2.Altair's physicsAI for fast design predictions leveraging historical simulation data 3.physicsAI for manufacturing applications

Crack Driving Force Burst Pressure Analysis of Piping Bend Custom Shape Surface Cracks Using 3D Crack Meshes in Elastic-Plastic FEA

Presented By
Greg Thorwald (Quest Integrity Group)

Chaired By
Chair Sean Teller

Authored By
Greg Thorwald (Quest Integrity Group)
Ryan Holloman (Quest Integrity USA
LLC)

In this paper, a cracked piping bend (i.e., elbow) is evaluated to determine the maximum loading magnitude due to a combination of internal pressure plus axial force and bending moment with custom shape surface cracks. This work is a follow-up to a 2019 NAFEMS World Congress paper that also examined cracking in piping elbows. Improvements to the detection sensitivity of in-line inspection (ILI) tools now provide detailed crack shape profiles of defects detected in pipelines. A fracture mechanics analysis can include the custom shape surface crack in the finite element analysis (FEA) model to more accurately compute crack front stress intensity used to determine a limiting flaw size or limiting load (i.e., burst pressure). The crack driving force method is used here to determine the limiting load, which is described in the engineering best practice standard API 579-1/ASME FFS-1 Fitness-for-Service. A benefit of the crack driving force method is using an elastic-plastic analysis to better capture yielding of the ligament around the crack as the loading approaches the limit value. The elastic-plastic J-integral values along the crack front are examined to find the maximum crack front value used in the assessment. Converting the J-integral to equivalent stress intensity allows comparison to the piping bends toughness to determine the limiting load when the values intersect. A piping bend geometry is used to examine the modeling details of creating the 3D crack models with the custom crack shape, obtaining convergence in the elastic-plastic analysis in the FEA solver, computing the crack front J-integral values, and determining the limiting load. The necessary crack front mesh refinement is also shown and described. The elastic-plastic FEA, using the pipe materials stress-strain curve, captures crack front blunting as the load increases. The geometry model includes the straight sections at each end of the bend and uses the non-linear geometry option in the FEA solver to capture any ovalization of the piping bend. An axial crack orientation with the crack located near the bend extrados is examined. The custom shape surface crack results are compared to the typical semi-elliptical surface crack shape results to show the typical improvement in the predicted burst pressure for the piping bend. Many custom-shape surface cracks detected by ILI have a less severe profile than the typical semi-elliptical crack shape. A benefit of the more detailed custom-shape crack analysis is to include the extra ligament around the crack and obtain a more accurate and often higher burst pressure, benefiting pipeline operators in maintenance and repair planning.

The Crucial Role of Nonlinear Contact Analysis in Engineering Applications

Authored & Presented By
Mike Sheh (INTES GmbH)

Chaired By
Chair Sean Teller

Abstract Nonlinear contact analysis plays a crucial role in engineering due to the fact that such phenomena is a must-understand/must-calculate task in order to design properly. Here we particularly interested in long term contact phenomena, rather than short term impact type of contact. Using automotive powertrain engineering as an example, engine durability assessment depends on contact simulation on all engine parts subject to temperature loading profile. Similarly in transmission, proper gear design requires accurate gear force, due to contact, calculation. The list goes on. Contact analysis is also a must in numerous other fields such as civil engineering, aerospace engineering, biomechanics, etc., and in simple daily experienced components such as joints, bolts, fasteners, and connectors, etc. Contact phenomena is known to be one of the most challenging mechanics to simulate. The influencing factors include material properties, surface conditions (friction and lubrication), and temperature profile, etc. One of the biggest difficulties is the sudden change in load path when contact partners change from the open (gap) to the closed (contact) state. Those factors and requirements post challenges in algorithm development in order to achieve accuracy and computational efficiency. In this presentation, we will briefly touch on the latest development in non-linear code PARMAS. And we will its application in simulating two simple and commonly seen cases - window seal and hinges. In the case of window seal, contact between seal and the glass need to be accurately predicted in order to correctly design the force to move the glass and the corresponding motor that drive it. The rotating hinges, in particular in electronic components such as laptops, mobile phones, etc., are subject to high cycle of usage. Customer requirement and market competition has shifted from initially to meet a simple functional requirement to the 'right' feel to open/close the devise. In engineering terms, it means the force and torque need to be just 'right'. We will discuss the challenges experienced in both cases, and how contact analysis is conducted to support the design.

Use of Hot-Spot Stress Method in Estimating Weld Fatigue in Rail Equipment.

Presented By
Dipak Patil (Harsco Rail)

Chaired By
Chair Sean Teller

Authored By
Dipak Patil (Harsco Rail)
Michael Petersen (Harsco Rail)

Welded joints in steel structures in track maintenance equipment industry are typically assessed for fatigue using two different stress range approaches: nominal stress range and hot-spot stress range when using SN methods. The nominal stress range is a traditionally simplified method that provides a conservative estimation but lacks accuracy in considering stress concentrations. On the other hand, the hot-spot stress range method is a more advanced and refined approach that offers a more precise evaluation of stress concentration, making it suitable for complex geometries. In 2015, the British standard BS7608-2015 incorporated the hot-spot method for evaluating weld joints, especially when using numerical methods as Finite Element Analysis (FEA). The weld joint classes are now categorically defined for both nominal and hot-spot approaches in new introductions, whereas earlier, it was based on the nominal stress approach only. Choosing the appropriate stress range method depends on various factors, including the weld joint's geometry, stress orientations, loading conditions, the desired level of accuracy, and primarily the available SN curve data for predicting fatigue damage. The work presented in this paper explores the hot-spot and nominal stress range approaches in determining weld fatigue damage in Rail Track Maintenance Equipment. The identified welds were evaluated and compared for fatigue damage using BS7608 using both nominal and hot-spot approaches on different mesh types, and mesh sizes.

Calibrating Cohesive Zone Models for Impact Simulations

Presented By
Sean Teller (Veryst Engineering LLC)

Authored By
Sean Teller (Veryst Engineering LLC)
Scott Grindy
Mark Oliver (Veryst Engineering)

Adhesives are used extensively as replacements for traditional joining techniques (welds, bolts, rivets, etc.). Cost and performance demands drive engineers to reduce the amount of adhesives used for the joint while still performing as required, and accurate simulations of the designs are critical to ensure performance of safety of the devices. This is particularly true for impact events and high strain rate loading - as with other polymers, adhesive toughness can change with strain rate, and testing only at quasi-static strain rates may mean that the joint is over-designed, unnecessarily increasing cost and weight. Cohesive Zone Modeling (CZM) is an FE analysis technique that allows simulation engineers to model the behavior (and potential failure) of adhesives with a low computational cost, but testing for cohesive zone modeling with traditional methods means significant testing resources as well as time-intensive inverse calibration methods to select the cohesive zone parameters. In this paper we will discuss using standard fracture toughness tests like the Double Cantilever Beam (DCB, mode I), End Notched Flexure (ENF, mode II), and Single Leg Bending (SLB, mixed mode I-II) tests to measure the adhesive toughness as well as the traction-separation relationship. We use a J-integral based method with Digital Image Correlation (DIC) to directly measure the traction-separation relationship to then fit the model. Our method includes advanced test and measurement methods, computer vision, and validation simulations to ensure that the calibrated material model matches the measured material response. We will introduce high strain-rate methods of the tests to measure the impact performance and calibrate rate-dependent models, including a novel method for measuring the mode I test that removes the need to measure the loading force and crack length. Additionally, we will discuss methods for measuring CZMs for polymer joining, as well as biomaterial interfaces. Last, we will provide exemplar simulations using a calibrated CZM.

Material Calibration and Progressive Damage Modeling of 3D Woven Textile Composites (3DWTCs) in a Multiscale Framework

Authored & Presented By
Deepak Kumar Patel (Dassault Systèmes SIMULIA Corp)

One of the challenges of 3D woven textile composites (3DWTCs) structural simulations with finite element analysis (FEA) is knowing how to represent the multi-material systems in the structure. 3DWTC is manufactured by laying up the warp and weft fiber layers and consequently running a Z-fiber in the thickness direction to bind the in-plane layers together. The hybrid configuration of this textile composite that consists of Glass, Carbon and Kevlar �ber tows, is studied using a multiscale model. Based on micromechanics concepts, fiber-matrix constituents' properties are extracted from its macro behavior by calibration and assigned to individual fiber tows, modeled discretely for different material systems. This work explains how non-homogeneous and non-isotropic material test data like the composite materials can be calibrated using the developed calibration tool. It shows the creation of a mean-field homogenization (MFH) material model for a transversely isotropic unidirectional fiber tow. The MFH approach is based on analytical formulation and used for multiscale material modeling to model composite materials. This approach can calculate composite responses using properties of the constituents; it can also decompose the composite strain into constituent strains and compute the constituent-level responses. This mean-field homogenization approach can be useful to model progressive failure of hybrid textile composites by modeling damage at the constituent level. In this work, a representative volume element (RVE) is modeled with geometry and architecture inputs drawn from the literature. The simulation workflow is established with meshing the RVE, material modeling, and appropriate analysis procedure. The RVE and the progressive damage material models together provide a unique computational framework to predict the tensile strength of 3DWTCs, and its dependence on various geometrical and material parameters. The numerical simulation result predicts the global stress-strain response and the detailed local complex failure mechanisms of the textile composites. The reported results are found to be in good agreement with experimental data available in literature.

Digital Twin for Durability - How to Schedule Maintenance the Smart Way

Authored & Presented By
Dheeraj Vemula (Altair Engineering)

Multiphysics Design and Analysis of Silver-Based Low-Emissivity Coating Technology

Presented By
Laila Salman (Ansys Canada Ltd.)

Chaired By
Chair Kyle Koppenhoffer

Authored By
Laila Salman (Ansys Canada Ltd.)
Gregory Haugen
Christian Weinmann
Jaewon Kim (3M)
Anthony Leger
Duane Mateychuk (Ansys Inc.)
Amrita Pati
Laila Salman (Ansys Canada Ltd.)

Low-Emissivity (Low-E) Technology is a unique and cost-effective solution for Modern Energy Saving Windows. In this work, we leverage simulation to characterize the optical and high frequency performance for several Silver-Based Low-E Coating designs. We demonstrate close match with reported measurements for double and triple silver coatings. Wave optics and ray tracing analysis are combined to account for sub-micron features while modeling the larger window models. We also perform high frequency analysis to characterize the signal attenuation through the windows. Energy waste through windows is estimated to cost U.S. consumers roughly $40 billion each year. Up to 60% of the total heat loss in a standard window is estimated to be due to radiation losses. Low-Emissivity (Low-E) technology has been used to optimize the spectral response of the windows to reduce the heat transfer; the goal is to achieve high transmittance for visible light and high reflectivity at higher wavelengths. In this work, we focus on silver-based Low-E coating technology which comprises more than 90% of the overall Low-E Market today. Double silver and triple silver coatings are simulated according to reported designs. Given the one-dimensional uniformity of these structures, we can utilize analytical solutions based on transfer matrix method. For this, we have used Ansys Lumerical STACK, which is an ideal choice for fast optimizations of coating layers. The transmittance closely matches those reported for the double silver and triple silver coatings, filtering the light at higher wavelengths to reduce heat transfer. We also characterize the angular response for each coating before exporting the response to Ansys Speos. In Ansys Speos we will use ray tracing to quantify the irradiance on the inside surface of our test vehicle. Using Speos, we can vary the coating's physical design parameters to decrease mobile signal attenuation while retaining most of the coating's thermal properties. This will create an optimized design that balances the conflicting design criteria of thermal rejection and signal attenuation. The authors have incorporated the advanced material-vacuum deposition and optical design implementation to be simulated using Ansys HFSS to evaluate the overall design performance. We have investigated two different design approaches starting with the utilization of standard untreated glass model defined with surface impedance boundaries and then applied the same simulation approach on treated glass with silver-based deposition. Once the glass characteristics are optimized, Fresnel Coefficients can be extracted and applied to the train model's windows' surfaces. The transmission coefficient (coupling) between cellular tower/base station antenna and mobile antenna located inside the train is studied using Ansys HFSS SBR+. Free space antenna to antenna coupling, simplified and realistic train CAD models were used while characterizing the windows glass with single, double, and triple silver based Low-E glass layers. Dynamic modeling scenario is conducted for a moving train at 3.8GHz and 6GHz operating frequencies.

Themo-Mechanical Analysis of System Level Effects on Electronic Packages

Presented By
Arvind Purushotaman (Ansys, Inc.)

Chaired By
Chair Kyle Koppenhoffer

Authored By
Arvind Purushotaman (Ansys
Inc.)
Nathan Blattau
Natalie Raines (ANSYS)

Finite element modelling and analysis has been the state-of-the-art in reliability prediction of electronic packages for more than 20 years. Depending on the complexity of the package, computational time and the loading condition, the modelling method may vary. Literature on finite element analysis of electronic package is vast and quite comprehensive. However, discussions on system level effects such as bolting of printed circuit boards, housing effects and the likes on package reliability are limited. In this paper, effects of three structural conditions, namely, (1) Free Warp, (2) Semi-Constrained, & (3) Fully Constrained, on 256 pin 17mm x 17mm ball grid array (BGA) and 2512 resistor are discussed. Semi-Constrained condition being more representative of what one can see in the field while the other two conditions being two extremes of the structural condition, gives a holistic understanding of how reliability of packages are affected by mount holes in a printed circuit board. A 3D nonlinear thermo-mechanical analysis was completed in Ansys Mechanical for: (1) -40°C to +125°C and (2) +25 °C to +125 °C. The temperature conditions give insight into the damage accumulated in the solder layer by individually looking at the hot condition (+25°C to +125 °C) and the cold condition (-40°C to +25 °C) of the thermal cycle. The inelastic behavior of solder material, SAC305, was modelled using a Garofalo creep model and an energy-based model was adopted for life prediction. The prediction was compared with thermal cycle (TC) testing for the said conditions. Ten samples of each package type were mounted onto a 1.6mm thick PCB coupon. The test coupon was bolted to an aluminum fixture to constrain warpage depending on the structural condition. Four packages of each type were also mirrored to understand its effect on solder joint reliability. A total of 40 samples per package type were cycled till failure for the two temperature conditions at 11°C/min ramp rate with a 15 min dwell time. The characteristic cycles to failure were analyzed using a 2-parameter Weibull distribution and compared to the simulated model results.

The Philosophy of Co-simulation

Authored & Presented By
Philip Top (Lawrence Livermore National Laboratory)

Chaired By
Chair Kyle Koppenhoffer

Engineering components and systems do not exist in isolation, they interact with other systems often of wholly different nature. For example, a power transformer has thermal aspects and electrical aspects but also interacts heavily with electronic control and communication systems, design considerations include noise, safety, security, fault detection, and pollution controls. Furthermore, its operational lifespan and performance depends heavily on the environment in which it exists both from an electrical grid perspective and climate factors. Often these aspect on their own can be well simulated in different existing tools, but the answers to important questions require many of these aspects considered together. The tools required for this kind of analysis may have different execution patterns, different notions of time, be written for different software platforms, or even execute in different environments. Continually adding new features to existing simulation tools can result in overly complex software, documentation, and input requirements and place unrealistic expectations on the users and developers. The use of co-simulation, meaning the coupled execution of multiple independent execution units, presents a way out of this complexity, a means of bringing disparate aspects into modeling and simulation in a way that is manageable and sustainable. This presentation will outline a "Philosophy of Co-simulation" that has guided the development and use of a co-simulation platform used in the Department of Energy for addressing large-scale, complex problems in infrastructure analysis. It will explore aspects of co-simulation and design decisions that make up the selected co-simulation philosophy and affect the way simulations are managed, deployed, debugged, and developed. Topics covered will include networking, communication patterns, execution control, data serialization, time management, and how the tradeoffs in these aspects play out in different co-simulation software packages. A series of use cases will be presented from the energy sector demonstrating coupling in power transmission and distribution system models, along with natural gas, communications, transportation, water, and climate. These examples will illustrate how a "Philosophy of Co-simulation" plays out in different situations with simulators of different scales, types, communication patterns, and notions of times. The presentation will conclude with a discussion of the role co-simulation can play in coupling FEA models with other types of models, developing digital twins, model validation, hardware in loop testing, and uncertainty quantification, all based on the notion that "Simulations are better together".

Simulation of Green Energy Thermal Battery System for Improved Design

Presented By
Josh Thomas (AltaSim Technologies LLC)

Chaired By
Chair Jeff Weirsma

Authored By
Josh Thomas (AltaSim Technologies LLC)
Luke Gritter
Kyle Koppenhoefer (AltaSim Technologies)

Green energy from wind or solar farms needs to be stored for usage when the wind isn't blowing or the sun isn't shining. Thermal batteries can store energy as heat then convert it back to electricity as needed. New battery designs have the potential to reduce the cost of energy storage, therefore there is a large commercial interest in the development of new technology in this sector. Thermal simulations have been developed in COMSOL Multiphysics providing rapid proof of concept of a promising new design. Arrays of large graphite blocks are placed in already existent shipping and storage containers. The graphite blocks are charged with temperatures in the 2000°C range. While traditional photovoltaic (PV) cells can convert solar radiation into electricity, they are not efficient at high temperatures. In contrast, thermophotovoltaic (TPV) cells are designed to work at extreme temperatures. Long TPV radiation collectors are dropped into open spaces between the graphite blocks to convert radiation from the hot graphite blocks into electricity. Surface-to-surface radiation with frequency dependent emissivity effects are included in the model and capture the spectral behavior of the TPV cell surfaces. based on TPV cells are damaged at high temperature so waste heat needs to be extracted from panels via active cooling. The goal is to minimize heat flux into the TPV while maximizing radiation exposure for conversion into electricity. Critical design parameters are studied in the conjugate heat transfer models including the open space gas pressure and gas type, gap distance between graphite surface and TPV panel, and graphite conductivity. The transient effect of moving the TPV panels from side to side to reduce efficiency loss from localized cooling spots is also studied using a moving mesh formulation. Finally, simulations quantify the flow rates necessary for an active water-cooled system to successfully keep the TPV panels operating below failure temperatures.

High Fidelity Multiphysics Simulation-Based Reliability Assessment and Design of a 77 GHz Radar Sensor for Advanced Driver Assistance Systems

Presented By
Ushe Chipengo (Ansys, Inc.)

Chaired By
Chair Jeff Weirsma

Authored By
Ushe Chipengo (Ansys
Inc.)
David Dang
Satish Kumar
Pouya Shojaei (Ansys)

Improved vehicle safety, electrification and autonomy have emerged as the current key drivers for innovation in the automotive industry. Various advanced driver assistance systems (ADAS) have been developed to address the requirements of these key initiatives. Specifically, sensor based ADAS have been developed to improve vehicle safety while also contributing towards a fully autonomous vehicle. Lidar, radar, ultrasonics, and camera-based sensors are being used to provide vehicles with an accurate picture of its immediate surroundings through object detection and classification. While the most robust and reliable environment mapping strategy will involve some variant of sensor fusion, radar has emerged as the backbone sensing technology. Radar is currently used in lane change assistant (LCA), adaptive cruise control (ACC), automatic emergency braking (AEB), blind spot detection (BSD) and occupant detection applications, among others. In fact, some vehicles under development have as many as 10 radar sensors. Underlying physics behind radar sensing mandates sensor placement in locations on the vehicle that introduce new reliability concerns due to the harsh operational environment outside the vehicle cabin. We present a high fidelity, physics-based reliability assessment for a 77 GHz radar sensor and its associated electronics on a PCB. Specifically, we will present system level (PCB, components and chassis/housing) thermo-mechanical solder fatigue analysis and demonstrate the impact of operational environment on the solder fatigue life prediction. We will also present system-level results from a random vibration-based solder and lead reliability analysis that uses an explicit finite element analysis mechanical solver. Using a random vibration power spectrum density profile (PSD) specified by IEC 60068-2-64, we demonstrate the impact of mounting point location on random vibration-induced solder and lead failure predictions. Another crucial component of radar sensors is the radome. Specifically, the radome acts as shield that protects the antenna and electronics while also providing an electromagnetically optimum interface for transmission and reception of the reflected signals from the environment. Here, we will use simulation to predict radome deformation and survivability in low-speed crashes. A deformed radome can modify antenna radiation patterns and impedance due to new loading conditions. Severely distorted radiation patterns can compromise the ability of the radar sensor to reliably detect targets during operation. We will use a deformed radome to quantify the impact of deformation on the radiation characteristics of the radar sensor antennas.

Enhanced Prediction of Multiple Shot Impacts using a FEA-CFD-DEM Modeling

Presented By
Ahmadreza Haghnegahdar (Ansys, Inc.)

Chaired By
Chair Jeff Weirsma

Authored By
Ahmadreza Haghnegahdar (Ansys
Inc.)
Sunil Acahrya
Satish Kumar (Ansys
Inc.)

Shot peening is a widely used surface treatment technique in various industries, aimed at enhancing the mechanical properties and fatigue life of metallic components. This method involves bombarding surfaces with small peens to induce compressive residual stresses and fortify the material. However, ensuring uniform coverage and optimizing parameters remain persistent challenges. Moreover, experimental studies are often costly, time-consuming, and restricted in scope. To address these challenges, this study proposes a comprehensive simulation framework that merges the capabilities of a finite element analysis (FEA), discrete element method (DEM), and computational fluid dynamics (CFD) solvers. This framework facilitates the evaluation of key performance indicators of peening, such as particle transport, surface deformation, dimple area, and coverage area. In addition, the validated framework enables the optimization of process parameters like shot and material properties. The FEA solver, utilizing Ansys LS-Dyna, simulates the dynamic response of materials under shot peening. It accounts for intricate interactions between shot particles and the treated material to predict plastic deformation and residual stress distribution. By integrating material properties, process parameters, and shot particle characteristics, Ansys LS-Dyna provides valuable insights into how shot peening influences the mechanical behavior of the targeted component under various peening conditions. However, due to the explicit nature of these highly deforming simulations, FEA solvers face computational constraints when simulating multiple shot particles. To overcome this limitation, a mesoscale approach is adopted using Ansys Rocky, a DEM solver. This approach enables the simulation of numerous shot particles, capturing their individual interactions and trajectories during the peening process [1]. As well the simulation tool provides detailed insights into high-fidelity collision event between the particles with different shapes and size distribution during a multi-pass peening process which has been shown to have significant effect on the process performance [2]. Moreover, a 1-way coupled computational fluid dynamics (CFD) is integrated using Ansys Fluent to simulate particle transport and achieve the desired impact velocity during shot peening. CFD-DEM coupling considers fluid-particle interactions such as aerodynamic drag, ensuring that particles reach the target surface with the intended velocity. This integrated approach offers a comprehensive understanding of the shot peening process, encompassing both mechanical interactions at the material surface and the fluid dynamics governing particle transport. Post-calibration and validation, the proposed simulation framework reduces costs, time, and increases flexibility by allowing the adjustment of various operating conditions. It also enables the examination of intricate details of the shot peening process, such as localized stress concentrations, deformation patterns, and particle interactions, which are challenging to capture using traditional experimental techniques. In conclusion, the generalized simulation framework utilizing a hybrid FEM-CFD-DEM solver presents an efficient approach to virtually investigate the effects of shot peening. [1] Majzoobi, G. H., Azizi, R., & Nia, A. A. (2005). A three-dimensional simulation of shot peening process using multiple shot impacts. Journal of Materials Processing Technology, 164, 1226-1234. [2] Agrawal, M., Haghnegahdar, A., & Bharadwaj, R. (2021, September). Improved Prediction of Sand Erosion by Accurate Particle Shape Representation in CFD-DEM Modelling. In SPE Annual Technical Conference and Exhibition? (p. D022S084R002). SPE.

An Interactive Decision Support Environment for Race Car Setup Selection using Predictive Lap Simulation Methods

Authored & Presented By
Mario Zarate Villazon (Aerospace Systems Design Laboratory)

Chaired By
Chair Michael Balchanos

The problem of predicting a racing car's lap time by finding the optimal trajectory across a racing track is key for improving vehicle performance towards a winning setup in motorsports. While drivers instinctively try to find this path with their driving style, engineers have been relying on mathematical methods and simulation-based techniques to estimate optimal race car trajectories in most closed-circuit motorsports. In most forms of racing, however, time is of the essence during race day operations, and fast decisions for race car setup configurations are key, especially considering the number of possible alternatives along with unique conditions and performance uncertainties. The use of physics-based modeling methods to enable predictive simulations for optimal trajectories on the racetrack effectively reduces the time required for analysis and evaluation of configuration alternatives against physical testing. Yet, very few solutions consider further integration with decision support tools and visualization of optimized vehicle setups in a virtual interactive environment. In response to this gap, this research presents an interactive and parametric decision support tool that enables decisions for race car setup in fast-paced situations through predictive simulation-enabled digital vehicle representations. A key module of this capability is enabled by the use of collocation algorithms for fastest lap optimization and is supported by Dymos, a state-of-the-art optimization tool. To ensure that this environment is interoperable with other simulation models and enabling solutions for integration and visualization, extensive use of the FMI Standard has been leveraged in a Python-based implementation environment. Considering the factors driving vehicle setup decisions, as well as visual ergonomics, the decision support environment is enabled by two different views, one for vehicle setup option selection and racetrack performance analysis. In contrast, the second view allows for virtual experimentation and vehicle setup assessments and tradeoffs. With the first view, the user can operate a dashboard by assigning different vehicle setups and obtain key performance indicators for a single lap to enable comparisons between different setups. The view also presents a track layout map with a heatmap of the test vehicle's velocity profile along calculated optimal paths and provides access to time series of other simulated performance variables. The second view allows for executing virtual experiments using physics-based simulations to construct data-driven Artificial Neural Network-based surrogate models to enable tradeoff assessment between various vehicle setup characteristics and performance metrics. Prioritization and ranking of vehicle setup alternatives allow for rapid decisions despite the high problem dimensionality and model uncertainty. This interactive environment is demonstrated through a Formula 1 vehicle use case example. Based on a standard vehicle dynamics model by De Buck & Martins and publicly available through the Dymos documentation, the baseline vehicle has been modified to match typical configuration parameters of a 2023 Formula 1 car. For validation purposes, the results from the F1 baseline vehicle simulation experiments of this model were compared against the public data available from Formula 1, and surrogate models were created based on notional Formula 1 car's setup ranges. These simulations support an integrated decision-support capability that combines physics-based simulation methods with rapid decision-making techniques for a race-day scenario. This capability could further enable the adoption of digital twins in a variety of industries where decision-making processes in high-stakes environments that require real-time decisions under uncertainties.

Validating real-time robotics models through high-fidelity simulation

Authored & Presented By
James Crist (EnginSoft USA)

Chaired By
Chair Michael Balchanos

Any autonomous robot system needs a digital multibody dynamics model of itself in the current environment to perform pathfinding and collision avoidance. Such a model needs to run in close to, or faster than, real-time to deal with a changing environment and updated sensor data. This poses a challenge for sophisticated models which may be highly accurate but lack the speed necessary to update predicted positions during time sensitive operations such as backflips which current autonomous robots are capable of. Instead, lower fidelity but extremely fast solvers are used. While onboard computers have increased in speed rapidly over the past few years these solvers are still not capable of solving complex non-linear effects such as the bending in an arm in real-time. By using a high fidelity model of the robot to evaluate the accuracy of the real-time solution designers can optimize the system. This presentation will show how understanding the conditions where the real-time solution isn't accurate can be used to place limits on controls systems to avoid these conditions. Furthermore, by having a model including effects such as contact and flexibility the stress and fatigue of parts can be evaluated to modify the design or place additional limits on the control system to extend service life. This approach allows designers to be very deliberate with trade-offs between structural rigidity and actuator responsiveness. In scenarios where a lightweight component is prioritized over a quick actuator limits may be placed on torque and acceleration limits to prevent the component from premature failure due to fatigue or flexing in a way that makes the end effector position unknown to the low-fidelity dynamics model.

Scenarios and Validation of Drill Bit Simulation in the Oil and Gas Industry

Authored & Presented By
Mahabub Alam (Ulterra Drilling Technologies)

Chaired By
Chair Michael Balchanos

A Cross-Society Collaboration Project, Mapping Consistency Confirmation Frameworks

Presented By
Alexander Karl (Rolls-Royce Corporation)

Authored By
Alexander Karl (Rolls-Royce Corporation)
Olivia Fischer (Georgia Institute of Technology)
Mathew French (Northrop Grumman Corporation)
John Matlik(Rolls-Royce Corporation)
Laura Pullum (The POM Group
LLC)
William Schindel (ICTT System Sciences)
Guodong Shao (NIST)
Nigel Taylor (MBDA Systems)

The computational modeling community has generated substantial literature and standards on model VVUQ, describing aspects of confidence in such models. Many such models play key roles in the engineering and life cycle management of large systems, involving processes in which many other artifacts are also present. These include product production, operation, and sustainment specifications, models other than computational models (e.g., MBSE and CAD/CAM models), datasets, and other types of artifacts. In addition to playing roles governing the life cycle of engineered products, some computational models become components of those engineered products. Across different disciplines involved in large programs and supply chains, there are substantial bodies of literature, standards, and practices concerning frameworks for the confirmation of consistencies (agreement, conformance, alignment) between certain of those different artifacts. Examples of such diverse "managed consistencies" and their related "confirmations" include (1) agreement between product specifications and physical test outcomes; (2) measures of agreement between a simulation and the phenomenon it simulates; (3) measures of agreement between the requirements for an engineered system's performance and a predictive simulation of that performance; (4) the agreement between a conceptual or mathematical model and an implemented computational model; (5) agreements between the specifications of an engineered system and the specifications of a computational model for simulation of that system; (6) agreement between the measured characteristics of production parts and design models of those parts; (7) agreement between the semantics of two different modeling systems; and (8) consistency of a model's intended context of use and plans for actual use of that model. We see some of the same terminology (e.g., "verification", "validation", etc.) across different groups and disciplines, but that terminology is sometimes used to describe seemingly different things in spite of using the same or similar terms. When a large supply chain or project includes several such disciplines, the integrated activity can imply needs for confidence measures that overlap and combine multiple consistency management frameworks. It is thus not sufficient for different groups to describe their work in isolation. Recognizing this, working groups of several technical societies (including AIAA, INCOSE, ASME, and NAFEMS) have agreed to "compare notes" in a structured way by collaborating to create a "Rosetta Stone" mapping that shows how the currently-published and currently-practiced versions of these consistency management frameworks connect or relate to each other, where they do. This presentation will summarize a limited-scope comparison that is currently underway, using a simple example comparing a few aspects of such current practices and standards from the ASME, AIAA, and NAFEMS Computational Model VVUQ community and the INCOSE Systems Engineering ISO 15288-based systems community. This work is focused on describing only consistency confirmation frameworks and only the current situation, but the resulting reference should later be helpful to others planning how to address its implications. The co-authors of this presentation include members of four society groups engaged in this comparison.

Automated Robustness Evaluation Process for Automotive Seat Structure Integrity and Occupant Injury using Design of Experiments

Authored & Presented By
Santosh Patil (BETA CAE Systems USA, Inc.)

Design Exploration Study of Vehicle Cab Floor Under Extreme Loading Conditions using Advanced Simulations

Presented By
Shawn Freeman (VIAS3D)

Authored By
Shawn Freeman (VIAS3D)
Akshay Dandekar
Celso Noronha (VIAS3D)
Kevin Reidy (Motiv Power Systems)

In the ever-evolving field of vehicle engineering, structural integrity has always been a crucial focus in the process of designing and validating a vehicle, making an important contribution to the functional design and configuration of cab floor, suspension system and other body components. By fully leveraging the material properties and employing geometric optimization, the components can be exploited to their full capability, ensuring the maximum performance. To achieve such a high degree of efficiency in the use of a component, it is imperative to thoroughly examine and verify the loads and forces experienced by these components. Hence, it is essential to identify the key loading spectrum for typical customer usage. This current work presents the significance of simulating and analyzing the vehicle cab floor, focusing on its response to extreme loading conditions encountered during real-world road travel. The study explores the behavior of the floor structure when subjected to various loadings, shedding light on the crucial role played by floor brackets. Additionally, the work investigates the torsional effects on the frame as it traverses diverse road conditions, emphasizing the significance of accurate simulation. Design exploration study is performed to identify the influence of key parameters of the design on structural response of the vehicle cab floor. The study identifies critical assumptions and boundary conditions that may adversely impact simulation accuracy under extreme loading conditions. Highlighting hotspots, the current study raises awareness regarding potential safety concerns and high-risk areas in the vehicle cab floor structure. Understanding the structural response under high loading conditions is vital for ensuring the safety and reliability of vehicles in real-world usage. By addressing various assumptions and emphasizing safety concerns associated with stress hotspots, the study contributes valuable insights to the field of structural analysis in automotive engineering. The findings are not only relevant for enhancing the performance and safety of vehicles but also offer valuable implications for future advancements in simulation methodologies applied to complex structural systems.

Integrated Modeling and Simulation for Data-Driven Product Design

Authored & Presented By
Manish Barlingay (Inceptra)

Chaired By
Chair Matt Sederberg

Simulation for everybody at all stages of the design

Authored & Presented By
Girish Mudigonda (Altair Engineering, Inc.)

Chaired By
Chair Matt Sederberg

Simulation has traditionally always been used for validation of the design but not to drive the design. Simulation-driven design process has several benefits including building a mature, optimized design at significantly lower costs. The primary reason this process is still not adopted widely is due to the limitation in the conventional finite element technology that wouldn't work at the speed of design. This method requires creating a meshed finite element model, which is time-consuming and expert intensive. Over the years, many vendors tried to implement solutions that automate several workflows, but the solutions aren't scalable and did not prove to be successful. To overcome these limitations, we need a new technology that is easy to use, fast to run and scales well with geometry complexity and the size of the assemblies. A new technology called SimSolid is based on the theory of external approximation that can perform simulation without creating a traditional mesh. Although the underlying theory is based on finite elements, there are several differences compared to the traditional finite element technology. In SimSolid, the discretization of the geometry can be performed using arbitrary shapes and the degrees of freedom are integrals over a volume thereby reducing the overall size of the simulation model. This gives the ability to run geometries that are large and complex in a fraction of time. Also, since the total number of degrees of freedom are small, the jobs can also be run on standard laptops without the need for expensive clusters or GPUs. With advent of this technology, simulation can be used from the very early stages of the design to drive design development. Any engineer with access to this technology should be able to get near instantaneous feedback on the design without requiring any expertise in finite element modeling and simulation. This also allows the possibilities to explore several design concepts and iterate with several designs early in the process to produce mature design faster.

Isogeometric analysis for rapid simulation of fully-featured CAD models

Authored & Presented By
Michael Scott (Coreform LLC)

Chaired By
Chair Matt Sederberg

In contrast to the laborious and error-prone process of translating computer-aided design (CAD) into computer-aided engineering (CAE) models, isogeometric analysis (IGA) performs the finite element analysis (FEA) simulation directly on smooth CAD geometry, using smooth spline basis functions. The idea of IGA was first introduced over 12 years ago by Dr. Thomas J.R. Hughes, and is now among the most active academic fields of FEA, with thousands of papers published and multiple international conferences. IGA has generated academic interest because it consistently performs faster and more accurately than FEA and is more robust. These benefits are especially pronounced when running simulations where high accuracy in the shape description is of value, such as contact or fatigue simulations. IGA promises more accuracy, not less, while providing new opportunities for higher efficiency. The biggest obstacle to the commercial adoption of IGA has been the lack of a suitably general and robust analysis-suitable geometry description. Coreform has developed a proprietary new spline format U-splines, which is being integrated with Cubit's meshing technology for a general commercial solution. This presentation will introduce a native spline preprocessor, Coreform Flex, and a native spline FEA solver,Coreform IGA, in workflows currently used by commercial customers.

Shifting Left: How Simulation for Designers Compliments Traditional Simulation To Achieve a Better Design Faster

Authored & Presented By
John Shew (MAYA Simulation Technologies Ltd)

Chaired By
Chair Barry Dietz

In engineering design, creating a new part involves understanding its purpose and how it fits into the bigger picture. Designers begin by laying out the functional and performance requirements, while still considering practical aspects like how it will be made. Once the initial design is drafted using CAD software, the next step traditionally involves passing it on to an analyst. These analysts specialize in conducting detailed simulations to test the design's performance under different conditions. When the analysis is completed, the analyst will often need to pass results back to the designer with suggestions for a few design improvements to, for example, help reduce stress in one area of the design or shift a natural frequency. Simulation has often been considered a task reserved for experts due to its complexity, and for detailed analysis that can still hold true. However, there's a growing recognition of the benefits of involving simulation tools earlier in the design process a shift-left approach. The perception exists that when designers use simulation tools themselves, the analysis provided might be oversimplified. That tends to assumes detailed analysis won't follow. We contend that empowering designers to use simulation tools to refine their designs iteratively, before they reach the analyst's desk, leads to better-performing products faster. In our demonstration, we'll walk through a practical example to illustrate how designers can leverage the latest simple, yet powerful, simulation tools to refine their designs. We'll cover steps like topology optimization, which helps identify the most efficient shape for a part, and parametric design optimization, which allows designers to tweak dimensions while considering factors like manufacturability. Throughout the process, we'll emphasize the importance of keeping things simple and practical, focusing on how simulation tools can be used to improve designs without adding unnecessary complexity. By embracing this shift-left approach and empowering designers to use simulation tools effectively, we can streamline the design process, catch potential issues earlier, and ultimately create better products.

Democratization of CAE - using colors to shift left

Authored & Presented By
Ramana Kappagantu (Siemens Digital Industries Software)

Chaired By
Chair Barry Dietz

Geometry based simulation requires discretization of geometry into finite elements or volumes. Though the geometry is 3-dimensional, some of those can be simplified into 0-, 1- or 2-dimensional elements. These components are connected through bolts, welds, glue, ... which translate to single- or multi-point constraint equations for the solver. Several industries have invested in establishing best practices for building models to address different engineering attributes. These rules can be further augmented with color coding to facilitate process automation. In this presentation, the authors start with a color-coded CAD model, e.g., of a brake corner assembly and show the automation of setting up a vibro-acoustic simulation process to study brake squeal. The concepts are extended to other applications such as electric-motor noise, panel transmission loss, dynamic stiffness of an automotive body-in-white, a conjugate heat transfer problem of PCB - some of which will be presented.

A Blend of Analysis. Every Discipline. One Product Development

Authored & Presented By
Robert Warren (GoEngineer)

Chaired By
Chair Barry Dietz

Product development from blank paper to prototype is a daunting task. Keeping the engineering team working together and making informed design decisions is even harder. Join us on a product development journey to learn how Analysis can help make design decisions in all fields of engineering. We will explore how to identify if the design will stand up to structural loads, whether fluids mix properly and if the electronics stay cool. Further, we will expand our project scope to better understand the manufacturability of the plastics components. With the Internet of Things becoming more and more prevalent in every device, why should your product be any different. Make sure your electronics can connect to WIFI using an array of tools that are seamlessly integrated and provide the most efficient workflows to support your engineering efforts. Advanced physics, cloud computing, cloud storage, and integrated collaboration help organize and boost efficiency in your team.

Building Bridge: Fostering Collaboration between Test Engineers and Analysts

Authored & Presented By
Erin Berry (Collins Aerospace)

I am proposing a presentation titled "Building Bridge: Fostering Collaboration between Test Engineers and Analysts" to address the call on the topic "Effective Collaboration between Simulation and Physical Testing". My presentation will bridge the simulation world into validation through testing. Often, the analyst behind the simulation or model has no hands-on test knowledge or is removed with a clear communication path to develop testing. The test engineer has no experience or limited experience in the modeling or simulation prior to the test article arriving in the test environment. Based on my unique background as a test engineer and analyst for Collins Aerospace, I can relate to both roles. However, the role highlights many classic issues during this process which can yield unclear results, misinterpreted results, costly testing, and over design. My presentation will break down the fallacies between the two roles and provide best practices to yield better results for both the test engineer and analyst. The best practices test method treats validation as a process with multiple input check ins and clearly defined roles based on solid communication practices. The process steps include a model review, statement of work, test readiness reviews, mutual instrumentation agreement, and the analyst previewing the data prior to test completion. Often, the process steps are classified as "Common Sense" but without formal gates are skipped by project management or not understanding the value. Several real-life examples from the aerospace industry will be used to guide the presentation. For instance, a comparison between pressure ripple modeled through CFD and the dynamics pressure ripple data on a positive displacement pump in lab environment. The difference in modal response from a FEA model compared to a vibration shaker test and deciding if the correlation is acceptable. Each simulation or model is limited by boundary conditions and each validation testing limited by test environment or budget. The process aligns expectations and roles to yield a mutual understanding and tailor testing with risk mitigation in mind.

Combining Physical Test with Structural FEA to Develop Package-Specific Failure Models for Electronic Components

Authored & Presented By
Tyler Ferris (Ansys, Inc.)

Printed circuit board assemblies (PCBA) typically consist of a series of off-the-shelf electronic components from a variety of manufacturers that are soldered to a single circuit board. The solder joints that connect the component bodies to the circuit boards are common failure locations in electronic assemblies, particularly in high shock/vibration environments (automotive, aerospace, portable electronics) and extreme power cycling conditions (power electronics, computing). The failure behavior for a particular component's solder joints is heavily influenced by the component package construction. The package dimensions and materials, the lead frame construction, and the die structure (in the case of active and discrete components) all play significant roles in determining when and how the component's solder joints will fail. Component-level failure models can be used in conjunction with simulation to predict load-specific failure; however, since these components are usually commercial, off-the-shelf produced by different manufacturers, it can be difficult to determine the internal structure and material properties of the package that influence failure behavior. Sometimes, the component manufacturers themselves do not even have a complete understanding of these parameters. Furthermore, even when a complete understanding of a component's structure is available, existing generic failure models may not predict a package's failure behavior as well as desired. Existing electronic package failure models and strain limits are typically derived empirically from physical test and extended for use on similar components. However, they do not always capture subtle structure changes, e.g., how a die attach material could change package compliance or how a specific solder joint shape might accelerate failures. For these reasons, it is desirable to create component-specific failure models and simulation strategies for common loading scenarios. This presentation will outline how to combine structural finite element analyses (FEA) with physical component testing to create failure models for specific electronic packages in mechanical shock and thermal cycling environments. Ball grid array (BGA) packages will be the focus of this workflow. The goal is to create both failure models and FEA strategies using a simplified test board that can then be referenced in future design strategies whenever the electronic component is being considered in a product design. The presentation will overview the key considerations for: - Choosing which components in a bill of materials are most appropriate for this type of analysis - Designing and implementing mechanical shock and thermal cycle testing that is relevant to potential environments - Combining simulations of physical testing with the test data to determine package-specific strain limit criteria and failure models - Proving those simulation strategies and failure models can be valuable tools in new product development and design strategies

Integrated Experimental and Numerical Approaches to Optimize Heat Blocking Efficiency in Intumescent Coatings.

Presented By
Taher Hafiz (Case Western Reserve University)

Authored By
Taher Hafiz (Case Western Reserve University)
Edrissa Gassama (Case Western Reserve University)

Intumescent coatings expand upon exposure to a flame to form a protective char layer between the flame and the underlying substrate. Historically, flame retardant polymers were made using different systems and components to help mitigate fire problems. Heat Blocking Efficiency (HBE) and intumescent coating performance have been studied for 1 mm, 0.5 mm, and 0.25 mm coatings of epoxy resin containing environmentally friendly ingredients such as tannic acid (TA) and ammonium polyphosphate (APP) for hot-rolled carbon steel (HRCS) fire protection using diffusive methane torch apparatus. An HRCS specimen (127 mm X 127 mm metal plate; is placed vertically and exposed to incident heat flux (15, 10, and 5 kW/m2). The model solved a (2D) heat conduction problem using the Finite-Element Analysis (FEA) method, and different thermal boundary conditions were specified, including thermal insulation and heat flux boundary conditions. The metal specimen's convection, conduction, and radiation heat transfer were modeled. The assumption is that heat transfer via exposed boundaries occurs in convection and radiation modes. It is described as non-linear irradiance, convective loss, and radiant absorption and emission from the metal substrate. The validation of the proposed numerical simulation was performed by comparison against in-situ Experimental results and by using an analytical solution for a simplified case obtained from a list of selected published benchmarks. The numerical results and the data generated experimentally showed a close agreement. This indicates that the numerical model has successfully replicated the real-world behavior of the studied system, and this is a highly desirable outcome in computational modeling. The results highlight that all intumescent coating thicknesses were able to delay the heat transfer through metal steel for up to 20 min and maintain the steel temperature below its structural integrity threshold, which ranges from 550 to 600°C. The lowest temperature recorded for the protected steel at 1-mm coating thickness was below 250°C, indicating that the HBE of the coatings reduced the temperature of the underlaying substrate

Advanced flexible body technologies for multi-body system simulation

Authored & Presented By
Sethu Subbarayalu (Dassault Systèmes SIMULIA Corp)

Capturing the flexible deformation in multi-body system (MBS) simulation is inevitable in order to retrieve authoritative data regarding key indicators about the mechanical systems under study, which routinely drives a multitude of major and minor design decisions across many industries. For small deformation analysis, it is state of the art since long to consider the elastic response based on linear reduced order models (ROM). This allows for sophisticated MBS analyses of highly dynamic scenarios, including, for example, multi-physics coupling via distributed surface and volume interactions, the reconstruction of internal stress for downstream fatigue analysis, and/or the reconstruction of acoustic emission. When the reduced finite element (FE) model contains sufficient data, the necessary computations for all these types of analysis are based on the state of the ROM with a small number of degrees-of-freedom only, which is orders of magnitude faster than resorting to FE analyses. But what, if the deformation is larger or nonlinear material behavior are involved such that linear ROMs are not sufficient to characterize the flexible response and its effect on the system? For such cases, a variety of approaches exists, and all come with their own advantages and disadvantages. This presentation will focus on two methodologies for capturing nonlinear flexibility that are readily available with commercial MBS analysis applications and that cover opposite requirements: Firstly, we demonstrate how nonlinear ROMs can be directly embedded into MBS with little more overhead compared to linear ROMs and how to take advantage of them for the simulation of new-generation wind turbines for load calculation. Key to the method is the observation that for technical reasons, many mechanical components are compliant with respect to one distinct deformation shape, for example, the flap-wise bending in the case of a wind turbine rotor blade, which allows to understand a nonlinear ROM as a natural enhancement of a linear ROM. This method serves to capture nonlinear flexibility with minimal cost in terms of computation times. Secondly, we show a robust technology for the co-simulation of strongly coupled MBS and FE models, which also allows for a convenient setup, particularly with regard to the MBS model, because of its support for the floating reference frame formulation. This can be extended to coupling multiple bodies to distinct portions of the same FE model that may feature, for example, highly nonlinear material behavior and/or nonlinear geometric effects, and that may interact with each other directly or indirectly through contact. Unlike nonlinear ROMs, co-simulation involves running a dynamic FE analysis. However, it proves to be a viable tool for case studies of how arbitrary nonlinear effects influence the behavior of highly complex mechanical systems.

FEA Simulation based Ride Index Evaluation of Rail Track Maintenance Equipment

Authored & Presented By
Dipak Patil (Harsco Rail)

The rolling stock ride index measures the quality of the ride experienced by passengers or cargo in the trains. A lower ride index indicates a smoother and more comfortable ride for passengers and less wear and tear on the rolling stock. Well-maintained, safe track contributes to a smoother ride, while well designed high-quality rolling stock can better withstand irregularities in the track, potentially reducing safety risks associated with poor track conditions. This paper evaluates the Ride Index of a self-propelled rail grind car maintenance equipment. While the primary focus for rail track maintenance equipment might be to perform rail track maintenance work rather than ensuring passenger comfort or carrying cargo, the concept of the Ride Index remains relevant. It impacts the structural integrity of the equipment, damage to the equipment mounted on it, operator comfort, efficiency, reliability, and overall safety of the equipment during work and travel on rail tracks. There have been numerous studies in the past few years on road vehicles, passenger, and freight train cars regarding ride quality, considering human comfort for vibrations and repetitive motions. However, similar studies on self-propelled track maintenance machines are limited. The ride index evaluation of the grind car equipment was conducted using an FEA-based modal superposition approach. The response accelerations and Ride Index were studied at the cabin floor and select critical points on the equipment floor. Predicted rigid body modes, such as Roll, Pitch, and Bounce, using normal mode analysis, were used in mode superposition analysis to predict the acceleration response and ride index for given excitations due to track defects. The damping parameter was varied and studied to optimize the structure for better ride quality. Results were compared for the equipment on different track gauges. The study primarily focused on the equipment's structure, suspension, and damping parameters with the goal of ensuring an acceptable Ride Index for both structural integrity and a smoother ride.

Seismic Certification of BESS Using Response Spectrum Analysis

Authored & Presented By
Rejean Belliveau (Optimec Consultants Inc)

A Connected, Performance-driven Electric Drive Development Process

Presented By
Young-Chang Cho (Dassault Systemes)

Chaired By
Chair Peter Perivolarakis

Authored By
Young-Chang Cho (Dassault Systemes)
Djamel Guezgouz (Dassault Systemes)

E-mobility relies on electric machines powered by power converters transforming electrical energy to mechanical energy. Their designs are driven by a wide range of performance, efficiency and durability targets, all in harsh environments in terms of temperature, humidity, vibration, and EMI. Despite the long history of electric machines and their relatively simpler anatomy, the performance targets in their traction applications are highly inter-dependent across multiple physics and domains, opening a new dimension of challenges. The automotive field is very competitive and the product shall be on the market at the right time with usually a very short turnaround time at the minimum cost, making the design experience very challenging for the suppliers. The OEM's have to deal with integration challenge of the power converter or device to optimize the whole system in the car and guarantee the expected performances. The simulation represents a real opportunity to address all the technical topics at the early design stage when the cost of change is low. It also answers the question "what if"without need of prototype and give an output that are not even accessible by measurement. The simulation does not aim to replace test but it lowers the cost of investigation and troubleshooting tool compared to experimental methods. We propose a powerful simulation tool and clear workflow to design and study the digital version of the components and system through a digital 3D experience. In this webinar we demonstrate our power electronics engineering workflows using the study of the insulated-gate bipolar transistor (IGBT) module that is widely used for inverter and charger design. It will emphasize the adding value of EMC/EMI, thermal, system and multiphysics simulations with 3DEXPERIENCE Platform. In our electric drive engineering workflows we highlight how the CAD-CAE connectivity can be leveraged to explore design potentials in early design stages and reuse proven engineering knowledge. In particular, the application of a modular, fully-parametric electric drive model to multiphysics, multifidelity analysis is discussed.

High-Fidelity Physics-Based Electromagnetics Simulation for Advanced Driver Assistance Systems

Authored & Presented By
Ushe Chipengo (Ansys, Inc.)

Chaired By
Chair Peter Perivolarakis

Autonomy and electrification have emerged as key drivers of innovation and growth in the automotive industry in recent years. Various advanced driver assistance systems (ADAS) have been developed as OEMs race towards a fully autonomous and zero-emissions future. Radar-based ADAS such as adaptive cruise control (ACC), automatic emergency braking (AEB), blind spot detection (BSD), among others are currently being implemented in todays vehicle. Connectivity related ADAS such as vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) using designated short-range communication (DSRC) and ultrawideband (UWB) technologies are poised to become ubiquitous in coming years. Before advanced driver assistance systems are deployed, they need to go through rigorous testing and validation. While measurement is valuable, simulation has emerged as a practical, cost effective and safer approach of testing the robustness of these systems, especially in corner cases. Here, we will present the application of an asymptotic high frequency electromagnetics solver in various automotive radar and connectivity related applications. Specifically, we will investigate the impact of vehicular obstructions, multipath effects, and non-line-of-sight (NLOS) conditions on 5.9 GHz DSRC links. Simulation results will be compared with measurement results for these corner case studies. Automotive radar simulations of a 77 GHz frequency modulated continuous waveform (FMCW) sensor in a full-scale, dynamic environment will be presented to show the extraction of synthetic radar returns from a 512-channel sensor for 4D radar. Accuracy of the synthetic radar returns will be evaluated by overlaying the processed detection scatter plot on the original full scale traffic scene. Recent research efforts have suggested micro-Doppler as a means for classification of vulnerable road users for autonomous vehicles. Specifically, variations in the micro-Doppler response of actors (pedestrian, cyclist, cars, animals etc.) in road traffic scenarios lead to distinct differences in their respective spectrogram images that can be exploited for target classification using machine learning (ML). Finally, we will present a simulation workflow for obtaining large volumes of spectrogram images to train a 5-layer convolutional neural network (CNN) that will classify four types of road actors with accuracy nearing 100%.

NVH Analysis of EV Propulsion Systems - Electromagnetic Computation Case Study

Presented By
Sumeet Singh (EMWorks Inc.)

Chaired By
Chair Peter Perivolarakis

Authored By
Sumeet Singh (EMWorks Inc.)
Mario J. Felice (VirsolTech Engineering Consulting
LLC)
Rabah Hadjit (ESI Group)
Vince Solferino (MÜLLER-BBM VibroAkustik Systeme Inc.)

This presentation illustrates the analysis steps necessary to achieve noise, vibration, and harshness (NVH) objectives in electric vehicle propulsion systems using a multi-physics approach consisting of electromagnetic simulation (EMWorks software) and NVH response computation (ESI software). Through virtual modelling and simulation case studies on a Tesla-type motor model, we present techniques for predicting and mitigating issues related to rotor eccentricity, rotor imbalance, and rotor magnet movement. Initially, we import a Tesla-type motor model into the EMWorks software and calculate its baseline electromagnetic performance, incorporating operating torque, stator core losses, winding losses, and radial tooth magnetic forces. Subsequently, we introduce rotor geometric eccentricity into the model to compare the impacts of tooth forces versus the baseline design. Following that, we integrate the motor with the rest of the propulsion system to represent the entire system assembly. As we now have an assembly model in place, a design sensitivity study is performed to evaluate critical dimensional parameters affecting the rotor eccentricity and its effect on NVH. Using this virtual prototype, we simulate rotor imbalance by intentionally altering the mass distribution. We highlight the predicted changes in radial forces, resonance frequencies, and noise radiation resulting from the imbalanced condition. In the final phase, we vary the position of the rotor magnets within the model and re-simulate with the full EV propulsion system. We compare vibration modes, deformation, and stress distribution between the baseline design and the magnet-shifted design. The presented results underscore how simulation can be leveraged to identify and resolve issues related to magnet positioning and retention early in the design process. Furthermore, evaluation studies are conducted for rotor imbalance and rotor magnet positioning with respect to the best NVH. In summary, this presentation guides through the application of EMWorks and ESI simulation software to model and evaluate an EV propulsion system's error states and demonstrates effective methods to predict and mitigate NVH issues.

Leveraging Adjoint and Design Exploration Methods for Fluids and Thermal Design Optimization: A Workflow Comparison

Authored & Presented By
Christian Windisch (Siemens Digital Industries Software)

This presentation will provide an overview of selected design optimization methods for Computational Fluid Dynamics (CFD) and Conjugate Heat Transfer (CHT) simulations, based on the available models in the Siemens Simcenter and Xcelerator portfolio. The goal is to provide an overview of each method from the perspective of an industrial simulation or design engineer who wants to explore the possibilities and improve a design. Liquid fluid flow through an S-bend geometry will be analyzed as an example. The objective is to improve the pressure drop by creating or modifying the geometry. The first method focuses on an adjoint topology optimization method. This will be used to generate the S-bend geometry from scratch, by only providing the inlet and outlet positions and the available design space that can be occupied by the part. The topology optimization generates the geometry by placing solid material in those parts of the design space that do not contribute to the reduction of the pressure drop. The second approach is based on providing an initial S-bend geometry and morphing the geometry surface based on the analysis of surface sensitivities. The surface sensitivities are generated with an Adjoint method and represent the required surface deformation to improve the pressure drop. The third method uses a parameterized geometry and deforms the surface based on a parametric design exploration. The Sherpa design exploration algorithm is used to identify a parameter combination that results in an improved pressure drop.

Optimization of Bearing Lubrication in Large-Scale Wind Energy Gearboxes with Smoothed Particle Hydrodynamics (SPH)

Authored & Presented By
Yannick D’Arino (dive solutions GmbH)

ROM Technique for attenuating Structural Vibration & Weight Optimization at BIW level

Presented By
Rajasekaran Mohan (Detroit Engineered Products)

Authored By
Rajasekaran Mohan (Detroit Engineered Products)
PRAVEEN KUMAR SAMPATH (DEP India Pvt. Ltd)

In an intensely competitive market, a primary hurdle for automotive engineer is the simultaneous reduction of product cost and enhancement of performance. Focusing on vehicle comfort, it becomes imperative to address low-cost and low-weight criteria from the outset of the Body in White (BIW) development cycle. Another challenge confronting CAE engineer is the mass of the BIW, comprising roughly one-quarter of an automobile's total mass. Therefore, minimizing the overall mass of the BIW in the early design stages is crucial, ensuring it meets required modal and stiffness targets. The objective of BIW stiffness analysis is to evaluate the impact of static loads encountered in day-to-day vehicle operations. In this paper, the BIW model undergoes discretization into a 1D structure, facilitated by the advanced technology of DEP MeshWorks - Reduced Order Modelling (ROM). An impressive 80-90% correlation is observed between the 1D and 3D Finite Element models. The intricate conversion from a complex 3D model to beams is seamlessly automated through the ROM approach. The CAE team can leverage auto-parameterization tools and advanced optimization tools within ROMs entities to create compelling concept models without compromising stiffness performance. The primary factors influencing the rigidity of the Body in White (BIW) are predominantly concentrated at joint locations. Therefore, understanding the contribution of each joint to the overall stiffness is crucial for enhancing the BIW's rigidity. Joint stiffness significantly guides the contribution of the release engineer in achieving the desired stiffness. Section parameterization is conducted on the 1D beam model through a Design of Experiments (DOE) study, followed by Optimization using Response Surface Methodology (RSM). This process is faster and guides us towards reaching the optimum cross-section of each member of the BIW model. Optimized design results comparatively decrease the overall CAE design cycle and weight reduction by 30-40 %. Furthermore, the decrease in mass and an increase in stiffness enhances the modal frequency of BIW. This paper focuses on executing the Reduced Order Model (ROM) procedure in the BIW model, with plans to implement the ROM strategy on the trimmed body in white in the near future.

Engineering Data Science Working Group: Hear from the Experts: Status and Future of AI/ML for CAE

Authored & Presented By
Fatma Kocer (Altair Engineering, Inc.)

Engineering Data Science Working Group Discussion Panel

Authored & Presented By
Vladimir Balabanov (The Boeing Company)

Simulation Driven Design into the Appliances Product Development Process

Authored & Presented By
Julio Anaya (Controladora Mabe S.A. de C.V.)

Chaired By
Chair Matt Ladzinski

A typical appliance development process starts with the generation of multiple design concepts. These design concepts are developed by designers and are evaluated in terms of safety, quality, appearance, and cost. After this evaluation, one design concept is selected for further development. Once a design concept has been reached and the 3D model is completed, the virtual validation stage begins using a finite element method simulation. This analysis is performed by a CAE analyst; in the case of a structural analysis, it is usually a system-level simulation. At this point, it is common to find that changes are required in several components, and an iterative process begins until the structural requirements are reached, then, the system finite element analysis can become a bottleneck for projects. This work deals with the activities carried out to promote the use of simulations made by designers, focused on the stages where designers generate alternatives and iterate with different geometries and materials using CAD software. The objective of this work is to reduce the iteration number in system-level simulation due to more mature parts being simulated during the complete model validation. There were defined different levels for simulations: part, sub-assembly, and system-level simulation. There were also defined responsibilities according to the designer's position in the organization. In this paper, Simulation Driven Design means a part-level simulation carried out for a Design Engineer during the design concept selection stage, supported by a CAE specialist, and involves functional and manufacturability feasibility simulations. A key aspect of this initiative is the use of designer-focused CAE Software, and the training plan needed for these tools. There was selected Altair SimSolid software for structural simulation and Altair Inspire software, for mold and forming process simulations. This paper shows the training plan mentioned, the results expected for a part-level simulation, both functional and manufacturability feasibility, and five study cases results, where it is possible to see the advantages in time and precision that these CAE tools provide to the appliance development process.

Using GenAI to Accelerate Engineering Simulations for Product Design

Authored & Presented By
Jonathan Kyle (Amazon Web Services (Seattle))

Chaired By
Chair Matt Ladzinski