Authored & Presented By Uma Maheshwar (BEC - GE Aerospace)
DescriptionAt GE Aerospace, we are investing in and progressing the technology building blocks that will define the future of flight. The CFM Revolutionary Innovation for Sustainable Engines (RISE) technology development and demonstration program with our partner Safran is a great example. With new technologies like Open Fan engine architecture, we are driving a generational step-change to accelerate the industry’s decarbonization of commercial flight, targeting at least 20% improved fuel efficiency and 20% lower CO2 emissions by the mid-2030s compared to today’s commercial engines. At GE Aerospace, we are supporting efforts to accelerate the uptake of alternative fuels and collaborate across the industry with the goal of making the future of flight smarter and more efficient. We are building on the spirit of invention that has fueled us for over a century to help propel the industry’s goal of achieving net zero carbon emissions by 2050. New technology maturation includes advanced engine architectures like Open Fan, compact core, and hybrid electric systems. We are advancing Fuel flexibility for 100% Sustainable Aviation Fuel (SAF) compatibility and hydrogen combustion.The maturation of next generation propulsion technologies and products present tremendous opportunities and challenges to advance the state of art of simulation technologies. The presentation covers the context of aviation industry, the next generation technologies to power the future of flight and the challenges & opportunities in simulation technologies to advance the future flight.
Authored & Presented By Sita Rameswara Sarma Akella (Mahindra & Mahindra Ltd.)
DescriptionThe demand of AI /ML applications in Engineering simulations of any product development, like automotive, Aerospace has been on the rise, particularly in the concept development phase of any ‘ALL NEW” kind of programs. Simulation plays a significant role in designing and developing a new vehicle through a well-established gateway process. In the case of Automotive Product Development, deriving an efficient design space which is satisfying all the conditions of vehicle architecture includes packaging and attribute performance targets (Aerodynamics, HVAC, Durability, Safety, NVH and Ride & Handling) is the key activity of concept development. The current challenge of any Auto OEM is especially, the product development timelines, how quickly introduce new product to the market, reducing the total development time both in simulation and physical vehicle development. This particular keynote is to highlight on all the learnings of conventional product development processes and introduce a hybrid kind of process, leveraging AI/ML Technology in speeding up the concept product development and also minimizing the development time in the detailed engineering phase. This talk focusses on proposing a novel approach of AI enabled design process, integration of generative AI for concept creation and quick attribute prediction using predictive AI models.
11:00
Presented By Sharad Anand Shivaprasad (Collins Aerospace)
Authored By Sharad Anand Shivaprasad (Collins Aerospace)Sushree Kshirabdhi Tanaya (Collins Aerospace)
AbstractThis paper explores the application of AI in Aerospace Engineering ,Engineering, focusing on the challenges of analytical derivation, making it cost-effective and less time-consuming, especially during the preliminary design . It covers the novel idea of developing neural network-based algorithms that impact the multi-disciplinary area of aerospace engineering, including structural analysis and fluid dynamics.We review the state of the art, gather the advantages and challenges of AI/ML methods across different aerospace disciplines, and provide our view on future opportunities. The basic concepts and the most relevant strategies for AI/ML are presented together with the most pertinent applications in aerospace engineering, revealing that AI/ML is improving aircraft performance, and these techniques will have a significant impact shortly. As some design parameters cannot be analytically predicted swiftly due to the complex and diverse failure modes involved, which can also be affected by manufacturing processes, it becomes necessary to always depend on tests and simulations for every laminate configuration that may have to be considered for optimization of the performance for a given set of loading and boundary conditions.In order to overcome this inability, a AI-based approach that relies on a Neural Network based Artificial Intelligence(AI) algorithm is proposed for empirically predicting thickness distribution(any design parameter, thickness is considered as example design parameter in this paper) across the structure which will be trained on the database of pressure distribution (Loads) vs thickness distribution (Design Parameter) across defined structure for various prior successful designs. The network can be viewed as a variation of a Radial Basis Function Neural Network with a Gaussian Basis Function and a one-pass learning algorithm, making it highly efficient computationally. The Neural Network algorithm explored in the present study appears more robust in reliably predicting swiftly the approximate values of any design parameter across any component of the aircraft.
Presented By Saurabh Deshpande (The Automotive Research Association of India (ARAI))
Authored By Saurabh Deshpande (The Automotive Research Association of India (ARAI))Mahesh Patwardhan (The Automotive Research Association (ARAI)) Rahul Mahajan (The Automotive Research Association (ARAI))
AbstractAs the automotive industry transitions toward more complex and multi-domain systems, the role of Computer-Aided Engineering (CAE) has evolved from supporting design validation to enabling front-loading of innovation for various applications. The presentation will provide a comprehensive overview of emerging trends and persistent challenges in automotive CAE, spanning key domains such as safety, durability, NVH, CFD, electric vehicles (EVs), ADAS and manufacturing simulations. It further highlights the increasing complexity of simulation due to non-linear behaviors, multi-physics coupling, integration of new-generation materials and human body models. Challenges such as correlation reliability, high computational demands and the scarcity of skilled personnel are explored. Emphasis is placed on the strategic use of process automation, Artificial Intelligence/Machine Learning (AI/ML) and Integrated Computational Materials Engineering (ICME) to address these challenges. The presentation concludes by positioning CAE as a catalyst for reduced development cycles and smarter, safer vehicles, while underscoring its continued dependence on physical validation.
Presented By Sandeep Santhanagopal (TE Connectivity)
Authored By Sandeep Santhanagopal (TE Connectivity)Sandeep Santhanagopal (TE Connectivity)
AbstractThis abstract outlines the simulation-driven development of a high-endurance electromechanical relay designed to meet Aero-MIL qualification standards, emphasizing how advanced multiphysics modelling and data-driven optimization enabled significant performance improvements while maintaining SWaP (Size, Weight, and Power) constraints.Accelerated Product Development Through Simulation:Traditional design methods followed a linear path, relying on design freeze, prototyping, and final qualification testing. This process, though effective, was time-consuming and often resulted in a trial-and-error loop. A novel approach now integrates advanced simulations early on, creating a virtual design loop between concept refinement and detailed design. This reduces reliance on physical testing, accelerates development by about 50%, and improves design accuracy.Novel Multi-Physics Simulation Strategy:Traditionally, relay design was manual and iterative, optimizing the contact system through static analysis and refining coil and magnet parameters via trial and error. The force-vs-stroke curve was derived solely from physical testing, making the process slow and prototype-dependent. Today, a comprehensive multi-physics simulation strategy integrates the contact system and electromagnetic performance into a unified model. Using ANSYS Mechanical Workbench, the contact system is optimized, while Maxwell Workbench assesses coil and magnet performance. These simulations are linked, enabling the creation of an accurate force-vs-stroke curve prior to prototype testing.This integrated approach led to a 28% increase in contact gap and a 17.6% increase in minimum contact force, alongside a 54% increase in contact area. The improvements also enhanced the contact system's performance by increasing A-spots, reducing contact resistance, and improving electrical performance. When prototypes were tested, the simulations demonstrated 90% accuracy. Using ANSYS optiSLang for Design of Experiments (DoE), these parameters were fine-tuned early in the design process, accelerating development and minimizing physical prototyping.This simulation-led approach enabled rapid iterations and successful qualification to Aero-MIL standards, showcasing the power of integrated simulation tools in optimizing high-reliability electromechanical systems.
11:20
Presented By Swapnil Nanaware (Stellantis)
Authored By Swapnil Nanaware (Stellantis)Sreenath Mallela (Stellantis) Darshan Pawargi (Stellantis) Ebrahim Saifee (Srellantis)
AbstractPredicting Head Injury Criterion (HIC) values during pedestrian protection assessments can significantly streamline CAE analysis. This paper explores various approaches, including machine learning models trained on prior simulation data, automated FEA setups, interpolation techniques for HIC mapping, material optimization, and simplified physics-based models for quick estimates. These methods enhance design efficiency while reducing analysis time.Key parameters affecting HIC values were identified and used to train machine learning models across different vehicle architectures and head form impactor locations. The best-fit algorithms were validated using Odyssee CAE software. The robustness of the ML model was ensured through diverse data set combinations. The model was then applied to predict HIC values using unknown data sets, highlighting effective application areas. The study emphasizes the importance of selecting appropriate training and prediction data for optimal model performance. The generated ML model can further assess parameter sensitivity and optimize design in minutes compared to the traditional CAE approach which take hours.
Presented By Aurobbindo Lingegowda (Bosch Global Software Technologies Private Limited)
Authored By Aurobbindo Lingegowda (Bosch Global Software Technologies Private Limited)Vaclav Kocourek (Robert Bosch GmbH) Dibakar Mahalanabish (Bosch Global Software Technologies Private Limited) Besher Baradi Mohamed (Robert Bosch GmbH) Lisa Schildwaechter (Robert Bosch GmbH)
AbstractThe increasing trust on Modeling and Simulation (M&S) in engineering processes has accelerated the shift toward virtualization in system development, integration, optimization and validation. With this transition, the credibility of simulation-based results has become essential, particularly as these results increasingly inform critical system design and release decisions. To address this, a structured approach for assessing and ensuring the credibility of M&S activities is needed, especially in the context of virtual testing.This work presents the application of the Credibility Assessment Framework developed by Robert Bosch (RB), designed to support consistent, scalable, and cross-domain evaluation of simulation credibility. Building upon previous work introduced at the NAFEMS World Congress 2023, this paper demonstrates how the framework enables systematic integration of credibility assessment into virtual testing processes. The framework consists of defined phases including engineering task description, decision consequence evaluation, M&S risk identification, and structured credibility assessment. These phases ultimately support simulation-informed engineering decision-making with traceability and transparency.A central element of the framework along with M&S guideline is the updated Credibility Wheel, which embeds Verification, Validation, and Uncertainty Quantification (VVUQ) activities, while maintaining alignment with external standards such as NASA & ISO. A vehicle model integrating cross domain subsystems with coordinated interactions is used to perform Emergency Braking maneuvers and ensure safe stopping distance with traffic participants.In conclusion, the framework facilitates a standardized and adaptable approach for establishing confidence in simulation outcomes used in virtual testing. It supports the broader goal of embedding M&S credibility into the organizational culture and development lifecycle, promoting informed, risk-aware engineering decisions across diverse applications.
Presented By Avijit Chakraborty (Indira Gandhi Centre for Atomic Research, Kalpakkam)
Authored By Avijit Chakraborty (Indira Gandhi Centre for Atomic Research, Kalpakkam)Prashant Sharma (Indira Gandhi Centre for Atomic Research) Sarat Kumar Dash (Indira Gandhi Centre for Atomic Research) G. Vijayakumar (Indira Gandhi Centre for Atomic Research) B.K. Sreedhar (Indira Gandhi Centre for Atomic Research)
AbstractFast nuclear reactors use liquid metals, such as liquid sodium, as coolants. To circulate the coolant through auxiliary circuits, electromagnetic pumps (EM pumps), which operate under the influence of magnetic field, are commonly employed. The most widely used EM pump is Annular Linear Induction Pump (ALIP). Performance of an ALIP is assessed based on various characteristics, including pressure versus flow rate and input power versus flow rate. To predict the performance of an ALIP across a wide range of operating conditions, several computational models such as electrical equivalent circuit models and two-dimensional numerical models have been reported in the literature. However, a more accurate analysis of an ALIP requires solving both the fluid dynamics and electromagnetic equations to account for the interaction between the fluid and magnetic field. This computation is highly complex and challenging; as a result, many of the reported numerical models have relied on simplifying assumptions. To further improve the performance prediction, a three-dimensional finite element method (FEM) based computational model was implemented in the multiphysics software COMSOL for a small ALIP with a nominal flow rate of 5 m³/h. This model incorporates the pump’s asymmetric components, considers the interaction between fluid sodium and the magnetic field, and utilizes a voltage source for excitation. The model was validated against experimental data obtained from testing the pump in a sodium loop. Possible reasons for any observed deviations have also been explained.
11:40
Presented By Mayank Kapoor (Mobis India Limited)
Authored By Mayank Kapoor (Mobis India Limited)Ashok Anumalla (Mobis India Limited)
AbstractThe organization and naming of components in chassis subframes, often comprising over 20-25 parts, is a time-consuming and error-prone process when handled manually. Each part must adhere to a specific naming format incorporating details such as part name, material type, and thickness. A machine learning (ML)-based approach offers a promising solution to automate this process, leveraging algorithms to classify and organize components efficiently. This proof-of-concept (POC) algorithm for MacPherson-type subframes utilizes Python and TCL scripts for automation. The PythonOCC module is employed to read and interpret the geometry of the components. The extracted geometric data is then broken down into a set of features, which serve as input for ML models to efficiently classify the components. This automated system significantly reduces the time and effort required for organizing subframe components, while eliminating human error. It ensures consistent classification and naming without relying on specialized engineering expertise. With advanced scripts and ML algorithms, the workflow becomes both faster and more accurate, enhancing productivity and reliability. In addition to improving subframe component organization, this approach demonstrates the broader potential of automation in complex assemblies, paving the way for its application across various engineering domains. With its focus on efficiency, precision, and accessibility, this innovation redefines the standards of component management.
Presented By Durga Prasad (TRANSVALOR)
Authored By Durga Prasad (TRANSVALOR)Satyajeet Kulkarni (Transvalor India)
AbstractThe manufacturing industry has always aimed to reduce the number of steps in the manufacturing chain and to improve the final parts’ properties.Chemical composition and microstructure evolution throughout the whole manufacturing chain is an important factor in the metal forming industry. Process simulation technology allows engineers to predict the unpredictable and helps to understand the process and increase the quality requirements of the components.It helps to visualize the flow of molten metal behavior during liquid state – liquid to solid state and during solid state. The engineer can create a model of the whole process and understand the phenomena occurring during the processes in a mold and help the manufacturers in optimizing their product design, reducing material consumption, and improving the quality of the final product.To match the increasing quality requirements for the mechanical parts, a better understanding of the effect of the manufacturing processes on metal composition and microstructure evolution is needed.As the final mechanical and metallurgical properties are most important, quantitatively predicting the impact of alloy composition and process conditions on manufacturing process and evaluation of structural analysis during thermochemical process and helps to meet stringent requirements of the customer.In our technical paper, we are going to focus on: Importance and advantages of simulation for metal manufacturing processes, current trends and the new advancements in simulation technology.
Presented By Sandeep Roy (Altair)
Authored By Sandeep Roy (Altair)Abinash Baruah (Altair) Farhad Behafarid (Altair)
AbstractOver the past decade, electrification has transformed numerous industries, offering substantial benefits while introducing complex new challenges. Among these, thermal management has emerged as a critical concern, as thermal issues remain the number one cause of failures in electronic systems, often resulting in costly consequences. Design teams frequently provide rough estimates of component heat generation to thermal engineers. However, as thermal challenges intensify, more accurate methods for identifying and quantifying heat sources are essential. This is particularly true in power modules, where power loss in a component is not constant and depends on the temperature of the component. Both overestimation—leading to over-engineered and inefficient solutions—and underestimation—risking system failure—are no longer acceptable in modern design practices.High-fidelity simulations offer a cost-effective and efficient solution for the thermal management of power electronic components and systems. This paper presents a novel methodology in which conduction and switching losses of MOSFETs are calculated using a one-dimensional simulation tool (Altair PSIM). These loss values are dynamically linked to a Computational Fluid Dynamics (CFD) and thermal analysis tool (Altair SimLab – Electronics Thermal) to accurately predict temperature rise. The results from this fully coupled simulation approach are validated against experimental data, showing strong correlation and demonstrating the effectiveness of the method.
13:00
Authored & Presented By Arjun Dev (MOBIS INDIA LIMITED)
AbstractThis paper presents an AI-driven approach for airbag deployment using image processing along with algorithm. Collecting airbag images and then image data is processed to extract key features, which are then fed into a machine learning model trained. Rapid training and validation of models using MLP(multilayer perceptron) with radial basis functions , Kriging, and neural networks, allowing for quick and accurate prediction. Simulation output data from LS-DYNA is used to generate training datasets. The integration of image-based feature extraction method and integration of AI/ML algorithm with LS-DYNA simulation results in a hybrid system that significantly reduces computational time while maintaining accuracy.
Presented By Bhanu Pratap Reddy (Stryker Global Technology Centre)
Authored By Bhanu Pratap Reddy (Stryker Global Technology Centre)Pranshu Rajput (Stryker Global Technology Centre)
AbstractShape Memory Alloys (SMAs), particularly Nitinol, are widely used in medical implants due to their superelastic and shape memory effects. Accurate simulation of their nonlinear behavior is critical for predicting device performance and ensuring reliability. However, determining a complete and reliable set of material parameters is often challenging, especially when direct experimental data is limited or unavailable.This study presents a methodology to identify suitable material parameters for Nitinol using optimization-based curve fitting. A representative stress-strain curve from literature served as the target response for this reverse engineering approach. A one element model was used to simulate the material response based on a constitutive model for SMAs. A sensitivity analysis was performed to understand the influence of key variables such as transformation stresses, elastic moduli, and transformation strain.An optimization loop was developed using a gradient-based algorithm to iteratively adjust these parameters and minimize the error between simulated and target stress-strain curves. The final parameter set closely matched the reference data and was subsequently validated in implant-level simulations.This approach enables accurate modeling of Nitinol behavior without requiring full-scale experimental characterization, supporting simulation-led product development. It also offers a transferable workflow for modeling similar smart materials in early design stages where test data may be incomplete.
Authored & Presented By Advait Tikle (TE Connectivity)
AbstractTraditional busbar design methodologies, developed for low-frequency electrical systems (50–400 Hz), are increasingly obsolete for next-generation aerospace applications operating at 1500–4000 Hz. These legacy approaches fail to account for high-frequency electromagnetic effects—specifically skin and proximity phenomena—that significantly increase AC resistance, power loss, and thermal hotspots, impacting system safety and efficiency.This study introduces a frequency-aware, electro-thermal co-simulation methodology that integrates electromagnetic loss prediction with thermal modeling through a feedback-driven, parametric design framework. Electrical resistance is dynamically updated using temperature-dependent material properties, enabling accurate prediction of Joule heating and temperature rise under high-frequency, high-current operation. The method captures the nonlinear interaction between frequency, geometry, and thermal behavior, eliminating the need for repeated physical prototyping.A comprehensive design study involving 128 cases was conducted to evaluate how key parameters—such as conductor dimensions, material type, frequency, current, and ambient conditions—influence thermal performance. Regression analysis was applied to derive predictive equations for temperature rise, which were implemented in a user-accessible design sheet. This enables rapid, first-pass validation of copper and aluminum busbar designs without requiring detailed simulations or thermal testing.Validation against experimental results showed high correlation, with less than 3% error in predicted temperatures. The methodology reduced design effort by 85% and simulation time by over 94%, offering a scalable, cost-effective solution for high-frequency electrical system design.This innovation supports faster design cycles, enhances design accuracy, and improves accessibility for both engineers and non-specialists. It is highly applicable to aerospace, rail, automotive, and electrified mobility sectors where high-frequency electrical distribution systems demand efficient thermal management and rapid validation workflows.
13:20
Presented By Vidit Sharma (ESTECO India Software PVT. LTD)
Authored By Vidit Sharma (ESTECO India Software PVT. LTD)Anuj Shukla (ESTECO India Software PVT. LTD) Eshan Amalnerkar (ESTECO India Software PVT. LTD)
AbstractThis study introduces a computationally efficient Reduced Order Modeling (ROM) approach for optimizing the BIW of a car, with the objective of reducing maximum steering wheel velocities in frequency response simulations. High-fidelity finite element simulations are often computationally expensive, particularly when exploring multiple design variables. To mitigate this, Proper Orthogonal Decomposition with Interpolation (PODI) was utilized to reduce the dimensionality of simulation data obtained from NASTRAN solver, where frequency response analysis was performed for varying shell thicknesses of 16 key BIW components. A ROM was constructed enabling accurate predictions of maximum steering wheel velocities across different design configurations. ROM allowed for the efficient handling of reduced data, ensuring that the model retained critical physical attributes while significantly lowering computational costs. Furthermore, machine learning techniques, particularly Radial Basis Function (RBF) networks, were integrated with the ROM to accelerate performance predictions across the design space. In addition, a Multi-Objective Robust Design Optimization (MORDO) framework was implemented to address uncertainties in design variables and operating conditions, ensuring that the optimized designs were both high-performing and resilient under real-world conditions. The ROM, enhanced with RBF networks, was coupled with HYBRID, a multi-strategy optimization algorithm, to efficiently minimize maximum steering velocities within the design space. The integration of ROM technology, RBF networks, and optimization strategies in modeFRONTIER resulted in significant reductions in peak steering wheel velocities. This approach highlights the effectiveness of combining ROM technology with machine learning and optimization for industrial design applications where computational efficiency and accuracy are critical.
Presented By Vinoth Ammasi (Keysight Technologies India Private Limited)
Authored By Vinoth Ammasi (Keysight Technologies India Private Limited)Sridhar Rajagopalan (Keysight Technologies India Private Limited)
AbstractInconel 718 alloy is commonly employed in aero-engine components, including compressor casings, gas turbine blades, and discs, where high-temperature tensile, creep, and stress rupture properties are critical requirements. The operating temperature of the Inconel 718 is about 700°C. However, it is susceptible to metallurgical issues during welding, such as consequential Laves phase formation in the fusion zone and micro-fissuring in the HAZ due to the development of eutectic phases and carbides. This will severely deteriorate the service performance and cause premature failure of welded parts. Prior understanding of metallurgical issues in the weldments at service conditions would facilitate the safe design of the welded parts in the design stage. The Finite Element Method (FEM) helps to evaluate the thermo-metallurgical-mechanical behavior of the welded parts at service conditions and delivers process optimization and weld quality improvement.In this paper, the TIG welding process and post-weld heat treatment of Inconel 718 were performed by the FEM. The thermal field, microstructure, residual stress, and mechanical properties of the Inconel 718 weldment at room and service temperature conditions were predicted using SYSWELD software. The thermal field as a function of weld process parameters and PWHT condition were obtained. The solid-state phase transformation and weld-CCT diagram of Inconel 718 is implemented in the software that were used to predict microstructural changes in the weldment. The residual stress and mechanical properties were obtained from the thermo-elastic-plastic and transformation strains theory and von Mises yield stress criteria. The predicted results, including weldment geometry, microstructural changes, and residual stresses at room and service temperature conditions, were validated with experimental findings and display good agreement with numerical results. This study emphasizes the potency of numerical simulation in the prediction of service condition weldment microstructural issues of superalloys in the design stage to ensure the weldments response to service conditions.
Presented By Pooja G (TE connectivity)
Authored By Pooja G (TE connectivity)Sandeep Santhanagopal (TE Connectivity)
AbstractContactors are electrically controlled switching devices widely used in industrial and power systems to manage electrical loads ranging from a few kilowatts to several megawatts. These devices are predominantly actuated electromagnetically, with the actuator assembly comprising key components such as coils, moving contacts, and return springs. A crucial advancement in modern contactor design is integration of economizers, which are electronic control circuits that reduce coil power consumption during the holding phase after initial actuation. This not only enhances energy efficiency but also improves thermal performance and extends the mechanical and electrical lifespan of the device. Traditional simulation methodologies often rely solely on electromagnetic analysis tools such as Ansys Maxwell, supplemented by manual calculations which does not capture the circuit dynamics. However, these approaches are limited in their ability to accurately capture transient phenomena introduced by the economizer circuit, particularly during switching events. In this work, a co-simulation methodology is proposed using Ansys Maxwell for electromagnetic field simulation and Ansys Simplorer for system-level modeling of electrical and mechanical subsystems. Maxwell is used to compute spatially resolved magnetic fields, eddy currents, and coil behavior based on 3D actuator geometry. Simplorer simultaneously models the electrical characteristics of the economizer and the mechanical dynamics including mass-inertia systems, spring-damper elements, and motion constraints. Consequently, the methodology provides superior accuracy around 0.09%, compared to traditional decoupled analyses. Experimental testing of operate and release characteristics is inherently limited due to the high-speed motion of the armature, requiring sophisticated high-speed cameras to observe physical displacement. More critically, precise estimation of armature flight time during release is essential for predicting and mitigating contact arcing. The proposed co-simulation framework provides a powerful design and diagnostic tool that overcomes these limitations, yielding improved accuracy, better insight into transient behavior, and significant reduction in time and cost associated with iterative prototyping.
13:40
Presented By Arul Ganesan (Detroit Engineered Products (DEP))
Authored By Arul Ganesan (Detroit Engineered Products (DEP))Shubham Verma (Detroit Engineering Products (DEP))
AbstractThis work explores the use of Conditional Generative Adversarial Networks (cGANs) for structural design generation, focusing on automotive Body-in-White (BIW) stick models. The goal is to generate feasible geometry representations conditioned on target mechanical properties—specifically bending and torsional stiffness. The BIW geometry is voxelized to transform the structural data into a grid-based format suitable for deep learning. The generator within the cGAN is trained to synthesize voxelized BIW geometries based on the provided stiffness parameters, while the discriminator evaluates their structural plausibility. This method enables rapid exploration of design alternatives that meet specified performance criteria, potentially accelerating the early stages of vehicle architecture development. The approach demonstrates the capability of cGANs to capture complex structural dependencies and encode functional relationships between geometry and performance. Future enhancements could include incorporating additional structural attributes, increasing voxel resolution for finer detail, or integrating performance validation feedback into the training loop for more accurate generation.
Presented By Soumik Ghatak (Ansys Inc.)
Authored By Soumik Ghatak (Ansys Inc.)Zeng Zhang (Ansys Inc.)
AbstractElectric 2W OEM customer is facing performance issues where fluid-structure interaction and cavitation in shock absorber fluid are likely playing crucial role due to intricate design. Accurate prediction of suspension system performance is critical for ensuring vehicle stability, comfort, and durability under dynamic loading conditions. One of the key performance-limiting phenomenon is Cavitation, which is an undesirable phenomenon that affects the essential functioning of the Suspension system i.e. Loss of Damping Performance, Noise and Vibration which leads to Damage and wear. CFD-structural 2-way coupling of solvers is challenging along with difficulty of dynamic meshing in thin gaps for traditional CFD solvers.This simple piston cylinder proof-of-concept work introduces a different compressible fluid solver which is called Conservation Element/Solution Element (CESE) to model a cavitating fluid with FSI. A special equation of state based phase change solver is used to incorporate cavitation in the fluid. In this work, we shall outline factors in selecting CESE solver and cover the various modelling aspects and challenges. The solution helps user to estimate force exerted by fluid on thin structures during each stroke of the shock absorber and hence they can optimize the material parameter of the structure.
14:00
Presented By Sohan Rao (Altair)
Authored By Sohan Rao (Altair)B Veda Vamsi Krishna (Altair) Sohan Sundaresh Rao (Altair) Hari Krishna Reddy M (Altair)
AbstractHead impact simulations are computationally intensive and represent a major bottleneck in the iterative design and optimization of automotive hoods for pedestrian safety. To address this challenge, a data-driven surrogate modelling approach was developed to significantly accelerate the simulation process. High-fidelity finite element simulation data from multiple head impact locations was used to train the surrogate model. Three surrogate models were developed, the performance was compared, and the best model was considered for the next step. The model was designed to predict key outputs such as displacements and accelerations curves at various impact points. From the predicted acceleration curves, the Head Injury Criterion (HIC), a critical metric in pedestrian safety was computed. The selected trained surrogate model achieved a prediction accuracy of greater than 90%. Once trained, the model was able to generate predictions in seconds, compared to 65 minutes typically required by traditional solvers. Structural Optimization was performed with the objective of minimizing HIC values by exploring design variables such as sheet thickness and reinforcement geometry. By replacing the traditional solver with the surrogate model in the optimization loop, a substantial reduction in computational cost and design cycle time was achieved. This AI-augmented workflow facilitates rapid design space exploration and supports the development of safer, pedestrian-friendly vehicle front-end structures, demonstrating the transformative potential of surrogate modelling in simulation-driven design.
Authored & Presented By Vinay Kumar (MOBIS India Limited)
AbstractAs we know the critical role of Battery thermal management system in Electric vehicles (EVs) and Hybrid electric vehicles (HEVs). It is also recognized that the batteries (Li-Ion) are highly sensitive to temperature fluctuations, requiring operation within a tightly defined thermal range to maintain optimal performance. However, often in peak load demand battery temperatures exceeds its safe operating limits. While existing cooling systems are employed to Lessen these risks, they often lack the precision and efficacy required to manage thermal loads uniformly across battery cells, particularly in extreme climates and peak load, which undermines battery performance and raises significant safety concerns.This paper introduces a unique solution to mitigate battery thermal issues for HEVs. The forced air-cooling thermal management system is developed where two Inlet air duct configuration have been used, first duct is configured to supply cabin air to the battery pack and the second duct is configured to supply cooled air from the HVAC (Heating ventilation & air conditioning) unit to the battery pack and a blower unit is installed at the outlet side of the duct which suck the air from both inlet ducts through the battery pack. This research work emphasizes primary methodologies, including building a 3D virtual prototype of BTMS (Battery thermal management system) and performing detailed CFD simulations to enable iterative testing and refinement of the system. To demonstrate the positive impact of two inlet duct configuration with HVAC air flow, following parameters have been analyzed such as heat dissipation and temperature distribution across the battery pack; Impact of positive air pressure at HVAC-inlet duct on blower efficiency; Air mass flow ratio at the inlet ducts and optimization of the ducts to increase the heat dissipation, mass flow rate and minimize the pressure drop across the battery pack.
14:45
Presented By Celal Karadogan (DYNAmore – an ANSYS Company)
Authored By Celal Karadogan (DYNAmore – an ANSYS Company)Shyam Vasu (Ansys)
AbstractArtificial Intelligence (AI) and Machine Learning (ML) are increasingly transforming Computer-Aided Engineering (CAE) workflows by enabling faster design iterations and reducing computational costs. Ansys SimAI is a deep learning-based surrogate modelling platform designed to replicate complex physics-based simulations with significantly reduced runtimes. This paper presents the application of SimAI in modelling an automotive side impact scenario, using physics-based high-fidelity data generated from LS-DYNA simulations. The study begins with the development of an AI model trained on a dataset where the pole impact position and door beam configurations are systematically varied. The trained model demonstrates strong predictive capabilities, accurately capturing vehicle deformation patterns and curves of pole force progress across a range of input configurations. In a second case, a separate model is trained using simulations with fixed pole and door beam settings, but varying rocker panel reinforcements. Again, the AI model shows excellent generalization, accurately predicting results for unseen reinforcement geometries. To expand the model’s applicability, both datasets are combined to train a comprehensive surrogate capable of simultaneously accounting for variations in pole position, door beam configuration, and rocker reinforcement design. This integrated model successfully predicts the deformed shape and force-time histories for new design combinations within the training space, showcasing SimAI’s ability to generalize across a multi-dimensional design space. The results demonstrate that expensive and time-consuming LS-DYNA simulations can be leveraged effectively to build AI models that dramatically reduce the time required for design exploration. In addition, SimAI models are valuable tools for continuous engineering development because they can be trained further with new data, allowing for iterative improvement and adaptability. This approach not only accelerates product development cycles but also opens up opportunities for more comprehensive design optimization using legacy and newly generated CAE data.
Presented By Bharath P T (Borgwarner)
Authored By Bharath P T (Borgwarner)Vipin Das (Borgwarner) Ted Zeunik (Borgwarner)
AbstractElectric Vehicle (EV) inverters are essential components in the powertrain of electric vehicles, converting direct current (DC) from the battery into alternating current (AC) to power the electric motor. The seals within EV inverters are critical for maintaining system integrity, preventing contamination, and ensuring the proper functioning of inverter components. This study focuses on the performance of High Voltage Direct Current (HVDC) Press-In-Place (PIP) seals made from Ethylene Propylene Diene Monomer (EPDM) rubber.The research investigates the mechanical performance of EPDM PIP seals under two material conditions: Least Material Condition (LMC), Nominal Material Condition (NMC) at temperatures of 22°C, -40°C, and 150°C. Finite Element Analysis (FEA) using Ansys software, incorporating Plane Strain approach & quasi static method to simulate the complex behaviour of the seals.The primary objective is to evaluate the integrity and leakage resistance of the PIP seals, using a Neo-Hookean Hyperelastic material model with stress relaxation modelled using Prony Series model obtained from the supplier test data. Results indicate that the PIP seals loses a minimum contact pressure of 1 MPa during -40C due to stress relaxation and also due to temperature reduction. For both LMC & NMC case the maximum equivalent total strain occurs at 150C and the minimum equivalent total strain occurs at -40C. For LMC it is 25.4% at 150C and 14.3% at -40C. The maximum equivalent total strain observed in the NMC & LMC condition is significantly below the failure limit of 255% for the elastomer material. The maximum force required to compress the seal in LMC condition at -40C is 1.42N/mm & at 150C it is 1.52N/mm. This research provides valuable insights into the design and optimization of EPDM seals for enhanced durability and performance in EV inverters.Keywords: EPDM, Hyper elastic, Plane Strain, Prony series, Stress relaxation, PIP seals, Neo-Hookean, EV Inverters, FEA.
Presented By Jagdish Goswami (Stryker Global Technology center)
Authored By Jagdish Goswami (Stryker Global Technology center)Venkateswaran Perumal (Stryker Global Technology Center) Chettiar Ramanathan (Stryker Global Technology Center)
AbstractThis paper presents a Model-Based Systems Engineering (MBSE) approach to the design and validation of a robotic arm. The simulation model serves as a proof of concept, demonstrating how system-level modeling can be used to capture functional requirements, simulate behavior, and guide engineering decisions early in the design cycle.The robotic arm model can execute user-defined trajectories—either input manually or extracted from historical data—to replicate precise cutting paths using a blade mounted on the end-effector. The system dynamically adapts to changes in the trajectory input, enabling customizable incision shapes tailored to patient needs. Through the MBSE methodology, we systematically analyzed joint-level dynamics, allowing accurate estimation of torque and power requirements across the arm’s degrees of freedom. These insights directly inform motor selection and control strategy design.Additionally, the model predicts resistive forces such as friction and back forces. The system responds to these forces by adjusting joint torques in real-time, maintaining smooth and accurate motion. Simulation results validate the model’s ability to track complex trajectories with minimal error and provide quantitative data on actuator sizing and force compensation. This work demonstrates the effectiveness of MBSE in robotic systems functional modeling.
15:05
Presented By B Veda Vamsi Krishna (Altair)
Authored By B Veda Vamsi Krishna (Altair)Hari Krishna Reddy M (Altair) B Veda Vamsi Krishna (Altair) Kamikkiya P (Altair) Akash Yella (IIT Tirupati) Sriram Sundar (IIT Tirupati)
AbstractReal-time brake monitoring system is one of the important aspects in the context of vehicle safety and performance. Conventional brake monitoring systems depend on a multitude of sensors leading to high integration costs, increased system complexity and maintenance challenges especially in real-world operating conditions. This work addresses these challenges by taking a practical approach to brake monitoring, minimizing sensor dependency and utilizing power of machine learning. A controlled disc brake setup was developed to demonstrate the concept. Instead of relying on numerous sensors, the system recorded only the angular velocity of the brake in real time. The acquired real-time data was further processed to extract segments of velocity signals specifically associated with braking events automatically. From these segments, statistical features were computed, which exhibited a strong correlation with the target performance metrics. These features served as inputs to an artificial neural network (ANN) model, which was trained to estimate critical parameters such as contact forces and brake torque quantities that were typically difficult to measure under real-world operating conditions. A validated multibody dynamics (MBD) model was used for ANN training, achieving an accuracy of greater than 90%. The approach enables real-time brake monitoring that is scalable, cost-effective, and suitable for manufacturers, operators, and service providers, reducing the need for extensive hardware and maintenance.
Presented By Ravi Putrevu (BIETC Boeing India Engineering & Technology Center)
Authored By Ravi Putrevu (BIETC Boeing India Engineering & Technology Center)Saketh Behara (Boeing India Engineering & Technology Center) Anirudh Srikanth Vasist (BIETC Boeing India Engineering & Technology Center) Biju Balan Laly (BIETC Boeing India Engineering & Technology Center)
AbstractWith the higher usage of composite materials and higher rates in the production of aerospace structural components, the possibilities of manufacturing defects also increase. One of such industry-wide common defects is porosity within the laminate structures. Due to the uncertainties involved in the prediction of failure loads within a reasonable degree of doubt and due to the limited amount of simulation or analytical data, these parts get rejected even though there may be inherent conservativeness in the analytical approach. The other common alternative is extensive testing to arrive at different failure strengths and moduli with varying levels of porosity. However, this is not only expensive due to the detailed test regime for such certification tests, but the data is limited to only a few points. So again, an analyst must resort to conservative assumptions when left to deal with a decision between safety and engineering judgement. There is a need therefore to be able to come up with simulation models that
Presented By Ajay Naiknaware (Whirlpool Corporation)
Authored By Ajay Naiknaware (Whirlpool Corporation)Avinash Chandra (Whirlpool Corporation) Siddhi Marathe (Whirlpool Corporation)
AbstractWhirlpool Corporation is a leading American multinational manufacturer of home appliances, headquartered in Benton Harbor, Michigan. Founded in 1911, it owns globally recognized brands such as Whirlpool, KitchenAid, Maytag, and Amana, and operates in more than 170 countries. Its product portfolio includes a wide range of home and commercial appliances.For consumer products, cost and performance of appliances are vital parameters. System-level simulation predicts emergent behavior in terms of the cost and performance of the product. The famous “V-Model” of system engineering starts with stakeholder analysis to provide requirements for architectural concepts. This multidisciplinary optimization model can be obtained using system-level simulation, which provides key inputs for prototype manufacturing in the concept stage. The outcome of system-level simulation includes system integration, performance management, verification and validation, and life cycle management. The system-level model further disintegrated into the subsystem level model and component-level model. Verification and validation must be done at the component level, sub-subsystem level, and system level. This study investigates a horizontal axis washer-dryer combination, focusing on the integration of 1D simulation and 3D flow analysis. 3D flow analysis using ANSYS-Fluent for optimizing air management, enhancing clothes interaction, reducing bypass, and improving flow paths. A 1D simulation with Dymola is used for system performance parameter calculation. Various component and subsystem-level models are integrated, and a workflow in Mode Frontier is developed to perform optimization studies, aiming to predict energy savings by applying constraints to design parameters. Appliance performance is assessed by evaluating the trade-off between Remaining Moisture Content (RMC) and energy consumption under full-load and half-load conditions. The study demonstrates a 24% reduction in energy consumption for full load, a 27% reduction for half load, and a 3% improvement in RMC, highlighting the importance of system-level integration for optimizing energy efficiency and achieving new energy label (NEL) trade-offs.
15:25
Presented By Anit Jain (Dassault Systemes)
Authored By Anit Jain (Dassault Systemes)Jayant Pawar (Dassault Systemes) Girish Patil (Dassault Systemes) Swapnil Shete (Dassault Systemes)
AbstractModern vehicles face a growing array of requirements driven by regulatory standards, consumer demands, and competitive pressures. Concept Structure Engineering (CSE), enabled by the 3DEXPERIENCE platform, facilitates the creation of numerous design alternatives for components and larger automotive systems, such as such as body and battery structures, which are subject to critical load cases, including safety tests. These load cases interact with objectives like cost and mass, complicating the sequential resolution of interconnected design challenges and hindering OEMs' accelerated time-to-market strategies. A promising approach to overcome these challenges involves transforming concept modeling into a comprehensive, vehicle-wide optimization task, which requires an evolving suite of technological advancements and iterative plan-do-study-act cycles.Key technologies for this vision include parametric CAD modeling, process automation, advanced finite element meshing, scenario-based simulation, high-performance computing integration, automated post-processing, and data analytics, seamlessly supported by the 3DEXPERIENCE platform. The integration of machine learning (ML) and artificial intelligence (AI) enhances these capabilities, enabling rapid simulation and predictive insights. A shared data model and embedded communication tools streamline process automation and improve team-based issue tracking.This paper presents initial minimum viable products (MVPs) using CSE on the 3DEXPERIENCE platform with implicit associative modeling, allowing for consistent assembly generation of complex systems like Battery Pack. Leveraging visual scripting and parametric modeling, specific cases such as optimizing Battery Pack under side pole impact are highlighted. ML models play a critical role in accelerating the assessment of design alternatives, identifying structural reinforcements that achieve an optimal balance between mass, cost, and regulatory compliance. Future developments will aim to extend these methods to multi-physics, multi-load case scenarios, offsetting the increased simulation demands with predictive efficiencies. By leveraging historical finite element models and machine learning models, OEMs can enhance adaptability, swiftly address evolving safety standards, and drive data-driven, efficient development cycles.
Presented By Mohammed Ibrahim Kittur (GMT Gummi-Metall-Technik GmbH)
Authored By Mohammed Ibrahim Kittur (GMT Gummi-Metall-Technik GmbH)Joshua Marlin (GMT Gummi-Metall-Technik GmbH) Chinna Narsugonda Peddola (GMT Gummi-Metall-Technik GmbH)
AbstractThe rail vehicle systems deploy rubber components to ensure vibration isolation, noise reduction, and durability under dynamic loading. The response of rubber material to the dynamic loading plays a key role to ensure the optimum performance of rail suspension systems. The finite element simulation accuracy which leads to design optimization depends on the selection of material models. This paper presents a comprehensive approach to material model selection, focusing on the integration of hyperelastic and viscoelastic (both linear and non-linear) models in the finite element analysis (FEA) of anti-vibration components used in rail applications.The study evaluates commonly used hyperelastic models—such as Neo-Hookean, Mooney-Rivlin, Odgen, and Arruda-Boyce models alongside time-dependent viscoelastic models to simulate real-time loading scenarios. Both linear and non-linear viscoelastic formulations are considered to assess their impact on the predictive accuracy of rubber behaviour under various loading conditions. The simulation results highlight how the selection of linear and non-linear viscoelastic modelling approach influences the stiffness and damping characteristics of rail suspension components. This investigation provides an outline to selection and implementation of material models within a design optimization loop. Concisely, this study bridges the gap between material model selection and performance requirements that leads to design of cost-effective and reliable virtual prototypes in rubber components in rail suspension systems.
Presented By Richie Garg (Schneider Electric)
Authored By Richie Garg (Schneider Electric)Divakara Rao (Schneider Electric) Akhilesh Rao Borra (Schneider Electric) Santhosh Kumar Vijayan (Schneider Electric)
AbstractRack Power Distribution Unit (rPDU) supplies critical power to servers in data centres reliably. It optimizes usage of energy and prevents overload. It can easily be scaled up and facilitates remote access to maintain uptime. Understanding thermal trends and behaviour is very important when it comes to the product functionality and safety. Predicting the critical temperatures of the product by using the one-dimensional (1D) model is simple, cost-effective, and saves the time as it runs in about a minute on a standard computer compared to the conventional computational fluid dynamics method requiring high computational resources. Testing is conducted on 1U (unit) rPDU equipped with twelve circuit breakers (CBs). The objective is to compare the measured CB temperature in test with 1D modelling. AC source and resistive load banks are used to power the CBs and to mimic the load in real applications. Thermocouples are used to measure the steady state temperature of the CBs and the ambient air of rPDU. Identical conditions are replicated in the 1D thermal model developed using MATLAB Simscape. The CBs are modelled taking care of radiative and convective heat transfer for thermal mass of body as well as air present inside the rPDU. The cables, compression lugs, terminals, and screws of CBs are considered in detail. Thermal and electric current paths are defined and solved. Predicted temperatures by simulation varied by 8.7 °C from 208 V test and 16.9 °C from 239 V test results. These preliminary simulations can be improved by detailing CB inner construction and sensitivity analysis of CB resistance. The 1D model provides extra information like temperature of air inside the CBs, temperature of the rPDU components, which are not measured in test.
16:10
Authored & Presented By Dinesh Lonkar (Atmus Filtration Technologies Inc.)
AbstractAtmus filtration Technologies is a global leader in filtering solutions for commercial vehicles, off-road vehicles, and equipment. Spin-on filters are one of the most extensively utilized products. Spin on filters are made up of mainly 5 to 6 parts and their design can vary depending on customer requirement and applications. Based on the need, different configurations of these filters are produced using appropriate part sizes and geometries. For each configuration, a laboratory test is required to determine its structural integrity. This task is time-consuming and creates a bottleneck in the time to market principle. To address this issue and provide quick solutions simulation-based tool was developed. Detailed simulations were performed for various configurations, designs, and test specification variables. Response curve optimization was developed and validated to ensure a good correlation between this tool and lab tests.This tool allows the product team to understand filter frequency, stresses in critical parts, ,and the design margin. The product team can easily determine the best configuration for spin on filters to meet the customer applications requirement. This tool is leveraged to reduce the time and cost involved with lab testing.
Presented By Mohit Kumar (Stryker Global Technology Center)
Authored By Mohit Kumar (Stryker Global Technology Center)Devesh Bhatia (Stryker Global Technology Center) Lakshmanan Chettiar (Stryker Global Technology Centre) Sreekanth Padmanabhan (Stryker Global Technology Center) Venkateswaran Perumal (Stryker Global Technology Center)
AbstractAccurate measurement and prediction of strain distribution in tensile test specimens is critical for good correlation in experimental testing and FEA result. Current workflow explores a comprehensive methodology to compare strain distributions obtained from finite element (FE) simulations with those measured using Digital Image Correlation (DIC) in uniaxial tensile tests. A flat dumbbell tensile specimen as per ASTM E8/E8M standard made of SS 304 material was modeled using Elastic-plastic nonlinear FE material model. Experimental test was conducted using a high-resolution 3D DIC system, full-field strain distribution was mapped across the specimen gauge length. The load-displacement curves align well, local strain measurement near high stress concentrations regions can show significant differences if FE model is not tuned properly. Current work discusses the qualitative Vs quantitative strain data measurement in FEA and DIC output along with full field strain distribution and error map prediction. The result comparison highlights the influence of factors such as geometry coordinates alignment, strain localization, necking behavior, and boundary effects. The influence of DIC spatial resolution and pixel size, FE model mesh sensitivity, strain averaging techniques is discussed on the result correlation. This work provides better Insights on integration of DIC data and simulation workflows, resulting in more robust model validation and improved result correlation.
Authored & Presented By Sridhar K (Schneider electric private limited)
AbstractIn the rapidly evolving landscape of digital transformation, the integration of future-oriented lab data with Digital Twins presents a significant advancement in predictive modeling and real-time system optimization. This paper explores the methodologies for optimizing lab data to meet future demands, ensuring its relevance and utility in enhancing Digital Twin technologies. By leveraging advanced data analytics, machine learning, and IoT integration, we demonstrate how future-ready lab data can empower Digital Twins to provide more accurate simulations, predictive maintenance, and improved decision-making processes. The synergy between optimized lab data and Digital Twins not only enhances operational efficiency but also drives innovation across various industries. This study highlights the critical role of data integrity, real-time data processing, and cross-disciplinary collaboration in achieving a seamless integration that propels Digital Twin capabilities to new heights. Furthermore, it emphasizes the importance of adopting a holistic approach that encompasses technological advancements, strategic planning, and continuous improvement to fully realize the potential of Digital Twins in transforming industry practices and outcomes. By addressing these key aspects, we aim to provide a comprehensive framework for leveraging future-oriented lab data to empower Digital Twins, ultimately contributing to the advancement of digital transformation initiatives across diverse sectors.
16:30
Presented By Kuralanban Ramu (BIPL)
Authored By Kuralanban Ramu (BIPL)Ramkumar Selvarajan (BIPL)
AbstractTension fittings frequently used in structural members are subjected to additional tensile forces due to the contact effect which induces extra loads in attachments like tension clips and other fittings, exceeding the original tensile load. This is caused by eccentric loading within the fitting, resulting in a heel-toe effect. Classical analytical methods are available to assess contact effects in simple tension fitting geometries.In this study, finite element analysis (FEA) model of a typical metallic tension fitting subjected to tensile loading is developed using both linear and nonlinear contact elements. These contact elements are employed to simulate the contact effect within the connections. Various contact element types are investigated to accurately represent contact behaviour under simple tension fitting geometries and loading conditions. The FEA results are then validated by comparison with classical hand calculations, confirming the reliability of the simplified model. This modelling approach can be extended to more complex geometries and loading scenarios to effectively simulate interaction effect due to contact.
Presented By Chetan Mahawar (Ansys Software Pvt. Ltd.)
Authored By Chetan Mahawar (Ansys Software Pvt. Ltd.)Chetan Patil (Ansys Software Pvt. Ltd.) Dilipkumar Damera (Ansys Software Pvt. Ltd.)
AbstractDiffusion is a fundamental phenomenon that plays a critical role across a wide range of industries such as environmental science, healthcare, consumer goods, automotive, aerospace, and heavy industries. In most practical applications these happens in interactions with structures leading to complex interactions such as stress-assisted diffusion, driven by internal gas pressures and cyclic mechanical loading on the enclosing structures. One of the most prominent emerging areas of interest is the structural diffusion of hydrogen, spurred by its clean, versatile nature and growing adoption across transportation, power generation, industrial processes, and energy storage sectors. Its also because of hydrogen’s unique properties, to be sourced sustainably and stored over long periods — position it as a cornerstone of the global transition towards a low-carbon economy. However, hydrogen diffusion within metallic structures can lead to significant material degradation, reducing ductility and durability through mechanisms such as Hydrogen Enhanced Localized Plasticity (HELP) and Hydrogen Enhanced Decohesion (HEDE). Understanding these degradation phenomena is vital for designing robust components for future hydrogen-based technologies. Ansys Mechanical offer powerful capabilities to model stand-alone and stress-assisted diffusion, enabling virtual exploration of diffusion-driven behaviour’s such as gas concentration gradients, diffusion fluxes and diffusion-induced strains. This virtualization can also provide crucial insights into changes in material properties caused by the entrapment of gases within interstitial and substitutional sites in a structure's microstructure. Thus, this paper will focus on simulation of hydrogen structural diffusion in metals and a theoretical review of different material degradation mechanisms such as HELP, HEDE which influences material properties change over time. As part of future work, the scope will be extended to simulate and study the embrittlement processes associated with void initiation, growth, and coalescence under the influence of hydrogen diffusion.
Presented By Dheeraj Kapse (CNH Industrial Technology Services India Pvt Ltd)
Authored By Dheeraj Kapse (CNH Industrial Technology Services India Pvt Ltd)Raffaele Caggiano (CNH Industrial Italia SPA) Stefano Largo (CNH Industrial Italia SPA) Alessio Serracca (CNH Industrial Italia SPA)
AbstractThermoforming is a widely used manufacturing process for shaping plastic sheets into various 3-Dimensional products. Ensuring uniform thickness distribution is crucial to achieve high-quality end products with consistent mechanical properties. In this research, we present a comprehensive investigation of thermoforming simulation techniques and their application to study thickness variation during the process.The primary objective of this study is to develop a reliable and efficient thermoforming simulation model capable of accurately predicting thickness variations in the formed parts. We begin by reviewing the existing literature on thermoforming. Next, we collaboratively build a simulation approach that considers various process parameters: material properties, and tooling conditions to simulate the thermoforming process more accurately.To validate the proposed simulation model, actual thermoforming products are 3D scanned. The 3D scan measured the thickness at various regions of the part.Comparisons between the simulation results and 3D scan results are then performed by averaging method to assess the precision, accuracy, and reliability of the developed simulation model. The study evaluates the thickness variation which guides designers and engineers to make informed decisions during the product development stage.Overall, this research aims to contribute significantly to the understanding of thermoforming simulations for thickness variation, paving the way for enhanced product quality, reduced material waste, and increased manufacturing efficiency. The findings from this investigation serve as a valuable resource for design engineers seeking to improve their thermoforming processes and ultimately advance the state-of-the-art in plastic manufacturing technology.Keywords: Thermoforming, Plastic, Manufacturing Simulation, Precision
16:50
Authored & Presented By Kapil Adsule (Finite To Infinite, Pune, India)
AbstractFinite element analyses (FE analyses) with linear-elastic material behavior enable efficient and reliable computations while simplifying the interpretation of results due to their reduced mathematical complexity. Additionally, they provide a robust foundation for subsequent investigations; for instance, durability assessments often rely on scaled and superimposed FE stresses. However, when these linear stress advantages are utilized, local stresses may exceed the local yield limit. In such scenarios, approximation methods are employed. This presentation introduces an innovative new incremental PLAST approach now available in FEMFAT software. These approaches consider the differential equation of the non-linear kinematic hardening model for the 1D case. By utilizing C-Gamma parameters optimized by FEMFAT during the fatigue analysis for the determination of back stresses, the linear equivalent stress history is thoroughly rearranged prior to Rainflow counting.The method showcases high precision when dealing with stress concentrations around sharp notches and local plastification. A comprehensive comparison between the PLAST method and other FEM software will be presented, focusing on accuracy and computational efficiency. One of the key advancements brought forth by the PLAST method is the ability to consider the exact load sequence influence on the fatigue behavior. This capability allows for a more accurate representation of real-life scenarios, enhancing the reliability and precision of the fatigue analysis.To demonstrate the practical application of the new PLAST method, a case study will be presented involving a rotor plate of an electric motor, a crucial component in automotive manufacturing. Through this application example, attendees will witness how the PLAST method can provide valuable insights into the elastoplastic stress behavior of automotive components. The case study will highlight the method's ability to predict fatigue life accurately, even under complex loading conditions. This presentation aims to provide a thorough understanding of the PLAST method's capabilities and its significant impact on enhancing the accuracy and efficiency of fatigue analysis in engineering applications.
Presented By Gaurav Chauhan (Gamma Technologies)
Authored By Gaurav Chauhan (Gamma Technologies)Roshan Kumar (Whirlpool) Ankur Kumar (Whirlpool) Pratik Kokate (Whirlpool) Venkatesh Dasari (Gamma Technologies)
AbstractThe rising demand for household appliances is driving manufacturers to adopt innovative and sustainable approaches in product development. Virtual Product Development (VPD) has emerged as a key enabler in this transformation, offering the capability to shorten time-to-market while reducing reliance on physical testing.This work presents a practical case study on the development of a system-level refrigerator model to assess performance under varying operating conditions and to support rapid concept evaluation and preliminary design decisions in a virtual environment. In the early stages of product development, understanding system behavior under critical test conditions—such as energy consumption and pull-down performance—is essential.To address this, transient performance models were developed using a 1D system modeling approach in GT-SUITE. These models enable accurate prediction of refrigerator performance, minimizing the need for extensive physical testing and enhancing early-stage decision-making. Simulation results were validated against physical test data, demonstrating strong correlation across key performance parameters. The outcomes highlight the effectiveness of system-level transient modeling in accelerating sustainable appliance development.
Authored & Presented By Jaya Raju Namala (Airbus Group India Private Ltd)
DescriptionThis presentation will talk about the various initiatives Airbus is taking worldwide to enable Advanced Modelling and Simulation (M&S) to replace certification full scale physical testing through concerted efforts across the Aerospace Industry to enhance M&S credibility. As part part of International M&S Credibility Assurance Framework Industry Standard development which Airbus are developing along with Boeing, Embraer, NASA, Certification bodies (EASA, FAA etc.,) and many other aerospace organizations, this keynote presentation will also address the takeaways from the standard and the efforts we are putting in to make M&S fast, intelligent and credible.
10:20
Presented By Guru Anandan (Altair)
Authored By Guru Anandan (Altair)Vinoth Dhanavel (Altair)
AbstractNatural fibre-reinforced composites, such as jute fibre composites, are increasingly used in the automotive sector due to their lightweight nature, sustainability, and contribution to reduced carbon emissions. However, accurate evaluation of their structural stability under thermal loading conditions is crucial to ensure reliable service performance. Specifically, predicting the critical buckling temperature — the temperature at which the laminate becomes unstable — is vital for design and safety considerations.Thermal buckling behaviour of laminated composite is influenced by several factors, including ply orientations, stacking sequences, and the presence of cutouts (such as circular, rectangular, or slotted holes) at various positions within the plate. Traditional analysis methods are computationally intensive and time-consuming, especially for complex, non-standard configurations.This study aims to develop and deploy an Artificial Intelligence (AI) / Machine Learning (ML) model capable of predicting the critical buckling temperature of jute fiber-reinforced composite rectangular plates with arbitrary, non-parametric designs. The model will be trained on data generated from thermal buckling simulations of various laminate configurations and cutout geometries, enabling rapid, accurate prediction for novel designs without the need for extensive finite element simulations.
Authored & Presented By Smit Sharma (MOBIS India Limited)
AbstractInstrument panel modules play an important part in mitigating injury risks during collisions. This study presents an optimization method for Instrument panel design for head impact analysis using LS- TaSC (LSDyna Topology and Shape Optimization tool).LS- TaSC was used to enhance the structural integrity and energy absorption of the Instrument panel module. By using topology optimization techniques, it refines material distribution within the Instrument panel module to minimize head form acceleration upon head impact. This optimization process regulatory constraints and industry-standard head impact criteria, ensuring compliance while achieving a balance between weight reduction and improved safety performance. The study outlines the computational process, finite element simulation and shape and topology refinements using LS- TaSC. Key findings highlight the impact of optimized material distribution on energy dissipation and force transmission, ultimately contributing to improved design. This research underscores the effectiveness of LS- TaSC in achieving a streamlined, efficient Instrument panel design while adhering to stringent safety requirements. The findings present valuable insights for automotive engineers seeking to enhance occupant protection through data driven optimization methodologies.
Presented By Pushpendra Mahajan (Whirlpool Corporation)
Authored By Pushpendra Mahajan (Whirlpool Corporation)Avinash Chandra (Whirlpool Corporation)
AbstractWhirlpool Corporation is a global leader in home appliances, with over 110 years of innovation. Known for its iconic brands like Whirlpool, KitchenAid, and Maytag, the company offers a wide range of products, including washing machines, refrigerators, dishwashers, and ovens. As consumer demand for quieter household appliances continues to rise, reducing noise in devices such as dryers has emerged as a critical area of focus in appliance design and development.. The high turbulence is the primary contributor to airborne noise, which induces flow separation from the blower blades. Flow separation, where the boundary layer detaches from the surface, leads to intense pressure fluctuations and unsteady vortex shedding, which in turn generates aerodynamic noise, including significant airborne acoustics. This necessitates a coupled approach that combines unsteady-state flow field calculations with aero-vibro-acoustic evaluations of the structure. The flow analysis is conducted using transient incompressible Reynolds-averaged Navier-Stokes (RANS) equations, which provide fluctuating surface pressure fields on both the blower and casing surfaces. The resulting aero-vibro-acoustic noise is then evaluated using the Multi-Domain Boundary Element Method (MDBEM). Although the incompressible CFD approach ensures faster computations, it only captures convective components of pressure fluctuations. To derive acoustic pressure fluctuations from the CFD data, this study uses a new derivation of acoustic analogy based on Curle’s integral formulation and Lighthill’s equation. The coupled methodology enables precise prediction of blower noise and its contributing factors, facilitating the optimization of blower design through systematic virtual experiments employing Design of Experiments (DOE). This approach led to a 6-8 dBA reduction in blower noise, highlighting the effectiveness of the optimized design in improving appliance performance.
10:40
Presented By Manish Kumar (Valeo)
Authored By Manish Kumar (Valeo)Arunprasad Janakiraman (Valeo)
AbstractOvermolded polymer parts are widely used in engineering applications based on the application needs and multiple benefits. However, Certain environment conditions can lead to localized stress concentrations, delamination, and premature failure, particularly in regions influenced by fiber orientation and weld line formation during molding. This work presents a coupled simulation methodology to evaluate the thermo-mechanical response of overmolded components under exposure to thermal shock—sudden temperature variations. Fiber orientation tensors are extracted from mold flow simulations and incorporated into the finite element model using an orientation-dependent constitutive formulation based on the Mori–Tanaka micromechanics approach. Weld line regions are identified and major weld line regions are selected for the material degradation due to weld lines in the simulation. The simulation is performed for one thermal shock cycle. The stress results are exported to perform the fatigue calculation for the required number of cycles. The effect of boundary conditions , fiber orientation and weld line are studied .This approach offers a predictive tool for design validation and performance optimization of overmolded structures operating in thermally aggressive environments.
Presented By Umakant Thakur (Whirlpool Corporation)
Authored By Umakant Thakur (Whirlpool Corporation)Sachin Nilawar (Whirlpool Corporation) Arjun Kadam (Whirlpool Corporation)
AbstractWhirlpool Corporation is a global leader in home appliances, with over 110 years of innovation. Known for its iconic brands like Whirlpool, KitchenAid, and Maytag, the company offers a wide range of products, including washing machines, refrigerators, dishwashers, and ovens. The dryer works on convective heat transfer.The area that comes in contact with hot air plays a vital role in drying performance. Tumbling action of cloth is important to avoid the tangling of cloth and to expose the cloth. Tumbling action is dependent on drum rpm, lifter/Baffle height and combination of lifter/baffle. This study involves developing a finite element (FE) model for the dryer and the clothes/Fabrics inside it. Simulation is carried out for a typical dry cycle. Which involves in representation of fabric material modeling and preparing FE models for clothes as sheets, towels & pillow covers based on AHAM standard cloth representation used for testing. The Scope of work involves a detailed study of how to get realistic tumbling behavior in dryers. Fabric model is to be built with appropriate sizes and material properties of cloth with meshing in appropriate sizes is needed, then create an LS-Dyna model of dryer and feed in the clothes as per the mentioned AHAM standard. To build a methodology to use fabric models and standardize them for use in laundry applications for washers and dryers which helps to reduce the cost of the prototyping, testing, and time. This simulation is vital for understanding the dryers tumbling during different RPMs, size of the baffle, and the number of baffles.Our area of interest is to build a model with cloth representation, run a dry cycle, and calculate the exposed area.
Authored & Presented By Prem Chinna Polamada (Mobis india limited)
AbstractThermal and flow simulations using CFD is becoming increasingly vital for analyzing heat and flow characteristics during both the conceptual and detailed design stages. These simulations enhance performance while reducing product development time and costs. This article delves into the impact of design parameters on design exploration studies in CFD.When conducting defrosting simulations, the complexity is heightened by internal turbulence, induced pressures, and pressure drops between subsystems, making it challenging, time-consuming, and costly to achieve meaningful results. However, by adjusting the variable parameters, we can significantly improve the outcomes. This paper provides a comprehensive review comparing the direction, velocity, and turbulence levels of flow optimization, which showed significant improvements. It also explores methods for optimizing the design of defroster nozzles. Various techniques for enhancing performance by modifying either the solid or fluid domain are discussed. The aim is to summarize the utilization and efforts dedicated to improving the performance of demisters and nozzles. For a quick estimation of defroster duct performance in the automobile industry, well-known correlations based on geometry and flow properties can be employed. This methodology can also be applied to other domains, such as electronics cooling, with proposed solutions.
Authored & Presented By Soumik Ghatak (ANSYS Inc.)
AbstractMany manufacturing processes in semiconductor industry require coupled structural-thermal behavior. These kinds of processes need structural solver as well as conjugate heat transfer solver. In this presentation, simulation methodology for two manufacturing processes have been demonstrated using coupled structural thermal solver. One is laser cutting and another one is ultrasonic welding. Methodology for both analyses has been demonstrated for proof-of-concept model and suitable post processing items have been shown. Laser cutting is one of the major manufacturing processes used for wafer dicing. For laser cutting analysis, specific card for heat flux trajectory has been implemented and strongly coupled thermal structural problem has been solved with temperature dependent erosion of the material. Based on the simulation result, users can estimate how much heat flux is needed to cut the material and what would be profile of the surface after the process. Ultrasonic welding is another important welding method in semiconductor manufacturing industry. For this analysis, suitable thermal property of material has been used, and temperature dependent tying of contact has been explored. Based on the simulation result, users can estimate how much heat is generated and how much power is needed to join the material.
Presented By Amruta Raut (Stellantis)
Authored By Amruta Raut (Stellantis)Mohamed Azarudin (Stellantis) Darshan Pawargi (Stellantis) Sumant Malagi (Stellantis) Ebrahim Saifee (Stellantis) Anantharam Sheshadri (Stellantis)
AbstractIsogeometric Analysis (IGA) utilizes higher-order and higher-continuity splines to represent both geometry and the solution field. This approach allows for a more accurate geometric representation, improved solution accuracy, and larger timestep sizes compared to the traditional Finite Element Method (FEM). Nowadays, all the industries using FEA to analyze the crash models are exploring the potential of IGA.In this study, a hybrid IGA/FEA modeling approach was adopted, wherein IGA shell elements were applied to critical components and integrated with conventional FE parts. Two case studies were conducted to evaluate this hybrid approach: the first focused on the B-pillar crash study, where all B-pillar components were modeled using IGA shells; the second involved the hood assembly for a pedestrian protection (Ped-Pro) load case, also utilizing IGA shells. The results from these hybrid models were compared with the corresponding traditional FE models and physical test results, demonstrating comparable accuracy. Since all legacy material models, contact and connection techniques can be used with IGA without any change, a Hybrid modelling approach can be used for vehicle crash models.
Presented By Pranav Shinde (QARGOS)
Authored By Pranav Shinde (QARGOS)Alok Das (QARGOS) Karthik Balachandran (Dassault Systemes)
AbstractThe Drag prediction of an electric cargo scooter is a very important stage in the design of an electric cargo scooter. Early knowledge of the drag forces will enable the designers to accurately estimate the motor power, battery pack size, vehicle range, and lastly the vehicle maximum speed and dynamics. In the present paper we are presenting a simulation based methodology to estimate the drag forces and drag coefficient of an Electric Cargo Scooter that is designed for use in cargo logistics application. The Computational Fluid Dynamics approach allows for estimating the drag forces and drag coefficient as a function of vehicle velocity accurately. CFD simulation based flow visualization enables to identify high pressure stagnation zones and propose design changes to reduce the drag force and hence the drag coefficient. The main advantage of simulation based method is reducing the number of prototypes built during the initial design stages and reducing wind tunnel tests. Future variants will also not require any wind tunnel measurements as they will be accurately predicted using the established process. Reynolds Averaged Navier Stokes (RANS) based steady state Computational method is utilized. The Study includes mesh independence and Turbulence model independence studies.
11:50
Presented By Kirankumar Pedamallu (Arup India Pvt Ltd)
Authored By Kirankumar Pedamallu (Arup India Pvt Ltd)Maruthi Kotti (Arup India Pvt Ltd)
AbstractVirtual testing has emerged as a transformative tool in passive automotive safety, addressing the limitations of traditional physical crash tests while enhancing cost efficiency, scalability, and real-world scenario coverage. By leveraging computational models and finite element analysis, virtual testing enables the simulation of diverse crash conditions, occupant postures, and vehicle configurations without requiring extensive physical prototypes.The current roadmap emphasizes integrating virtual testing with physical validation through hybrid methodologies, particularly for regulatory compliance and NCAP assessments. Initiatives like the EU-funded OSCCAR project aim to standardize Human Body Models (HBMs) for evaluating diverse occupant demographics (e.g., varying sizes, genders, and postures) and emerging autonomous vehicle layouts. Euro NCAP’s 2025 protocol formalizes a five-step virtual testing process combining OEM simulations with physical verification tests, using ISO scores and injury risk KPIs for validation.HBMs now play a central role in addressing the biomechanical limitations of Anthropomorphic Test Devices (ATDs), offering detailed injury prediction for organs and skeletal structures. However, current HBMs face challenges in muscle reflex accuracy, neural delay variability, and injury risk function standardization. NCAP frameworks also lack universally accepted HBM assessment criteria, relying instead on ATD-derived metrics that may not fully capture human physiological responsesFuture advancements will focus on possible digital twin integration, improved HBM fidelity for pre-crash kinematics, and AI-driven scenario generation in combination with Active safety scenarios. The industry is moving toward "full virtual testing" paradigms that combine HBMs with validated vehicle environment models, particularly for evaluating advanced driver assistance systems and unconventional seating configurations. These developments aim to extend passive safety protections to vulnerable populations while maintaining regulatory rigor through hybrid physical-virtual validation frameworks.We will explore the current simulation tools and their ecosystem, underscoring the importance of evolving virtual assessment practices to align with global safety protocols and their seamless integration.
Authored & Presented By Abhijit Chaudhuri (Airbus)
AbstractAirbus has been playing a key role over many years in development and validation of modelling and simulation methods for metallic and composite aircraft structures for dynamic crash and impact phenomena. In particular, for a dynamic crash and impact event, the prediction of the damage in composite structures under high loading rate and addressing all the possible failure modes (fiber/matrix failure, delamination, disbonding…) and their interaction through simulations is challenging. Apart from the material failure prediction, another aspect is to also predict the failure of the joints between the structural parts (bolted, bonded joints). This presentation will focus on the simulation method development of the prediction of damage in composite and sandwich structures for soft impacts like bird strike and tyre debris impacts using the building block approach and validation at different levels through development tests. Another interesting aspect is the development of simulation capabilities to predict the secondary impact (particularly for bird strike). Also an important point is to predict the behaviour of the bird itself during the impact and the interaction of the bird and the structure during the flow of the bird in order to correctly assess the force transmitted from the bird to the structure during the impact phase. During the development phases of the aircraft, "realistic simulations" make it easier to ensure quality, safety, reliability and also reduce costly and time consuming higher level tests. The aim at a higher level is to demonstrate the means of compliance for A/C certification through credible simulation methods. The presentation will also highlight Airbus vision and future challenges for crash and impact simulations of aircraft structures.
Authored & Presented By Vivek Tripathi (Atmus Filtration Technologies)
AbstractAtmus Filtration Technologies is a global leader in filtration solutions for on-highway commercial vehicles and off-highway vehicles and equipment. These filtration solutions use spring loaded valves extensively for multiple applications. Behavior of these spring-loaded valves is very complex phenomenon. Hence, performing detailed simulations to predict pressure drop across these valves is quite time consuming and resource intensive. To address this, extensive CFD analyses were performed to generate validated results on a wide range of valve configurations and applications. A non-dimensional 1D model was then developed using machine learning and data training from these CFD simulations. This model was implemented as an excel based tool to increase technical productivity in product development process. It is understood that the valve pressure drop is dominated by spring force which determines the valve displacement, resulting maximum velocity and associated inertial head. The pressure drop across the valve is function of Reynolds number. This tool predicts the pressure drop with root mean square percentage error of around 10% with respect to CFD simulation. Product teams leverage this tool during the concept phase, thereby reducing the overall product development cycle.
12:10
Presented By Himanshu Gururani (Dassault Systemes Solutions Lab, Pune)
Authored By Himanshu Gururani (Dassault Systemes Solutions Lab, Pune)Sachin Ural (Dassault Systemes Solutions Lab) Anand Pathak (Dassault Systemes Solutions Lab) Himanshu Gururani (Dassault Systemes Solutions Lab)
AbstractMinimally Invasive Surgery (MIS) has gained widespread adoption due to its advantages of smaller incisions, faster recovery times, and improved surgical precision. Surgical staplers, essential for wound closure in nearly 80% of procedures, are widely utilized but increasingly associated with malfunctions, and adverse patient outcomes. The rising incidence of stapler failures highlights the urgent need for improved testing – we demonstrate how virtual validation methods can be used to augment conventional physical tests while simultaneously providing greater reliability and efficiency. This study presents a simulation-driven framework to optimize and validate surgical stapler designs, specifically targeting common failure modes such as malformed staples and blood leakage. A unified modeling and simulation approach is implemented, featuring parametric exploration of stapler and anvil geometries. Tissue behavior is modeled using a Hyperfoam material model, while stapling dynamics, including complex tissue-stapler interactions, were captured through the Coupled Eulerian-Lagrangian (CEL) method. Eulerian Volume Fraction tracking and minimum principal stress analysis enabled detailed assessment of staple formation and identification of potential leakage pathways.The framework developed for stapler validation has been used in real-world settings to design staplers with substantially reduced risk profiles, and can easily be extended or customized for any surgical stapling application. This virtual validation methodology offers a scalable, robust solution for advancing surgical stapler development, enhancing device reliability, and accelerating innovation in medical device manufacturing.
Presented By Nagananda Upadhya Gopalakrishna (BETA CAE Systems)
Authored By Nagananda Upadhya Gopalakrishna (BETA CAE Systems)Michael Richter (MATFEM Ingenieurgesellschaft mbH)
AbstractIn recent years, the pursuit of lightweight products has become a critical objective, particularly in the automotive industry. An increasing number of components are being replaced with non-reinforced and fiber-reinforced plastic materials to meet stringent demands for both weight reduction and safety. However, this shift introduces significant challenges in industrial crashworthiness and pedestrian safety simulations, especially for larger components with locally varying mechanical properties influenced by the injection molding process.From a simulation perspective, addressing these challenges requires multi-step processes: i. conducting an injection molding simulation, ii. mapping the resulting material properties onto the structural mesh, iii. preparing accurate material model definitions, iv. performing the structural simulation, and v. analyzing the results. While effective, this workflow can be resource-intensive, particularly during the early design stages when design geometry is still evolving, and full-scale injection molding simulations with standard solvers are prohibitively costly in terms of time and budget.To tackle these challenges efficiently, this work proposes, additionally to the standard injection molding analyses, a simplified solution that streamlines the integration between injection molding and structural simulations. This approach enables multiple "what-if" studies and optimization loops, facilitating rapid iteration during early design stages, while maintaining decent quality results. Additionally, this helps to bridge the gap between structural and molding engineers by introducing simplified solvers, empowering engineers from various disciplines to explore molding effects without extensive expertise.A benchmark study was conducted to demonstrate the effectiveness of this approach using a common plastic compartment subjected to standard loading tests. For this purpose, a common plastic part of hat-profile shape was picked. Simulations were performed with both conventional isotropic material models and high-fidelity orthotropic material models derived from injection molding simulation data. The results highlight and quantify the impact of manufacturing processes on the component’s crashworthiness, underscoring the importance of incorporating manufacturing effects into structural simulations.
Presented By Vaibhav Gulakhe (Whirlpool Corporation)
Authored By Vaibhav Gulakhe (Whirlpool Corporation)Aswin R (Whirlpool Corporation)
AbstractThe utilization of virtual product development has become increasingly crucial in modern industry, enabling accelerated design conceptualization and final product realization. This study investigates the efficacy of engineering simulation, specifically computational fluid dynamics (CFD), in evaluating the performance of a dishwasher spray arm for soil removal. Employing a multiphase approach, the simulation assesses the spray arm's performance by quantifying wall shear stress, which is then correlated with the soil removal rate. This methodology provides a quantitative measure of the spray arm's effectiveness in a virtual environment. The study involves developing a CFD model of the dishwasher spray arm, incorporating relevant fluid dynamics principles and boundary conditions. The simulation results are analyzed to determine the distribution of wall shear stress and its correlation with soil removal efficiency. To validate the proposed approach, the simulation findings are compared with experimental data obtained from physical testing of the spray arm. The comparative analysis demonstrates a good correlation between the simulation results and the physical test outcomes, confirming the reliability and accuracy of the virtual assessment method. This study highlights the potential of CFD simulation in optimizing dishwasher spray arm design and performance, reducing the need for extensive physical prototyping and testing. The findings contribute to the advancement of virtual product development techniques and provide valuable insights for enhancing the efficiency and effectiveness of household appliances.
12:30
Presented By Mayur Sabale (Knorr-Bremse Technology Center India Pvt. Ltd.)
Authored By Mayur Sabale (Knorr-Bremse Technology Center India Pvt. Ltd.)Pradip Sontakke (Knorr-Bremse Technology Center India Pvt. Ltd.) Shashank Borde (Knorr-Bremse Technology Center India Pvt. Ltd.)
AbstractOver the years Finite Element Analysis (FEA) has become the most important part of design and development process of mechanical components. While FEA provide quick insights into the behavior of a component under given operating conditions, designer needs to do iterative refinement to achieve the expected outcome. This process becomes time consuming for complex components. To reduce the time required for development, we have used an approach where the data from FEA is used to train Machine Learning (ML) model and predict the output.This study examines the use case of solenoid bracket plate, which is used to secure solenoids in an assembly at a certain location using bolts. In the conventional method, FEA is used to determine the reaction force that the bracket plate claws exert on the solenoids as a result of bolt tightening. It is of utmost importance to achieve the specific value of a reaction force to avoid any leakages from the assembly.The new approach, identifies key bracket plate design parameters and generates a parametric FEA model. Important design parameters are used in a Design of Experiments (DOE), and a database of FEA results is created to train ML model. A trained ML model is used to predict reaction force at the claws. Lastly, predicted reaction forces are validated using FEA simulation for the set of design parameters.
Presented By Akshaya Gomathi (Ansys)
Authored By Akshaya Gomathi (Ansys)Mukul Atri (Ansys)
AbstractThis study presents a single cell explosion event within a battery pack using LS-DYNA. The objective of the study is to investigate the structural and thermal responses of adjacent cells and the battery enclosure under internal cell failure conditions. The single cell explosion is modelled using the equivalent energy-based approach in LS-DYNA. A coupled EM thermal-structural is adopted to simulate the deformation, internal short and thermal runaway of the battery. Key failure modes such as casing rupture, inter-cell deformation and structural integrity loss is examined. The HPPC parameters for cell characterization and short-circuit criteria are added to study Multiphysics behavior. We shall also consider residual effect of manufacturing processes. The simulation results highlight the critical influence of cell spacing, venting features, and casing strength on explosion propagation. Findings demonstrate workflows to conduct virtual testing for safter pack design and thermal management strategies to meet industry Standards such as SAE J2464, AIS 038.
Authored & Presented By Madhan Kumar (Simerics Inc)
AbstractThis paper discusses the CFD investigation to estimate the cavitation characteristics of the Princess Royal propeller using the RANS-based CFD code Simerics-MP+. The propeller used in the Princess Royal research vessel at Newcastle University was chosen for the present cavitation benchmark study. Accurate prediction of propeller cavitation characteristics is of paramount importance to avoid its adverse effects, such as reduced propulsive efficiency, erosion, and increased noise. Initially, the propeller performance was validated in open water conditions, and then cavitation studies were conducted for different flow conditions based on the advanced coefficient and cavitation number. The equilibrium dissolved gas model [1] which accounts for both aeration and cavitation effects, is used to model the cavitation characteristics. The gas present in the fluid can be in various states, such as free, dissolved, or mixed, based on the local pressure. The cavitation bubbles and the propeller performance characteristics obtained from the CFD simulation showed good agreement with the available experiment and numerical data. In addition, the noise characteristics of the propeller at different cavitation conditions were obtained and reported to elucidate the influence of cavitation on the propeller noise. The Ffowcs-Williams-Hawkings (FW-H) acoustic analogy is used to predict the noise propagation to the receivers and then compared against the test data for validation. It was found that the noise data from the present study showed good agreement with the test data
12:50
Presented By Shankar Venkat (Arup India Pvt Ltd)
Authored By Shankar Venkat (Arup India Pvt Ltd)Galal Mohamed (Arup)
AbstractHuman Body Models (HBMs) are detailed biofidelic finite element models of the human body, which encompass different genders and physiques including detailed anatomical features of the skeletal structure, internal organs, and other soft tissues like skin, flesh, and ligaments. The use of HBMs in numerical simulations offers exciting opportunities in automotive, aerospace and civil aerospace development in areas such as safety, comfort, and ergonomics. These models will play an increasingly important role in the study of human body kinematics and assessment of injury risks in collision accidents. One of the very informative and interesting application of the HBMs is the virtual study of biomechanical response of the aircraft occupants during the brace posture for the emergy landing and the subsequent impact scenarios. Using a generic aircraft seat, a series of standard dynamic load cases are performed to predict occupant kinematics and the extent of injury risk to the aircraft occupant. This research will contribute to the wider application of HBMs in the aviation industry and promote biomechanical research into aircraft occupant safety.
Presented By Vaideeswarasubramanian Kannan (Flsmidth)
Authored By Vaideeswarasubramanian Kannan (Flsmidth)Saminathan Karumbayiram (FLSmidth)
AbstractA hydrocyclone is a device used to separate particles in a slurry based on their size and density, playing a crucial role in the mining industry for efficient mineral separation. The slurry feed enters the hydrocyclone, creating centrifugal force that separates the particles. The heavier particles move to the outer wall and exits as underflow, while the lighter particles move to the core and exits as overflow due to back pressure. This study presents a comprehensive analysis of hydrocyclone performance using finite element analysis and computational fluid dynamics in accordance with ASME guidelines. A commercial numerical simulation software is utilized to highlight the benefits of performing analysis in determining stress distribution, fluid flow patterns, and potential improvement areas. The evaluation of various operational characteristics is significant to enhance the separation performance, and the utilization of simulation studies is emphasized. These analyses include the integration of cloud computing to enhance computational efficiency for large-scale models. This paper provides insights into optimizing design and operational parameters, to improve reliability and performance.
14:10
Presented By Dore Prasad R (Tyco Electronics Corporation India (P) Ltd.)
Authored By Dore Prasad R (Tyco Electronics Corporation India (P) Ltd.)Sandeep Santhanagopal (Tyco Electronics Corporation India (P) Ltd.)
AbstractIn Aerospace, Defense, and Marine (ADM) applications, achieving robust sealing performance is critical for maintaining Ingress Protection (IP) ratings against environmental stressors like dust and water. Traditional simulation methods rely on linear approximations and simplified contact mechanics, which fail to accurately capture the nonlinear, hyperelastic behavior of elastomeric gaskets under screw preload. While conventional FEA can predict general deformation trends, it often lacks the fidelity to model real-world sealing performance due to insufficient material characterization and oversimplified boundary conditions.To address these limitations, this paper presents an advanced simulation-driven methodology combining high-fidelity nonlinear FEA with hyperelastic material modeling. A key innovation is the development of a physics-based transfer function that correlates IP ratings with sealing pressure, friction coefficients, compression ratios, material properties, and preload forces. Experimental material characterization was performed to extract precise stress-strain data, which was curve-fitted to a Neo-Hookean model for accurate hyperelastic behavior. The FEA model incorporates large deformations, contact nonlinearities, and screw preload effects in ANSYS, enabling a system-level assessment of sealing integrity.A sensitivity analysis was conducted to identify critical design variables, while IP test conditions were translated into pressure boundary conditions to predict minimum sealing pressures for target IP ratings. The model was validated against physical tests, demonstrating >90% accuracy in predicting gasket compression and contact pressure distribution. Deviations (<10%) were attributed to surface imperfections and manufacturing tolerances, highlighting the model’s robustness.By adopting this methodology, design cycles can be reduced by ~40%, and physical prototyping costs can be cut by ~30%, while enabling rapid design iteration. This approach advances simulation technology by providing a predictive, physics-based framework for sealing performance optimization, moving beyond empirical trial-and-error methods. The results underscore the value of hyperelastic modeling and system-level FEA in achieving reliable IP-rated enclosures for demanding ADM environments.
Presented By Mahendra Jadhav (Whirlpool Corporation)
Authored By Mahendra Jadhav (Whirlpool Corporation)Sachin Nilawar (Whirlpool Corporation)
AbstractWhirlpool Corporation is a global leader in home appliances, with over 110 years of innovation. Known for its iconic brands like Whirlpool, KitchenAid, and Maytag, the company offers a wide range of products, including washing machines, refrigerators, dishwashers, and ovens. The physics of horizontal axis washing machines is complex due to the rotating nature of the system, which operates at spin speeds ranging from 1,200 to 1,400 RPM. To ensure a robust product design at these speeds, it is essential to carefully analyze factors such as vibrations, load distribution, and mechanical stresses. Gap closure simulation is vital for understanding the machine's behavior during spinning, as imbalances and excessive vibrations can significantly affect performance.Gap closure is a dynamic simulation load case that is analyzed using the ADAMS–ANSYS approach. The main goal of the gap closure simulation is to prevent the complete closing of the initial gap due to deformation, as this could cause contact between rotating and non-rotating parts during high-speed spinning. Determining the critical spin speed of the wash unit, along with gap closure, is essential to avoid resonance, which could lead to excessive vibrations or potential damage.The study involves conducting experiments on a wash unit to establish a correlation between experimental results and simulation outcomes, explicitly focusing on critical speed and Gap closure.
Presented By Ankit Khandelwal (ITB Engineering services private limited)
Authored By Ankit Khandelwal (ITB Engineering services private limited)Yannick Lattner (ITB Ingenieurgesellschaft Technische Berechnungen mbH)
AbstractModern world electronics are seeing a rapid change and still accelerating development. Fundamental physics pose great challenged to engineers trying the maintain thermally safe operations. These challenges are additionally complicated by the need to enable continuous operations. CFD-Simulations are essential to predict and analyse the thermal system's behaviour. CFD results precisely predict all relevant thermal and flow properties under controlled conditions.Data centre thermal management systems - Are we really awake to combat a safe and sustained thermal management with rapid shift of IT Servers heat load requirements? The thermal handling of radiations emerging through newly digitised smart phones or any electronics appliances? There is a pressing need for designs that ensure lasting thermal comfort for users and device cooling, backed by validated CFD simulations. Thermal management in data centre cooling involves controlling key parameters like coolant flow rate, room humidity, and air quality indices (PMV, PEV). Simulations help identify hotspots and evaluate worst-case scenarios such as power outages.CFD simulations reveal critical flow zones like stagnation or recirculation areas that may cause hotspots near server racks. Whether air, water, or immersion cooled, CFD is key to optimizing thermal infrastructure. CFD being a powerful way to model and optimise thermal air flow in data centres, is on other hand typically challenging and requires domain experts. It requires critical thinking & detailed layout to comprehend correct input parameter. High-fidelity CFD simulations require significant computing power and expertise plays to balance between the two which is vital for any critical simulation. Domain expertise helps correct mesh models, avoiding numerical instabilities, interpret simulation results with engineering judgments, suggesting design fixes that actually work in practice, not just in the simulation, validating cooling redundancy scenarios, etc.
14:30
Authored & Presented By Abhiram D R (Infosys)
AbstractThis study presents the robust design optimization of the 2D cross-section of two-stage turbine discs for large civil gas turbine aero engines. Turbine discs, classified as critical parts, must satisfy various safety criteria defined by certification agencies like EASA and FAA due to the hazardous consequences of their failure. These discs endure high centrifugal and thermal loads, which are cyclical and lead to fatigue.In addition to integrity and fatigue requirements, turbine discs must meet functional requirements such as disc lean and rim-roll, limiting allowable deflection at the disc rim. System-level requirements, such as weight limits, must also be adhered to, making turbine disc design suitable for multi-objective optimization.This work conducts multi-objective optimization of the 2D cross-section of two-stage turbine discs. A systematic approach based on design-of-experiments (DoEs) is followed to identify critical design parameters affecting disc characteristics. This approach helps manage the number of design parameters in the final DoEs.DoEs also aid in generating surrogate models to predict disc characteristics, including integrity margins, stress (and fatigue life), functional behaviors (disc lean and rim roll), and weight, based on design parameters. These surrogate models, verified with a separate validation dataset, range from simple polynomials to complex models like radial and elliptical basis functions. The surrogate models facilitate multi-objective optimization to achieve an optimal 2D cross-section for the turbine discs, satisfying all criteria. The full paper will detail the optimal solution, benefits compared to a baseline design, selected design parameters, DoE construction, surrogate models, optimization objective function, and robustness assessments.
Presented By Mayalekshmi K.M (CNH Industrial Technology Services India Pvt. Ltd.)
Authored By Mayalekshmi K.M (CNH Industrial Technology Services India Pvt. Ltd.)Stefano Largo (CNH Industrial Italia S.P.A.) Alessio Serracca (CNH Industrial Italia S.P.A.)
AbstractPrinted Circuit Boards (PCBs) used in agricultural off-highway vehicles such as tractors are exposed to intense random vibration and continuous mechanical loading which can result in failures like cracked solder joints and broken lead wires due to repetitive board flexing. It is vital to ensure the structural durability of PCBs under these demanding conditions for reliable performance of control and monitoring electronics. This study presents a state-of-the-art simulation-based approach to evaluate the structural and fatigue behavior of PCBs designed for agricultural machinery applications.The PCB was simplified by modeling only key layers and critical components to optimize computational effort without compromising accuracy. The workflow begins with Normal Mode Analysis to identify the natural frequencies and mode shapes. It was followed by Power Spectral Density based Fatigue Analysis using Steinberg’s method, which helps to identify high-risk components. A sub-model was then developed with detailed modeling focusing on these critical regions. Random Response Analysis was performed on the sub-model, followed by SN-curve based Vibration Fatigue Analysis for a more accurate prediction of fatigue life. To further enhance the reliability prediction, a thermal-based Solder Fatigue Analysis was performed, considering temperature effects that may lead to joint and lead failures over time. This combined approach offers a comprehensive understanding of PCB durability under realistic machine operating conditions in agricultural industry.The study integrates advanced structural analysis techniques in the design of agricultural electronics. The detailed analysis enables accurate prediction of crack initiation and propagation, enhancing long-term durability assessment. It provides insights into failure zones, enabling robust PCB designs that withstand the harsh conditions of agricultural machinery. By reducing prototyping cycles and improving computational efficiency, this method contributes to lower maintenance costs and increased reliability of electronic systems used in agricultural machinery industry. Keywords: Printed Circuit Board (PCB), Agricultural Machinery, Simulation Driven Design, Vibration Fatigue
Presented By Vinay Yadav (Tafe Motors and Tractors Limited (TMTL))
Authored By Vinay Yadav (Tafe Motors and Tractors Limited (TMTL))Tarun Nema (Tafe Motors and Tractors Limited (TMTL) Pawan Singh (Tafe Motors and Tractors Limited (TMTL) Sanjiv Pathak (Tafe Motors and Tractors Limited (TMTL) Hemant Shrikhande (Tafe Motors and Tractors Limited (TMTL)
AbstractTechnology brings success and growth but it has undesirable side effects such as pollution. A type of pollution predominant on the agricultural and industrial divisions is noise. There are a lot of noise sources around us primarily automotive vehicle, machine and industry. These noise sources lead to dangerous diseases and also able to do permanent hearing loss for a person. Tractor engine is one of the unwanted noise sources. It produces high amplitude noise due to vibration. These vibrations lead to fatigue failure of other components. To control noise of engine, silencer is used. So, there is a need to design silencers more accurately. Silencers have a lot of design parameters based on aero-dynamic criterion, mechanical criterion, geometrical criterion and economical criterion. Some of the primary parameters are backpressure loss, insertion loss and transmission loss. This paper is about simulation of finite element analysis (FEA) to know the transmission loss and back-pressure in round (cylindrical) and elliptical tractor’s mufflers for a given frequency range. Finite element modelling of the problem has been performed in ANSYS Workbench 2020R2 for transmission loss. The result obtained from round muffler is compared with elliptical muffler. Analytical solution and traditional laboratory methods are discussed. These traditional methods are the four-pole transfer matrix method and three-point method. Computational Fluid Dynamic (CFD) analysis is performed using Hyper works CFD and AcuSolve CFD for determination of back-pressure in elliptical and round muffler. Results of round muffler is compared with elliptical muffler. It is observed that elliptical muffler is better in TL and backpressure.
14:50
Authored & Presented By Priyabrata Maharana (BQP Tech Pvt Ltd)
AbstractBQP would like to share results from its study on using quantum inspired algorithms for optimizing battery pack designs.The study explores quantum inspired optimization algorithms to investigate new battery pack designs that enhance the thermal management system and reduce the average temperature rise of cells in electric vehicles. The configuration of battery units, including the spacing between cells, their arrangement, the design of the housing and other related parameters, has a significant influence on the thermal behavior of the pack.In this work, the battery pack is modelled using MATLAB and Simulink to generate the output temperature profile. This system level modelling for a two-wheeler electric vehicle enables the formulation of an optimization problem that relates the input parameters to their effect on cell temperature. The objective of the optimization is to improve thermal performance while maintaining the overall performance of the battery and extending its lifespan by limiting the cell temperature.The optimization problem is solved using Quantum Inspired Optimization Algorithms which uses concepts from quantum mechanics to accelerate convergence and achieve highly optimal solutions with an increased rate of design exploration.The optimal solution obtained for the thermal management system using Quantum Inspired Algorithms identifies the most effective arrangement of battery cells under the operating conditions and reduces the heat retained in the system compared with conventional arrangements. The optimal arrangement also prevents thermal runaway, thereby lowering the risk of battery related incidents in electric vehicles.The optimized positioning along with placement of phase change materials, the initial results demonstrate a reduction of 40 degrees Celsius in the maximum temperature of the battery pack.
Authored & Presented By Pallavi Pawar (TE Connectivity)
AbstractMedia converters are essential networking devices that enable seamless communication between Ethernet copper and fiber optic channels, thereby extending network reach and ensuring reliable data transmission in demanding industrial and aerospace environments, including shelters, ground vehicles, and naval platforms. Ensuring operational reliability under such harsh conditions necessitates stringent environmental qualification, with vibration analysis being a critical factor in evaluating mechanical robustness. To meet the requirements of a specified vibration standard, a simulation-driven development process is implemented to identify structural vulnerabilities prior to physical testing. Modal analysis revealed natural frequencies within the operational excitation range, indicating potential resonance risks. To address these, the converter underwent sine sweep and random vibration simulations using power spectral density (PSD) profiles. The simulations assessed key parameters such as amplitude, its peaks, stress, displacement, ensuring they remained within allowable limits.This project has been instrumental in establishing a standardized methodology for the specified standard compliance, reducing lead time for future media converters by 50%. The simulation methodology is now scalable to other media converters and has been validated across multiple product fixtures, maintaining a 5% deviation between simulation and test results. This simulation-driven approach enhances durability, ensures compliance with stringent defense and industrial requirements, and reduces testing costs by 20–30%.This study underscores the critical role of vibration simulation in the development of rugged Ethernet-to-fiber converters, highlighting its value in improving product longevity, reliability, and performance in high-demand applications.
Presented By Mahendra Wankhede (Whirlpool of India Ltd.)
Authored By Mahendra Wankhede (Whirlpool of India Ltd.)Ketan Parashar (Whirlpool of India Ltd.)
AbstractWhirlpool Corporation, a global leader in home appliances with more than 110 years of innovation, is renowned for its iconic brands such as Whirlpool, KitchenAid, and Maytag. The company offers a diverse range of products, including refrigerators, washing machines, dishwashers and ovens. As consumer preferences increasingly lean toward safer and reliable home environments, enhancing the safety in appliances—particularly washers and dryers—has become a key priority in product design and development. Ventilation performance thus becomes a critical design parameter, particularly when CO₂ concentrations approach safety thresholds—a key concern in scenarios such as accidental child entrapment inside the drum. In an effort to enhance ventilation performance and identify cost-reduction opportunities without compromising system efficacy, a detailed investigation was conducted on a drum assembly exhibiting CO₂ levels near the upper permissible limit. A baseline Computational Fluid Dynamics (CFD) simulation was developed and experimentally validated to quantify the existing CO₂ distribution. Subsequently, a Design of Experiments (DOE) methodology was implemented, incorporating eight design variables—such as flapper positioning, dry channel state, inlet/outlet hose diameters, blower shell openings, condenser integration, exhaust hose presence, and drum perforations—across sixteen simulation runs.The parametric study enabled a systematic sensitivity analysis, revealing that the inclusion of a condenser subsystem was a dominant factor influencing CO₂ accumulation. This approach facilitated the identification of critical interactions and latent knowledge gaps in the current architecture. The insights gained not only informed ventilation system optimization but also supported design rationalization aligned with cost-reduction objectives, establishing a data-driven foundation for future design iterations.Keywords: Ventilation performance, Washer, Dryer, CO₂ concentration, O₂ levels, Computational Fluid Dynamics (CFD), Design of Experiments (DOE)
15:30
AbstractDesign improvement for chassis subframes is critical during the development process, particularly when failures occur in CAE simulations or actual fatigue testing. Traditional methods for enhancing structural performance relied on a hit-and-trial approach, which was time-intensive and often ineffective in achieving optimal results. To address this issue, a streamlined solution using Altair Optimization techniques was introduced. This approach involved targeting the localized regions experiencing fatigue failures, and analyzing them using static stresses obtained at the instance of damage. By improving the static strength of these local regions, the overall fatigue life of the subframe was significantly improved. However, the manual nature of this process, involving multiple sub-steps, led to lengthy optimization cycles of 2-3 weeks per design and was prone to errors from minor data inaccuracies. Automation was subsequently developed to minimize manual intervention and streamline the workflow. Using TCL scripting, the optimization setup was automated, enabling rapid improvement of subframe fatigue performance. This innovation reduced the optimization cycle to just 1-2 days, significantly enhancing efficiency while maintaining accuracy and reliability. This automated approach not only accelerates the design improvement process but also ensures consistent and robust results, offering a transformative advancement in subframe optimization practices. It demonstrates the potential of automation in addressing complex engineering challenges with precision and speed.
Authored & Presented By Harsh Sharma (SCALE GmbH)
AbstractSimulation-driven product development involves numerous CAE model iterations, where each version represents a critical difference. Usually, these multiple model versions are generated by hundreds of simulation engineers working in teams distributed across the globe, making functional collaboration a key for effective product development. To manage vast amounts of CAE data generated by engineers working simultaneously on a project, it is imperative to have a robust version management system to track changes in the CAE data. A robust version management is the backbone of an effective simulation data management (SDM) system. It involves capturing and documenting model changes at every design iteration. An accurate documentation of the model changes is crucial as it helps in understanding the model evolution and collaboration among engineers. However, documenting is usually considered a boring and tedious task by many engineers. This often leads to bad change documentation, which in turn reduces the data discoverability and causes knowledge loss. With the onset of AI in engineering simulations, engineers can now learn more from their simulation data. In this paper, authors have explored an AI-assisted approach for facilitating the change documentation by augmenting the change comments via automatically extracted details, as studied in the SAFECAR-ML research project. A goal of SAFECAR-ML is to develop an AI model that understands the nature of design changes and thereupon automatically generates change descriptions. When a detailed and informative change documentation is available, LLM-based generative AI can be used for discovering and creating simulation-related content in an SDM system, for example by using RAG approaches. A long-term outlook is to build an AI-assisted capability to perform complex tasks in an SDM system, like search and summarisation of the data, automatic evaluation of simulation results, and research on thinking models for making recommendations on further model changes.
Authored By Richie Garg (Schneider Electric)Efrain Gutierrez (Schneider Electric) Ezequiel Salas (Schneider Electric) Michel Romero (Schneider Electric)
AbstractA 4000 A circuit breaker was tested for temperature rise under different setups. The main objective was to replace casted copper terminals with integrated heat sinks by extruded copper terminals with external aluminum heat sinks, which have a lower carbon footprint. However, a significant temperature variation was observed after repeating the same setups (up to 11°C), which difficulted to accurately assess the performance of the new terminals. To diagnose the issue, thermal and electromagnetic simulations were performed. From the simulations results, it was found that temperature variations could be caused by external air currents. This was supported by further in field investigation. Different air currents caused by opening doors and the laboratory ventilation system were causing air flow disruptions at random intervals producing natural convection mitigation on the circuit breaker busbars. The random nature of these effects made it difficult to identify the cause of the temperature variations. Moreover, the vertical configuration of the busbars of the breaker made them more susceptible to air flow disruptions. Finally, to avoid these effects, physical barriers and a horizontal busbar configuration were used in the circuit breaker. By using horizontal busbars and physical barriers (curtains and cardboards), a better air control was achieved. Moreover, this is also applicable for the standard vertical busbar configuration included in UL 489 and ANSI C37.50 standard. If we ensure a good air control, any busbar configuration should give consistent results.
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Presented By Bharath Kanaparthi (Dassault Systemes India Private Limited)
Authored By Bharath Kanaparthi (Dassault Systemes India Private Limited)Geetha Avula (Dassault Systemes India Private Limited) Dayanidhi Panda (Dassault Systemes India Private Limited) Emlin Vathsalan (Dassault Systemes India Private Limited)
AbstractMultidisciplinary Design Optimization (MDO) of an aircraft wing is very critical in the design stage of a civil aircraft to obtain enhanced performance with minimum possible structural weight. It involves interaction of aerodynamics and structures, wrapped around a process integration and optimization tool for exploring the optimal parameters and performance in a multidisciplinary environment. In practice, it is often observed that integration of multiple tools poses challenges that restricts engineers to explore the design space. This makes the process iterative, requiring manual intervention and therefore time consuming.The parametrized design variables considered in this study for the aerodynamic optimization of the wing planform are wing area, aspect ratio and taper ratio. The design variables for the structures are the thickness of spar, rib, stringers and skin. The specified constraints are the fuel volume dominated by the space between front and rear spar of the wing, the permissible stress and buckling factor. The location of the ribs and spars have also been parametrized. The aerodynamic loads for the cruise angle of attack are computed using CFD on the CAD models generated in the design space using CATIA Apps and transferred to the structural mesh model for further stress and buckling analyses on using SIMULIA Apps on the 3DEXPERIENCE platform. The optimization by design of experiments is carried out in the form of a nested approach i.e.: the structural optimization within an Aerodynamic optimization loop. The set objective has been to obtain optimal performance of a wing with a maximum Lift to Drag ratio with minimum structural weight subject to the fuel volume, stress and buckling constraints. The present abstract explains in detail the developed MDO system based on the seamless integration of CAD-CFD-FEA tools to obtain the optimal design parameters meeting all the necessary constraints within the 3DEXPERIENCE Platform.
Authored & Presented By Sudersan Sridhar (SLB)
AbstractMETA Casing Reconnect system offers a metal-to-metal, gas-tight casing repair system for efficient well intervention in the Oil and Gas operations. Traditionally reliant on legacy designs and repetitive Finite Element Analysis (FEA) studies, the Casing Reconnect design cycle can be significantly improved with the automated FEA workflow. Simulation Process and Data Management (SPDM) methodology using modeFrontier and Volta is used to democratize FEA simulations thus opening the possibility of performing design optimization through Design of Experiments (DOE) and sensitivity analyses. The final deliverable is provided as a Runbox to the stakeholders eliminating the need for active intervention of a FEA engineer during the design process. The workflow encompasses parametrized model creation to report generation thus streamlining design and validation. Each iteration of this automated workflow significantly improves efficiency and cost-effectiveness, thus reducing product costs. This project underscores organization's commitment to sustainability by championing a simulation-based design approach, significantly reducing the need for physical testing and minimizing environmental impacts.
Presented By Srinivas Tangirala (Pilabz Electro Mechanical Systems Pvt. Ltd)
Authored By Srinivas Tangirala (Pilabz Electro Mechanical Systems Pvt. Ltd)Radhakrishnan Ramdasa, Kannan Lakshminarayanana, Supreet Sultanpura, Surya Narayan (Center for Excellence in Energy and Telecommunications (CEET))
AbstractThe design of electric motors for electric vehicles (EVs) presents unique challenges due to the direct interdependence between motor performance parameters and vehicle dynamic requirements. Traditional motor design approaches typically begin with an assumed motor geometry, which is iteratively refined through successive simulations to meet target speed and torque specifications. This method, however, can be time-consuming, resource-intensive, and susceptible to suboptimal outcomes. To address this, a physics-based, vehicle dynamics-driven methodology is proposed, wherein key motor performance parameters — including torque and speed demands — are directly derived from the assumed vehicle specifications which include its gross weight, wheel radius, the battery voltage, and the frontal area to achieve the target gradeability as per Automotive research association of India (ARAI). The resulting envelope of torque-speed values forms the boundary conditions and constraints for the subsequent motor design process. A case study involving the dynamic performance simulation of a commercial electric vehicle, the Tata Nexon EV, was conducted to benchmark the proposed methodology. The simulated torque and speed demands showed good agreement with the datasheet specifications of the vehicle’s production motor. Based on these performance requirements, an internal permanent magnet synchronous motor (IPMSM) was designed whose geometry was derived from the results of vehicle dynamic simulations tailored to encompass the torque-speed envelope. A detailed two-dimensional axisymmetric finite element method magnetic (FEMM) simulation of the designed IPMSM was performed to estimate its efficiency, the winding temperature, and its torque-speed curve. Maximum torque trajectory and the maximum torque per Ampere (MTPA) table was derived to help facilitate the development of a control system for the designed motor.
Presented By Dheeraj Kapse (CNH Industrial Technology Services (India) Private Limited)
Authored By Dheeraj Kapse (CNH Industrial Technology Services (India) Private Limited)Stefano Largo (CNH Industrial Italia SPA) Alessio Serracca (CNH Industrial Italia SPA)
AbstractWith the growing demand for lightweight, high-performance components in industries such as aerospace, automotive, and biomedical, lattice structures have emerged as a viable solution, offering a superior strength-to-weight ratio. This paper presents 2 state-of-the-art end-to-end workflows for the generation of topologically optimized lattice structures that not only meet mechanical performance targets across duty cycles but also adhere to Additive Manufacturing (AM) constraints.First workflow is suitable in the scenario when part design is unavailable. It starts with defining a raw design space to generate an optimised lattice design confirming duty cycle and printing feasibility. This process has flexibility of utilizing NURBS (Non-Uniform Rational Basis Spline) curve for generating smooth geometry encompassing Lattice.Second workflow is suitable for converting an actual production part into a lattice part using Field Driven Design analogy. This process is suitable for parts where original production part is available and the user has lot of design constraints.The proposed workflows integrate advanced topology optimization algorithms with nature inspired lattice generation techniques. By coupling finite element analysis (FEA) with optimization routines, the structure is first optimized for stiffness, strength, and fatigue over the defined duty cycle. This optimized topology with added known Factor of Safety is then translated into manufacturable lattice geometries.Further, the workflow incorporates build orientation optimization and print simulation to validate manufacturability and minimize print-induced distortions. The results are validated through case study on polymer-based additive manufacturing (3D Printing) platforms.This contribution showcases a comprehensive and robust design-to-manufacturing pipeline that can be readily adapted across industries seeking to harness the full potential of topology-optimized lattice structures, ensuring performance fidelity and print reliability.Keywords: Lattice, Additive Manufacturing Simulation, 3D Printing, NURBS
AbstractAutomotive OEMs can derive significant cost savings by reducing the quantity of physical crash tests and thereby accelerate product development, when they follow the Euro NCAP Virtual Testing guidelines. It helps in optimizing the overall vehicle development process via more efficient simulations, as well as facilitates in early adoption of new safety regulations. In this pursuit, companies must comply with strict Euro NCAP requirements, which includes transparency and traceability of virtual tests. A major challenge therein is model validation – which requires highly precise detailing and extensive use of data for accurately replicating real physics of the problem. Deploying these workflows into an existing simulation process can be a complicated and time-consuming task, particularly when integrating various simulation and testing methods. A powerful simulation and process data management system (SPDM) can thereby assist companies to automate their entire simulation process, ensures transparency for all stakeholders and optimizes the collaboration experience. In this paper, authors demonstrate how companies can use a SPDM system to integrate Virtual Testing into their simulation workflows, ensuring end-to-end automation, comprehensive documentation, and maximum transparency. Various aspects of Virtual Testing can be efficiently managed within SPDM system - definition and tracking of project requirements, efficient management of model data, automatic simulation setup, transparent analysis of results and generation of interactive web reports consisting of Virtual Testing specific checks, which drastically reduces engineer’s manual effort, followed by a secured and efficient transfer of data to Euro NCAP web portal. Protecting input and output data against manipulation is a key concern in an industrial level Virtual Testing process, which is addressed via automatic hash generation for the simulation data. The process of making data tamper proof can be managed and tracked within a SPDM system, which increases protection of intellectual property and ensures confidence in simulation results.
Presented By Vishal Mahajan (Mobis India Limited)
Authored By Vishal Mahajan (Mobis India Limited)Upendra Chilakamarri (Mobis India Limited)
AbstractIn the realm of high-speed printed circuit board (PCB) design, maintaining signal integrity and ensuring reliable data transmission are paramount. This paper explores the integration of advanced equalization techniques with signal integrity simulation methods to address the challenges posed by high-speed data communication.Advanced equalization techniques, such as Feedforward Equalization (FFE), Decision-Feedback Equalization (DFE), and Continuous-Time Linear Equalization (CTLE), are critical for mitigating signal degradation and noise in high-speed PCBs. These techniques enhance the performance of communication channels by compensating for losses and distortions, thereby improving signal quality and reliability.Complementing these techniques, signal integrity simulation plays a vital role in predicting and analysing the behaviour of high-speed signals. By employing simulation tools, designers can evaluate the effectiveness of various equalization methods and optimize PCB layouts to minimize signal integrity issues such as crosstalk, reflection, and electromagnetic interference (EMI).This paper presents a comprehensive study of the combined application of advanced equalization and signal integrity simulation techniques. Through detailed analysis and practical case studies, we demonstrate how these methods can be synergistically employed to enhance the performance of high-speed PCBs.
Presented By Koteswara Rao Gochika (Whirlpool)
Authored By Koteswara Rao Gochika (Whirlpool)Ratikanta Dehury (Whirlpool) Sankarshan Verma (Whirlpool)
AbstractThe knowledge of the bulk behavior of powder, such as bulk density, flowability, and tapped compressibility, is imperative to efficiently design powder processing machines and plants. These bulk powder properties in-turn depend on several particle-level properties such as particle density, particle mechanical properties, particle size distribution, particle-to-particle friction, etc. Therefore, the bulk behavior of the powder can be optimized by tinkering the particle level properties. However, the techniques to measure the particle-level properties could be expensive or inaccessible for everyone.The objective of this study is to develop an alternative approach or technique to estimate the particle-level properties of the powder. In the current work, the authors have developed a methodology that employs a simulation model made in LS-Dyna software using the Discrete Element Method (DEM) technique and Six Sigma tools. This model takes as input the bulk density, tapped density, and particle size distribution of a powder and provides an estimate of three important particle level parameters, namely the particle’s density, the particle’s Young’s modulus, and the particle’s Poisson’s ratio.Hence, a relationship can be built between the particle’s density, the particle’s Young’s modulus, and the particle’s Poisson’s ratio to bulk powder properties like the bulk density, tapped density, and particle size distribution. These bulk powder properties are easy and cost-effective to measure, compared to the particle-level properties. For instance, using this model the particle level properties can be estimated with an average simulation run time of 1 hour (with 16 processors).The authors have also validated the model using two different regularly available powders i.e. table salt and instant coffee. The results and assumptions of the model are presented in detail. The validation of the results has been performed using a standard powder testing machine i.e. Hosokawa PTX.
Presented By Nagaraja Jade (Whirlpool of India)
Authored By Nagaraja Jade (Whirlpool of India)Amit Nikam (Whirlpool of India)
AbstractWhirlpool Corporation is a global leader in home appliances, with over 110 years of innovation. Known for its iconic brands like Whirlpool, KitchenAid, and Maytag, the company offers a wide range of products, including washing machines, refrigerators, dishwashers, and ovens.Brushless DC (BLDC) motors are widely used in modern washing machines due to their high efficiency, compact size, and precise speed control. However, electromagnetic noise generated during motor operation can contribute significantly to acoustic noise and affect the appliance’s performance and user comfort. This study focuses on the prediction and analysis of electromagnetic noise in a washing machine BLDC motor using finite element analysis (FEA) and electromagnetic simulation techniques. Multi-physics simulation methodology has been developed to predict the noise radiation from the motor. This case study presents a multidisciplinary methodology to predict the electromagnetic noise of a BLDC motor by coupling Electromagnetics and Vibro-acoustics. The study also explores mitigation strategies by considering the controllable electrical, physical and mechanical properties of the rotor and stator assembly. The parameters such as Magnet RFD, Magnet thickness, Winding resistance, Slot opening of stator and rotor, and Motor mass are considered. Design of Experiment (DOE) analysis has been performed with two-level settings to understand the effect of potential factors on the sound power level (SWL) of the motor. The different statistical tools, such as the Pareto chart and interaction effect plots used to understand the factors having higher and lower effects on the SWL of the Motor. The optimized solution has been calculated using Regression Analysis and Monte Carlo simulations. This proposed optimized solution has a reduction of 5 dBA in motor noise level compared to the baseline design. This work provides valuable insights for designers aiming to develop quieter and more efficient motor-driven appliances.