Electric Vehicle Motor Magnetic Noise Reduction By Integrating Electromagnetic Simulation and Machine Learning

Presented By
Vinothini Gurudevan (Valeo India Private Limited)

In the present day, Permanent magnet synchronous motors (PMSM) are increasingly being utilized in Electric vehicle applications. These vehicles require very silent electrically driven compressors (EDC) as whining noises of motors are not preferred by customers. Despite that the PMSM motors are effic...Read Full Abstract

In the present day, Permanent magnet synchronous motors (PMSM) are increasingly being utilized in Electric vehicle applications. These vehicles require very silent electrically driven compressors (EDC) as whining noises of motors are not preferred by customers. Despite that the PMSM motors are efficient and known as silent at low speed the usage at high speed will lead to high noise “electromagnetic noise”. The conventional use of electromagnetic simulation offers comprehensive insights into magnetic noise but prioritizes precision over efficiency. To enhance the efficiency of this traditional approach, the concept of machine learning presents distinct avenues for enhancing simulation capability

This paper proposes a unique technique which reduces Stator force harmonic distortion in Back Electromotive force and fundamental harmonics amplitude in PMSMs. Machine learning (ML) techniques like neural network, FEA Simulation & Adaptive meta model for optimum prognosis (AMOP) optimization are explored for adaptive harmonic reduction in Fast Fourier transform (FFT). The Electromagnetic (EM) simulation provides an intricate understanding of the magnetic noise within the motor, encompassing magnetic flux lines and nodal force distribution. Building upon this physical understanding, the ML methods optimize rotor & stator profile in real-time to dynamically minimize harmonic distortion by training predictive models of enormous datasets on motor operating conditions.

The simulation and experimental results demonstrate that the proposed technique effectively reduces Total Harmonic Distortion (THD), thereby significantly enhancing efficiency and minimizing electromagnetic noise in the EDC system. These findings highlight the influence of key design parameters, such as the non-uniform air gap and flux barrier shape. Machine learning & AMOP framework used in the design and analysis of the motor remain majorly important in enhancing the performance of PMSMs. This innovative approach represents a promising step towards expediting the design and optimization of motors contributing to faster development cycles of sustainable energy technologies. Hide Full Abstract

Authored By
Vinothini Gurudevan (Valeo India Private Limited)
Mohamed , Khanchoul, mohamed.khanchoul@valeo.com Valeo,France, System Architect
Ilakya, Elumalai, ilakya.elumalai@valeo.com Valeo India Private Limited, Senior Engineer
ManojKumar, Shankar, manojkumar.shankar@valeo.com Valeo India Private Limited, Senior Engineer

Future-oriented lab data empowering Digital Twins

Authored & Presented By
Sridhar K (Schneider electric private limited)

In 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 demand...Read Full Abstract

In 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. Hide Full Abstract

Enabling Advanced Structural Modelling and Simulation (M&S) as Certification Means of Compliance in Aerospace

Authored & Presented By
Jaya Raju Namala (Airbus Group India Private Ltd)

This 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 p...Read Full Abstract

This 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. Hide Full Abstract

Tractor muffler design and simulation for transmission Loss (TL) evaluation and Back Pressure

Presented By
Vinay Yadav (Tafe Motors and Tractors Limited (TMTL))

Technology 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 t...Read Full Abstract

Technology 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. Hide Full Abstract

Authored By
Vinay Yadav (Tafe Motors and Tractors Limited (TMTL))
Tarun, Nema, tbnema@tmtl.co.in Tafe Motors and Tractors Limited (TMTL), Assistant Manager (CAE analyst)
Pawan , Singh, pawan1@tmtl.co.in Tafe Motors and Tractors Limited (TMTL), Assistant General Manager
Sanjiv , Pathak, skpathak@tmtl.co.in Tafe Motors and Tractors Limited (TMTL), Senior Manager
Hemant , Shrikhande , hvshrikhande@tmtl.co.in Tafe Motors and Tractors Limited (TMTL), Vice President

Testing and Electromagnetic simulations Circuit Breaker Busbar Architecture

Presented By
Richie Garg (Schneider Electric)

Skin effect is the nature of AC to move at the periphery of a busbar results in supplementary power losses compared to DC. Proximity effect is the disturbance current distribution of the busbar, if it is under the influence of a magnetic field, other than its own. The proximity effect is of three ki...Read Full Abstract

Skin effect is the nature of AC to move at the periphery of a busbar results in supplementary power losses compared to DC. Proximity effect is the disturbance current distribution of the busbar, if it is under the influence of a magnetic field, other than its own. The proximity effect is of three kinds, direct, inverse or reverse and induced. Direct proximity effect is when the busbars in vicinity carry current in same direction. Inverse proximity effect is when busbars in vicinity carry current in opposite direction. Induced proximity effect is interaction between the current flowing in the busbar and the induced current in neighboring metallic parts. The temperature rise induced in the busbar could damage the insulators holding them up. In a 3-phase busbar, with many busbars per phase, these three proximity effects overlap. Electromagnetic simulations are performed in ANSYS Maxwell 3D to quantify the AC current per busbar per phase. Test is also performed to quantify current and temperatures. The aim of the study was to test the performance of NS breaker terminals. Hide Full Abstract

Authored By
Richie Garg (Schneider Electric)
Efrain , Gutierrez, efrain.gutierrez@se.com, 07045330842, Schneider Electric, Electromechanics/Electrotechnology, Experienced Principal Technical Expert

Application of Machine learning in Aerospace Structure Engineering and Its Future Directions

Presented By
Sharad Anand Shivaprasad (Collins Aerospace)

This 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 th...Read Full Abstract

This 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. Hide Full Abstract

Authored By
Sharad Anand Shivaprasad (Collins Aerospace)
Sushree Kshirabdhi , Tanaya, SUSHREEKSHIRABDHI.TANAYA@collins.com, 9019165864, Collins Aerospace, Lead Engineer(Advanced Structures)

Use Of Transient Dynamic Simulations For Damage Prediction Of Aircraft Composite Structures For Bird Strike And Tyre Debris Impacts

Authored & Presented By
Abhijit Chaudhuri (Airbus)

Airbus 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 s...Read Full Abstract

Airbus 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. Hide Full Abstract

Aid of CFD simulations in safe and efficient thermal management of IT servers in data centre cooling

Presented By
Ankit Khandelwal (ITB Engineering services private limited)

Modern 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 ...Read Full Abstract

Modern 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. Hide Full Abstract

Authored By
Ankit Khandelwal (ITB Engineering services private limited)
Yannick, Lattner, Yannick.Lattner@itb-fem.de, +49 (0)231 94 53 65 - 36, ITB Ingenieurgesellschaft f&
, 252
r technische Berechnungen mbH, Team Lead CFD

Parameter Identification For Nitinol Shape Memory Alloy Modeling Via Stress-Strain Curve Optimization

Presented By
Bhanu Pratap Reddy (Stryker Global Technology Centre)

Shape 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 reliab...Read Full Abstract

Shape 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. Hide Full Abstract

Authored By
Bhanu Pratap Reddy (Stryker Global Technology Centre)
Pranshu Rajput (Stryker Global Technology Centre)

Hybrid Backstepping and Sliding Mode Control with MPDT thruster as actuator for Attitude correction in CubeSats

Authored & Presented By
Akash C (Hindustan Institute of Technology and Science)

CubeSats have transformed access to space with their low cost and modular architecture.

However, achieving precise and reliable attitude control remains a persistent challenge due to

the spacecraft’s nonlinear dynamics, limited actuation capabilities, and susceptibility to
...Read Full Abstract

CubeSats have transformed access to space with their low cost and modular architecture.

However, achieving precise and reliable attitude control remains a persistent challenge due to

the spacecraft’s nonlinear dynamics, limited actuation capabilities, and susceptibility to

external disturbances such as gravity gradient and solar radiation pressure. Traditional control

strategies like PID often underperform in such environments due to their inability to handle

nonlinearities and uncertainties effectively.

This paper presents a hybrid control approach that combines Backstepping Control (BSC) and

Sliding Mode Control (SMC) to achieve robust and accurate attitude correction in CubeSats.

The backstepping framework ensures stability for the nonlinear attitude dynamics, while

sliding mode control enhances robustness against external disturbances and modelling errors.

To overcome the chattering effect commonly associated with SMC, a boundary layer

technique is introduced.

Simulation results, based on a 3-axis CubeSat model implemented in MATLAB/Simulink,

demonstrate significant improvements over traditional PID and standalone nonlinear

controllers. The hybrid controller achieves a low tracking error, reduces convergence time and

improves energy efficiency. These outcomes highlight its suitability for real-world applications

such as Earth observation, deep space missions, antenna pointing, and formation flying where

precise orientation is critical.

The proposed hybrid BSC-SMC strategy thus offers a viable solution for CubeSat attitude

control, balancing nonlinear system handling, disturbance rejection, and hardware

constraints. Future work includes onboard implementation and adaptive tuning to optimize

performance under real-time operational conditions. The MPD Thruster is used as actuator.

The thruster’s performance is studied w.r.t CubeSat. Hide Full Abstract

Powering Future of Flight – Challenges and Opportunities in Next Generation Simulation Technologies

Authored & Presented By
Uma Maheshwar (BEC - GE Aerospace)

At 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 technologie...Read Full Abstract

At 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. Hide Full Abstract

Leveraging Finite Element Simulation and Machine Learning Algorithm for Reaction Force Prediction of Solenoid Bracket Plate

Presented By
Mayur Sabale (Knorr-Bremse Technology Center India Pvt. Ltd.)

Over 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 exp...Read Full Abstract

Over 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. Hide Full Abstract

Authored By
Mayur Sabale (Knorr-Bremse Technology Center India Pvt. Ltd.)
Pradip, Sontakke, pradip.sontakke@knorr-bremse.com, 7709928400, Knorr-Bremse Technology Center India Pvt. Ltd., Senior Engineer
Shashank, Borde, shashank.borde@knorr-bremse.com, 8378986770, Knorr-Bremse Technology Center India Pvt. Ltd., R &
D Manager

Machine Learning-Enhanced Reduced Order Modeling for BIW Design Optimization: A Study in Frequency Response Reduction

Presented By
Vidit Sharma (ESTECO India Software PVT. LTD)

This 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, p...Read Full Abstract

This 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. Hide Full Abstract

Authored By
Vidit Sharma (ESTECO India Software PVT. LTD)
Anuj, Shukla, shukla@esteco.com ESTECO India Software PVT. LTD, Senior Application Engineer
Eshan, Amalnerkar, amalnerkar@esteco.com ESTECO India Software PVT. LTD, Application Engineer

Measuring Strain Distribution in Tensile Test Specimen Finite Element Model and DIC Experimental Data

Presented By
Mohit Kumar (Stryker Global Technology Center)

Accurate 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...Read Full Abstract

Accurate 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. Hide Full Abstract

Authored By
Mohit Kumar (Stryker Global Technology Center)
Devesh, Bhatia, devesh.bhatia@stryker.com Stryker Global Technology Center, Sr. Engineer
Lakshmanan, chettiar, chettiar.ramanathan@stryker.com, 09821747480, Stryker Global Technology Centre, Sr. Software Engineer
Sreekanth, Padmanabhan, sreekanth.01@stryker.com, 08600100874, Stryker Global Technology Center, Principal Engineer
Venkateswaran, Perumal, venkateswaran.perumal@stryker.com, 09632043715, Stryker Global Technology Center, Director - R&
amp
D (Simulation)

Reimagining Crash Simulations with AI: Transforming Legacy Data into Scalable Surrogate Models

Presented By
Celal Karadogan (Ansys)

Artificial 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 physi...Read Full Abstract

Artificial 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. Hide Full Abstract

Authored By
Celal Karadogan (Ansys)
Shyam, Vasu, shyam.vasu@ansys.com Ansys, Senior Application Engineer

Non-dimensional model for pressure drop prediction across spring loaded valve

Authored & Presented By
Vivek Tripathi (Atmus Filtration Technologies)

Atmus 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 phenom...Read Full Abstract

Atmus 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. Hide Full Abstract

Fluid Structure Interaction Analysis for Hyper Elastic Umbrella Valves

Authored & Presented By
Navin Surana (Atmus Filtration Technologies)

Atmus Filtration Technologies is a global leader in filtration solutions for on-highway commercial vehicles and off-highway vehicles and equipment. Hyper elastic umbrella valves are used in many applications including Fuel Water Separators. It is important to evaluate pressure drop performance of th...Read Full Abstract

Atmus Filtration Technologies is a global leader in filtration solutions for on-highway commercial vehicles and off-highway vehicles and equipment. Hyper elastic umbrella valves are used in many applications including Fuel Water Separators. It is important to evaluate pressure drop performance of these valves to select the right valve for the application. When the valve is installed, it is seated on the valve seat with a contact pressure and prestress. In CAD that is represented by an interference with the valve seat. The valve was analyzed in fully coupled fashion using system coupling tool, fluid and mechanical solvers of a commercially available software. The tensile and compression uniaxial stress strain data were used for modeling the valve material using Mooney Rivlin 3 parameters model. With the contact and small gaps to be considered in the analysis dynamic meshing with smoothing and remeshing is required. FSI analysis with the remeshing sometimes is tricky to get the converged solution. Hence a novel approach of overset meshing was used to avoid the issues of dynamic mesh failure and convergence with only a minimal loss of accuracy. The overset approach eliminates the need of remeshing and hence is easier to get convergence and less computationally expensive. Hide Full Abstract

Beyond Software Training: Building Fundamental FEA Understanding for Marine Structural Engineers

Presented By
Sai Sabarish M (Warship Design Bureau, Indian Navy)

Ship structural engineering and design have traditionally relied on analytical methods such as first-principles or standard class rules. Over the past two decades, FEA has become integral to mainstream design processes. With advances in the computation power and technology, FEM is now regarded as on...Read Full Abstract

Ship structural engineering and design have traditionally relied on analytical methods such as first-principles or standard class rules. Over the past two decades, FEA has become integral to mainstream design processes. With advances in the computation power and technology, FEM is now regarded as one of the most reliable methodologies for assessing the structural parameters.

However, the increasing complexity of FEA has revealed significant skill gaps among engineers, presenting a persistent challenge in the structural analysis workflows. It has been observed that, despite holding undergraduate or graduate degrees, many engineers lack a fundamental understanding of FEA concepts - such as discretisation, mesh refinement, realistic applications of loads and boundary conditions, and theories behind niche yet vital analyses. Furthermore, knowledge silos within teams - a byproduct of divided responsibilities – contribute to a lack of understanding of the broad context across roles.

This paper examines these issues, with particular focus on conducting FEA using the Ansys Software Suite for marine structural applications, and proposes a methodology to address them and enhance both efficiency and effectiveness. To bridge these gaps, we introduced a structured competency matrix, enabling systematic assessment and targeted upskilling across all team roles. The framework employs standardised metrics to evaluate each individual’s proficiency, both in their specific role and in their understanding of other roles. Areas assessed include geometry preparation, meshing, boundary conditions, result validation, and foundational engineering knowledge. Targeted training modules are then implemented to address identified weakness, fostering cross-disciplinary understanding and collaboration.

Implementation of this approach can result in measurable improvements, including significant reductions in modelling errors and analysis time, as well as enhanced reliability. The paper outlines a scalable methodology for ongoing skill assessment, role-appropriate training and continuous validation, ensuring that engineering teams remain current with evolving industry standards and simulation technologies in marine engineering field. Hide Full Abstract

Authored By
Sai Sabarish M (Warship Design Bureau, Indian Navy)
Pinaki, Kaushal, pinaki0032@gmail.com, 8427913001, Warship Design Bureau, Indian Navy, Lt (WDB) - Structures

3D CFD Simulation Methodology For Safe And Efficient Hydrogen Refueling For Hydrogen Internal Combustion Engines

Presented By
Gourav Khanna (Cummins Technologies India Pvt Ltd)

As conventional fossil fuels are on the verge of depletion, the search for alternative fuel for automotive applications has intensified. Among these, hydrogen stands out due to its high energy content per unit mass, high octane number, and compatibility with Internal combustion engines (ICE). Howeve...Read Full Abstract

As conventional fossil fuels are on the verge of depletion, the search for alternative fuel for automotive applications has intensified. Among these, hydrogen stands out due to its high energy content per unit mass, high octane number, and compatibility with Internal combustion engines (ICE). However, the volatility of hydrogen (H2) presents challenges, particularly during the refueling process, where uncontrolled temperature rise occurs because of negative Joule-Thomson (JT) effect. This brings an alarming bell for the safety of fueling stations, vehicles and mankind.



This paper investigates the physics involved in hydrogen tank filling, focusing on maintaining the hydrogen gas temperature below 85 °C during the process. A 3D Computational Fluid Dynamics (CFD) analysis was performed to model the temperature and pressure behavior of hydrogen during filling. The study provides insights into the optimal fill rates, temperature distribution, and the evolution of peak temperature locations inside the tank, contributing as a critical dataset for safe and efficient hydrogen refueling strategies.



This study was carried out using two simulation software - ANSYS Fluent 2024R1 and Simerics 6.0.0. The results from both the software show strong agreement, while Simerics demonstrated a significant computational advantage with runtimes as compared to Ansys Fluent. Additionally, a mesh and time-step sensitivity study were conducted to ensure the accuracy and stability of simulations.



Key words: CFD - Computational Fluid Dynamics, H2 – Hydrogen, ICE - Internal combustion engine, JT - Joule-Thomson. Hide Full Abstract

Authored By
Gourav Khanna (Cummins Technologies India Pvt Ltd)
Swati, Veerbhadra, swati.veerbhadra@cummins.com, 8788856759, Cummins Technologies India Pvt Ltd, Thermal and Fluid Systems Engineer
Abhay, Sahu, abhay.sahu@cummins.com, 9992555095, Cummins Technologies India Pvt Ltd, Thermal and Fluid Science Engineer - Group Leader

Enhancing HEVs Battery Performance, Reliability & Lifespan Through A Novel Battery Thermal Management Concept By Leveraging CFD Simulation Techniques

Authored & Presented By
Vinay Kumar (Hyundai Mobis India ltd.)

As 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...Read Full Abstract

As 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. Hide Full Abstract

Simulation driven tool for predicting the structural integrity of spin-on filters.

Authored & Presented By
Dinesh Lonkar (Atmus Filtration Technologies Inc.)

Atmus 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 re...Read Full Abstract

Atmus 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. Hide Full Abstract

Process Development for reduced variation in suspension level performance using MORDO method

Presented By
Jugal Pathak (Mahindra & Mahindra)

Competition in automotive industry is fierce and manufacturers must meet the needs of their costumers to maintain or increase their market. It is important for Original Equipment Manufacturers (OEMs) to create affordable vehicles with better driving experiences, comfort for occupants and safety for ...Read Full Abstract

Competition in automotive industry is fierce and manufacturers must meet the needs of their costumers to maintain or increase their market. It is important for Original Equipment Manufacturers (OEMs) to create affordable vehicles with better driving experiences, comfort for occupants and safety for costumers. Suspension architecture plays critical role in vehicle ride, handling and stability behavior and is directly connected with driving experience. Incorrectly positioned hardpoints can significantly affect the performance of a vehicle. Consequently, recommended tolerances for suspension hardpoints positioning are made to avoid deterioration of Kinematics and Compliance (K&C) parameters. Therefore, part level tolerance design is critical in vehicle development to minimize production cost and reduce vehicle performance deviation. The purpose of this paper is to develop a process for identifying robust design solution in terms of architectural hardpoints for MacPherson type front suspension without compromising K&C parameters. Multi-objective Robust Design Optimization (MORDO) method has been adopted to assess the variation in performance parameters for given tolerances in suspension hardpoints. K&C analysis is carried out using ADAMS/CAR software and Multi-Objective Robust Design optimization is executed using Mode Frontier.

Keywords: Suspension part Tolerance, Kinematics and Compliance (K&C), Multi-objective Robust Design Optimization (MORDO) Hide Full Abstract

Authored By
Jugal Pathak (Mahindra & Mahindra)
Ganesh, Lingadalu, GANESH.LINGADALU@mahindra.com Mahindra &
amp
Mahindra, Senior Lead Engineer - CAE Vehicle Dynamics

A Brief Study Of Parametrization In CFD For Optimized Results

Authored & Presented By
Prem Chinna Polamada (Mobis india limited)

Thermal 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 des...Read Full Abstract

Thermal 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. Hide Full Abstract

Design And Analysis Workflows For Topologically Optimized Lattice Structures Generation Conforming Duty Cycle And Printing Feasibility

Presented By
Dheeraj Kapse (CNH Industrial Technology Services (India) Private Limited)

With 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 ge...Read Full Abstract

With 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 Hide Full Abstract

Authored By
Dheeraj Kapse (CNH Industrial Technology Services (India) Private Limited)
Stefano, Largo, stefano.largo@cnh.com CNH Industrial Italia SPA, Material Sim DB Design Analysis Engineer
Alessio, Serracca, alessio.serracca@cnh.com CNH Industrial Italia SPA, Manager Structure &
amp
Durability Analysis

Numerical Simulation Methodology For Thermoforming Manufacturing Process For Precisely Evaluating Thickness Variation

Presented By
Dheeraj Kapse (CNH Industrial Technology Services India Pvt Ltd)

Thermoforming 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 investigati...Read Full Abstract

Thermoforming 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 Hide Full Abstract

Authored By
Dheeraj Kapse (CNH Industrial Technology Services India Pvt Ltd)
Raffaele, Caggiano, raffaele.caggiano@external.cnh.com CNH Industrial Italia SPA, Design Analyst
Stefano, Largo, stefano.largo@cnh.com CNH Industrial Italia SPA, Material Sim DB Design Analysis Engineer
Alessio, Serracca, alessio.serracca@cnh.com CNH Industrial Italia SPA, Manager Structural &
amp
Durability Analysis