Browse Topic: Simulation and modeling

Items (27,031)

A decision-making framework for field manufacturability of vehicle parts in expeditionary environments

2026-01-0143To be published on 04/07/2026

Expeditionary environments (such as remote exploration missions, forward military operations, and disaster response zones) demand adaptive manufacturing solutions to support vehicle sustainment in the absence of traditional supply chains. This work introduces a conceptual mathematical framework for modeling the constraints and trade-offs inherent to expeditionary manufacturing, with a focus on vehicle repair and spare parts fabrication using low-energy and simple automated systems including desktop-scale 3D printers and CNC machines. The model integrates key variables such as energy availability, material transport cost, fabrication time, and environmental limitations to support rapid decision-making on part manufacturability and in-field feasibility. A case study involving the on-demand production of some common wear and failure parts on a vehicle, including suspension components and the water pump, is used to demonstrate how this framework can guide the selection of suitable

Mollan, Calahan, Pandey, Vijitashwa, Patterson, Albert E.

A Study on Fatigue Life Prediction Method for Point-Based Joints Considering Multiple Fracture Modes – FDS, Blind Rivet, and Blind Nut Focus

2026-01-0180To be published on 04/07/2026

The application of light weight alloys for vehicle bodies such as aluminum alloys and composite materials are rapidly advancing to support the construction of multi-material vehicle bodies. These materials play a crucial role in reducing overall vehicle weight, enhancing fuel efficiency, and complying with increasingly strict environmental regulations. As the automotive industry continues to evolve toward electrification and sustainability, the integration of lightweight and high-performance materials has become a key design strategy. However, the use of multiple materials introduces new challenges in manufacturing, particularly in joining technologies. Since different materials have varying mechanical properties, thermal behaviors, and surface characteristics, selecting appropriate joining methods is essential to ensure structural integrity and durability. Depending on the material types, thicknesses, production processes, and cost constraints, various joining techniques—such as

Takuno, Sougo, Isono, Toshiyuki, Urakawa, Kazushi, Goto, Suguru, Kawamura, Hiroaki, Niisato, Eita, Ishigami, Yuta

Multi-Objective Reinforcement Learning Framework for Transmission Shift Schedule Optimization

2026-01-0162To be published on 04/07/2026

Building upon previous work that successfully employed a Reinforcement Learning (RL) agent for the autonomous optimization of transmission shift programs to enhance fuel efficiency , this paper addresses a critical limitation of that approach: the neglect of human-centric factors. While the prior methodology achieved substantial fuel consumption reductions by training an RL agent in a Software-in-the-Loop (SiL) environment, it did not explicitly account for aspects such as driver comfort and preferences, which are paramount for realworld user acceptance and drivability. This work presents a multi-objective optimization framework extending the artificial calibrator to simultaneously maximize fuel efficiency and enhance driver comfort. The method introduces a modified RL reward function that penalizes undesirable shift behavior to ensure a smooth driving experience (drivability). This new methodology also incorporates a mechanism to capture and integrate driver preferences, moving beyond

Kengne Dzegou, Thierry Junior, Schober, Florian, Rebesberger, Ron, Henze, Roman, Sturm, Axel

Mathematical Modeling of Trust Calibration for Human–Automation Safety

2026-01-0530To be published on 04/07/2026

Trust between humans and automated vehicles is increasingly recognized as a pivotal factor in achieving safety in advanced vehicles and mobility environments. Poorly calibrated trust—either over-trust leading to complacency or under-trust leading to disengagement—has been identified in numerous incidents involving advanced driver assistance systems and autonomous functions. Yet, most current approaches to managing trust rely on surveys, heuristics, or qualitative assessments, providing little predictive capability for safety engineers and system designers. This paper presents a formal mathematical framework for trust calibration, modeling trust as a dynamic, evolving construct grounded in probabilistic reasoning. We represent trust as a Beta-distributed belief that updates over time through Bayesian inference, with parameters adjusted by observed successes and failures, contextual cues, and system feedback signals. The model also incorporates forgetting and drift functions, allowing

Wen, He, Mounir, Adil

Benchmarking Driver Responses in Head-On Conflicts: Toward an Objective Protocol for Human and AV Evaluation

2026-01-0534To be published on 04/07/2026

Head-on emergency conflicts present a unique challenge for evaluating driver and automated vehicle (AV) performance. Unlike most emergency scenarios that follow a direct stimulus–response sequence, head-on encounters are characterized by a stimulus–wait–response pattern in which time to contact (TTC) strongly shapes driver behavior. Drawing on naturalistic and simulator data, this study quantified human responses to head-on conflicts. When the opposing vehicle crossed the centerline with more than 2 seconds of TTC, drivers delayed braking until an average of 1.3 seconds (SD = 0.8) before impact, regardless of the timing of the intrusion. By contrast, when TTC was less than 2 seconds, braking was initiated only 0 to 1.0 second before impact. As a result, many drivers applied the brakes less than half a second before collision. These findings provide a meta-analytically grounded benchmark for comparing human behavior to AV decision-making processes and demonstrate the need for conflict

Muttart, Jeffrey, Dinakar, Swaroop, Maloney, Tim, Adikhari, Bikram, Gernhard-Macha, Suntasty

The Characterization of Lumbar Acceleration in Rear-End Impacts Across Different Surrogate Occupant Types

2026-01-0552To be published on 04/07/2026

Longitudinal lumbar acceleration is often overlooked as a key variable when biomechanically assessing lumbar response in rear-end collisions. Previous studies have assessed lumbar accelerations for occupant types individually, but comparative analyses between surrogate occupant types have been limited. Human volunteers best reflect kinematics in real crash scenarios but are limited to low-severity impacts due to ethical limitations. Post-mortem human subjects (PMHS) can be employed in higher speed impacts, however their lack of muscular input may not extensively capture occupant kinematics. Anthropomorphic test devices (ATDs) offer trial consistency and repeatability, however the widely used Hybrid III ATD's relatively simple lumbar spine may not fully emulate the human lumbar vertebrae. Based on these physiological differences between occupant types, there may be corresponding differences in lumbar spine acceleration that have not been objectively quantified. The aim of this study was

Zambare, Keya, Ogbu Felix, Jordan, Arana Barcala, Emily, Westrom, Clyde, Caraan, John, Adanty, Kevin, Shimada, Sean

Research on Parameter Matching and Control Strategy of Electric Semi trailer Power System

2026-01-0449To be published on 04/07/2026

Reducing carbon emissions is an important issue for environmental protection, while also saving fossil fuels for all mankind and preventing global warming. Therefore, reducing carbon emissions is of great significance. The carbon emissions in the transportation sector account for an important proportion of global carbon emissions, and the carbon emissions of heavy-duty commercial vehicles also account for an important proportion of the carbon emissions in the transportation sector. Therefore, the electrification of heavy-duty commercial vehicles is one of the important ways to save energy and reduce emissions for heavy-duty vehicles. Especially the research topic of this article: semi-trailer trains. Similar to passenger cars, semi-trailer trains also have two electrification ideas: hybrid and electric drive. Considering the high energy demand of heavy-duty commercial vehicles, the high cost of large capacity batteries, the current low level of development of electric heavy-duty

Zheng, Hongyu, Zhu, Kaixuan, Zhang, Yuzhou

A System of System Design Framework for Multi-Domain Vehicles

2026-01-0263To be published on 04/07/2026

A System of System Design Framework for Multi-Domain Vehicles - U.S. Army OPSEC approval has been received. Please find the approved Abstract for your consideration below : Traditionally, ground vehicle design is based on identifying engineering solutions that fulfil the requirements and specifications put forth by the stakeholders. Although a vehicle is a single entity, it is composed of many subsystems and thousands of parts that must operate together in unison to meet all design goals. A System of Systems (SoS) design approach enables the consideration of sub-system performance within a framework of overall system operation which includes possible trade-offs. This collaborative approach to subsystem and primary system design draws upon modelling, optimization, tradespace analysis and virtual studies. In this paper, a system of system design approach will be investigated for a collection of multi domain vehicles assembled to undertake coordinated search and rescue operations on land

Somanchi, Anang, Abeynayake, Chandima, deshmukh, Mrunal, Suresh, Johir, Ramnath, Satchit, Turner, Cameron, Schmid, Matthias, Castanier, Matthew P., Rapp, Stephen, Jaczkowski, Jeffery, Wagner, John

Real-Time Hardware-in-the-Loop Validation of Hybrid and Electric Vehicle Powertrain Systems

2026-01-0409To be published on 04/07/2026

This paper presents applied methodologies and results from real-time Hardware-in-the-Loop (HIL) testing of electrified powertrain systems, based on experience gained during advanced development programs in the electric vehicle (EV) sector. It outlines how HIL accelerated control software validation, enhanced fault coverage, and improved development cycles for critical powertrain components such as Vehicle Control Units (VCU), Inverters (INV), Chargers, DC-DC Converters (DCDC), and Electric Motors (EM). As electrified propulsion systems grow in complexity to meet performance, efficiency, and safety demands, robust and scalable validation techniques are essential. Modern EV powertrains integrate multiple ECUs managing distinct subsystems, requiring thorough testing of both individual components and their interactions. To address this, a real-time HIL test bench was developed to simulate a broad range of driving conditions, powertrain operating scenarios, and fault cases. The test bench

Sathiyamoorthy, Pradeepraja

Thermal Characterization of an Automotive High Voltage Junction Box

2026-01-0390To be published on 04/07/2026

The high voltage battery junction box (HVJB) controls and protects the high voltage connections of the battery pack to the traction, auxiliary and charging systems. HVJBs are composed of busbars, contactors, fuses and other protection systems. The health of the HVJB is paramount to ensure performance and reliability of electric vehicles. However sensing and monitoring in the HVJB is often lacking due to cost, causing limited capability of the vehicle controller to estimate the status and health of the unit. This publication focuses on the experimentation of an automotive HVJB to characterize the operation and build the foundation for the development of prognostic algorithms for HVJB. A production HVJB has been acquired and heavily instrumented. Extensive testing are performed in adiabatic and in ambient conditions at various current levels for various durations of operation. The testing setup was calibrated and iterated based on preliminary results, and the testing conditions were

Arigo, Sam, Borgerson, Joe, D'Arpino, Matilde, ZHU, Di, Zhang, Liwen

A Latent Space Framework for Modeling Transient Engine Emissions Using Joint Embedding Predictive Architectures

2026-01-0423To be published on 04/07/2026

Accurately modeling exhaust emissions during transient events, such as rapid acceleration and deceleration, remains a long-standing challenge in the automotive field and is a prerequisite for real-time control and optimization in modern powertrain systems. The nonlinear emission behavior is difficult to capture, and early mathematical models frequently relied on approximations that limited their accuracy. With the rise of data-driven methods, especially deep neural networks, including multilayer perceptrons and long short-term memory (LSTM) networks, modeling capabilities have improved significantly. However, to achieve meaningful accuracy, these models often require deep architectures, which increase computational load and inference time. This makes their application in real-time control systems challenging, as controllers must run at high sampling rates while maintaining accuracy, which is often a contradictory requirement. This paper addresses the trade-off with a new framework

Sundaram, Ganesh, Gehra, Tobias, Ulmen, Jonas, Heubaum, Mirjan, Görges, Daniel, Günthner, Michael

A Cloud-Native Testing-as-a-Service (TaaS) Framework for Safety-Compliant Verification of Automotive Software.

2026-01-0192To be published on 04/07/2026

The rapid shift toward Software-Defined Vehicles (SDVs) is driving an unprecedented rise in the complexity of automotive software. At the same time, compliance with functional safety standards such as ISO 26262 and cybersecurity standards such as ISO/SAE 21434 has become non-negotiable. These pressures expose significant gaps in the current verification and validation (V&V) practices. Today, many organizations rely on costly proprietary cloud ecosystems or heavily instrumented on-premises Hardware-in-the-Loop (HIL) labs. While effective in certain contexts, these methods are capital-intensive, difficult to scale, and often lead to fragmented compliance workflows. Most critically, they fall short of delivering a unified, traceable process that aligns seamlessly with Automotive SPICE requirements, particularly SWE.4 (Software Unit Verification), SWE.5 (Software Integration Testing), and SWE.6 (Software Qualification Testing). This paper proposes a cloud-native Testing-as-a-Service (TaaS

Venkatesan, Raghul, Pogulakonda, Meghna, Daware, Kartik

Machine Learning Modeling for Compressive Property Prediction of Foams

2026-01-0512To be published on 04/07/2026

Foam material models for automotive structural analysis typically require tensile and compressive data at multiple strain rates. The testing is costly and may require a long time to complete. For many applications, foams of similar chemistry are used and the foam structural responses, such as stiffness and compression force deflection, are controlled by the foam density. In such cases, Machine Learning (ML) lends itself as an ideal tool to detect the trends in material response based on density and strain rate. In this paper, five microcellular polyurethane (PU) foams of different densities were tested at four strain rates ranging from 0.01/s to 100/s. A ML model capable of predicting compressive stress-strain response for a range of densities was developed. The model demonstrated good prediction capability for intermediate strain rates at all foam densities and in extrapolating stress-strain curves at higher densities at all strain rates. The strain rate trends for a density outside

M, Gokula Krishnan, Kavimani, Harish, Muppana, Sai Siddhartha, Savic, Vesna, Chavare, Sudeep, V S, Rajamanickam

Numerical Study of Gasoline Direct Injection Sprays across Different Operating Conditions using a Machine-Learning-Based Rate of Injection

2026-01-0338To be published on 04/07/2026

Gasoline Direct Injection (GDI) is a key technology for increasing engine efficiency and reducing criteria pollutants, often in combination with boosted operation and engine downsizing. The development of advanced GDI injection systems is increasingly focused on refining the in-cylinder spray process, encompassing spray formation, mixing, and combustion, to achieve both homogeneous and stratified combustion. Given the broad spectrum of operating conditions, from cold-start to early and late injections at different engine loads, accurately modeling the spray formation and its evolution presents significant challenges. This study reports on a computational fluid dynamics investigation of spray morphology across different injection pressures under both cold-start and late-injection conditions. Due to a lack of information regarding the rate of injection (ROI) for the targeted injectors, a recently developed machine-learning-based model is used to extract the ROI from optical data of

Lien, Hao-Pin (Paul), Torelli, Roberto, Zhao, Le, Park, Ji-Woong, Zhang, Anqi, Pei, Yuanjiang, Hwang, Joonsik, Lee, Kyungwon

Optimization of Ride Comfort and Handling Using Advanced Control Strategies for Semi-Active Suspension Systems

2026-01-0211To be published on 04/07/2026

Semi-Active suspensions are decisive for both comfort and handling. This study develops a quarter‑vehicle model that captures pitch and roll and compares control strategies under realistic road inputs. The controller employs an adaptive control suggested design that updates online to reject road‑induced disturbances and parameter drift, while a bio‑inspired optimizer sharpens the gains for robust, deployment‑oriented performance. The framework evaluates seat travel, body and passenger accelerations, suspension deflections, tire deformation at each wheel, and body pitch/roll, with sensor demand kept deliberately low. Across representative road profiles, time‑ and frequency‑domain assessments in MATLAB/Simulink show clear and consistent superiority of the adaptive design—and even stronger performance for the optimized variant—over the conventional sliding‑mode baseline. Vibration is decisively suppressed, suspension and tire‑load excursions are tightly contained, and overall stability

M.faragallah, Mohamed, Metered, Hassan, Abdelaziz, Taha, Mohamed, M.A.

Analysis of Failure Causes and Optimisation of Automotive Cycloidal Rotor Pumps

2026-01-0184To be published on 04/07/2026

Cycloidal rotor pumps are widely employed in automotive and aerospace industries due to their compact structure, high volumetric efficiency, and minimal flow pulsation. With the advancement of new energy vehicles, rotor pumps are increasingly required to operate reliably under more demanding conditions, including higher rotational speeds, elevated pressures, and more severe operating environments. These requirements place greater emphasis on pump durability and structural integrity. This study investigates a fracture failure of the internal rotor observed during bench testing of a cycloidal rotor pump. Metallographic and physicochemical analyses were conducted on the failed rotor. The results indicated that the fracture was not attributable to material defects. Instead, the failure originated from improperly designed slot locations within the rotor, which created a structural weak point at the slot opening. Additionally, sharp corners introduced during machining of the slots resulted

Li, Meng, Xie, JIa, Qin, Gongyu, Yang, Hanming, Wang, Liangmo

Optimal Vehicle Sampling for Fleet-Wide Emissions Monitoring using Submodular Maximization

2026-01-0283To be published on 04/07/2026

This article addresses the problem of optimal vehicle sampling for fleet-wide in-use emissions monitoring, a necessity driven by the absence of direct emissions sensors in modern production vehicles and the variable impact of in-use changes and operational factors (mileage, time-in-service, workload) on emissions performance across a fleet. Recognizing that comprehensive fleet testing is impractical due to significant downtime and cost, we propose a novel approach to identify a small, yet optimally informative subset of vehicles for sampling. The proposed approach leverages submodular function maximization, a technique rooted in optimal experimental design, specifically D-optimal design, to maximize the determinant of the information matrix (e.g., of $X^TX$, where $X$ is the regressor/design matrix in the case of a linear in parameters model). This approach ensures that the collected data yields maximum information for refining and building accurate models for emissions characteristic

ZHANG, JIADI, Li, Xiao, Kolmanovsky, Ilya, Tsutsumi, Munecika, Nakada, Hayato

A Data-Driven Model for Predicting Casting Solidification Time and Mold Thermal Stress

2026-01-0233To be published on 04/07/2026

This study proposes a data-driven surrogate modeling framework for predicting solidification time and mold thermal stress during low-pressure die casting (LPDC) of aluminum alloy wheels. The methodology employed an optimal Latin hypercube design (OLHD) to sample key parameters including cooling channel geometry and process conditions. A sequential simulation methodology combining ProCAST and Abaqus was implemented to generate a comprehensive dataset of solidification times and thermal stress distributions. Based on this dataset, surrogate models were developed using Support Vector Regression, Kriging, and Polynomial Response Surface Methodology, with their hyperparameters automatically tuned through Bayesian Optimization (BO). The optimized models were rigorously evaluated using four statistical metrics: Coefficient of Determination (R²), Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). The evaluation results show that the BO-SVR model

fuhao, Fan, Zhan, yunlang, Zhan, Zhenfei, Yang, Yutong, Xiao, yong, HUANG, SHIYAO

Advanced Power Dissipation Strategies for Friction Brake Usage Reduction under high-load conditions in Electric Vehicles

2026-01-0452To be published on 04/07/2026

This paper presents a powertrain control strategy for hybrid and battery electric vehicles. The architecture that was used for simulation and testing is an extended-range electric vehicles (EREVs) equipped with dual electric drive motors and a generator-coupled internal combustion engine (ICE) that is not mechanically connected to the wheels, but the same feature may be used for any vehicle that relies on a combination of regenerative and mechanical brakes. During prolonged downhill driving under high-load conditions—such as high battery State of Charge (SOC), full passenger or cargo load, and towing—regenerative braking may be insufficient, increasing reliance on friction brakes and risking overheating. To address this, the proposed feature activates a combination of discrete and continuous energy dissipation mechanisms to absorb excess power and enhance braking performance. Discrete dissipators include the battery thermal conditioning system, ICE cooling system, and induced motor

Nogueira, Leonardo, de Souza, João Pedro, Bhakare, Allwyn, Arora, Dushyant, Murjani, Jash, Walsh, McKenize, Abedinilaksar, Mohammadjavad, LE, Truc, Senft, Stefan, ESSABRI, Ayoub

Lap Time Performance Sensitivity to Tire Model Parameters: An Optimal Control Framework for Model Selection and Calibration

2026-01-0214To be published on 04/07/2026

The push for vehicle development through virtual prototyping and testing in motorsports highlights the critical challenge of tire model selection and calibration, especially when vehicle dynamics must be accurately captured. The calibration process for tire models such as the Pacejka Magic Formula (MF) relies on parameter identification and experimental data fitting. While optimization algorithms have been implemented to calibrate tire models, few studies explore the effects of parameter selection on overall vehicle performance, complicating prioritization for the vehicle’s modeling and simulation strategy. To bridge this gap, this paper leverages optimal control methods to quantify how the variability of MF tire model parameters propagates to the overall vehicle model and impacts lap time prediction accuracy. To achieve this, a subset of parameters critical to combined slip of the MF tire model are varied through a Design of Experiments (DOE). These variations are executed on a flat

Zarate Villazon, Angel M., Brown, Ian, Balchanos, Michael, Mavris, Dimitri

A CFD Based Multi-Physics Modeling Approach to Predict Vent Gas Flow Paths due to Can Melting during Prismatic Cell Thermal Runaway

2026-01-0407To be published on 04/07/2026

As the automotive industry increasingly adopts high-energy-density pouch cells, ensuring vehicle safety against catastrophic thermal runaway (TR) has become paramount. Predicting the complex failure sequence of pouch cells, requires high-fidelity simulation tools that can capture tightly coupled physical phenomena. This paper presents a comprehensive, three-dimensional multi-physics Computational Fluid Dynamics (CFD) framework designed to simulate the entire TR event. The simulation originates with a multi-step Arrhenius chemical kinetics model to calculate the heat and gas generated by the primary exothermic reactions. This process drives a rapid increase in internal temperature and pressure, which is resolved by the model’s fluid dynamics solver. The initial vent opening is triggered when this internal pressure exceeds a predefined mechanical burst threshold, simulating a realistic seal rupture. Concurrently, a Conjugate Heat Transfer (CHT) analysis calculates the temperature

Mukherjee, Swarnava, Schlautman, Jeff, Srinivasan, Chiranth

Anisotropic mechanical property simulation of fiber reinforced PEEK using composite theory and comparison to experimental results

2026-01-0256To be published on 04/07/2026

The mechanical properties of 3D printed composites have been shown to vary due to the manufacturing infill direction due to artifacts from the printing process. PEEK (Polyether Ether Ketone) and PEEK reinforced with carbon fiber were studied for these experiments because they are widely used for their high strength properties. 3D printed composites that behave with anisotropic characteristics have been evaluated under Laminate Composite Theory (LCT), which can be used to determine the mechanical properties of these 3D printed composites. By changing the orientation of the extruded strands in a 3D printed part, the structure can be optimized in a specific orientation for specific loading conditions, and LCT can be applied for simulating mechanical responses. Three point bending tests were performed on rectangular 3D printed samples and compared to a 3D simulation using LCT for a similar bending load. This allows for the use of LCT in combination with a finite element software such as

Bradley, Coilin, Garcia, Jordan, Sibley, Brian

From Static Ground to Moving Ground Wind Tunnels: Feature Selection for Reliable Estimation of Drag Coefficient Discrepancies

2026-01-0203To be published on 04/07/2026

Moving ground wind tunnels offer a more accurate test environment for ground vehicle drag coefficient measurement due to their highly realistic representation of the boundary layer phenomenon. However, historically most vehicles have been tested on static ground wind tunnels. As a result, the measured drag coefficient of these vehicles may not be sufficiently realistic for certification purposes. Therefore, it is valuable to build statistical models to estimate moving-ground wind tunnel drag coefficient by using information from a static-ground wind tunnel and other relevant vehicle characteristics such as presence of aerodynamic devices (spoilers, air dams, etc.). However, to build accurate statistical models, appropriate predictive features must be identified as a first step. In this paper, an aerodynamic feature selection study has been conducted to identify vehicle characteristics that contribute to drag coefficient estimation discrepancies between a static- and a moving-ground

Singh, Yuvraj, Jayakumar, Adithya, Rizzoni, Giorgio

Mechanical Properties of Elastomeric Foam-Based Compression Pads for Battery Pack Applications

2026-01-0247To be published on 04/07/2026

With the increasing adoption of electric vehicles (EVs) worldwide, ensuring the long-term reliability and performance of the battery systems has become a paramount engineering challenge. Lithium-ion cells exhibit dimensional changes throughout their operational life, characterized by reversible “breathing”—expansion and contraction during charge and discharge cycles—and irreversible swelling due to aging. Compression pads are critical components for ensuring the lifetime performance of battery packs. The primary function of a compression pad is to act as a compliant cushion between cells. It accommodates these volumetric fluctuations by exerting consistent and optimized pressure. By absorbing the stress from cell expansion and maintaining structural integrity within the module, compression pads mitigate degradation mechanisms and ultimately maximize the durability and safety of the battery system over thousands of cycles. This paper highlights the importance of tailoring elastomeric

Deng, Weilin, Gunashekar, Subhashini

Machine Learning-Based Prediction of Wind-Induced Interior Noise in Ground Vehicles

2026-01-0599To be published on 04/07/2026

In response to increasing customer demand for enhanced passenger comfort and perceived vehicle quality, OEMs in automotive and commercial vehicles are placing significant emphasis on reducing the interior cabin noise. At highway speeds, wind noise is a primary contributor to the overall noise within the vehicle cabin. Conventional approaches to predict vehicle wind noise rely on physical testing, which can only be conducted in the later stages of the design process once a physical prototype is available. Increased adoption of established computational fluid dynamics (CFD) methods has enabled earlier assessment. However, such simulations require several hours to complete, posing a challenge in the context of rapid design iteration cycles. With the growing adoption of artificial intelligence in engineering, machine learning (ML) approaches have been proposed to predict a vehicle’s aerodynamics performance. Nevertheless, development of ML techniques in the context of aeroacoustics

Higgins, John, Fougere, Nicolas, Sondak, David, Senthooran, Sivapalan, Moron, Philippe, Jantzen, Andreas, Bi, Jing, Oancea, Victor

CFD-Guided DoE-ML Optimization Methods in a Heavy-Duty Hydrogen Engine

2026-01-0326To be published on 04/07/2026

This study introduces a CFD-guided Design of Experiments (DoE) and Machine Learning (ML) framework for the co-optimization of piston and pre-chamber geometries in a passive pre-chamber heavy-duty hydrogen engine operating at medium and low loads. Starting from a reference configuration—an omega-type piston and a methane-optimized pre-chamber—the design space was parameterized using seven geometric variables. A Sobol sequence was employed to generate 96 randomized design variants in the DoE, each evaluated through high-fidelity 3D-CFD simulations to capture key combustion and performance metrics. The resulting dataset served as the foundation for developing and evaluating several ML regression models. A rigorous ML workflow was adopted, featuring 5-fold cross-validation and hyperparameter tuning via Bayesian optimization to ensure generalization and robustness. Model selection was based on multi-metric performance criteria including prediction accuracy, error stability, and sensitivity

Menaca, Rafael, Shakeel, Mohammad Raghib, Liu, Xinlei, Mohan, Balaji, AlRamadan, Abdullah, Cenker, Emre, Silva, Mickael, Zhang, Anqi, Pei, Yuanjiang, Im, Hong

Experimental analysis and numerical simulation of the performance parameters of pyrolytic tire oil in a compression ignition engine.

2026-01-0345To be published on 04/07/2026

The search for alternative solutions for non-fossil fuels has led to several studies worldwide. This study focuses on environmentally responsible solutions to accelerate tire degradation, focusing on the transformation of these residues into fuel for diesel engines. The objective of this study was to experimentally evaluate, through numerical simulation, the performance of a compression ignition engine operating with Diesel S10, crude and refined pyrolytic tire oil, and mixtures in proportions of 20, 40, and 60% with Diesel oil. The experimental tests were performed on a single-cylinder engine coupled to a dynamometer bench, and the numerical simulation was performed using the Diesel Engine RK software. The experimental results indicated that increasing the proportion of refined pyrolytic oil in diesel slightly improves engine performance up to approximately 2750 RPM, after which the performance is reduced compared to pure Diesel. The addition of crude pyrolytic oil slightly improves

Santana, Claudio Marcio, Prudente, Lucas Rhuan, ROCHA, ANA MAURA, Peixoto, Claudio

Operational Feasibility of Battery Swapping in U.S. Transit: A Case Study of King County Metro

2026-01-0402To be published on 04/07/2026

Battery swapping technology has emerged as a promising alternative to conventional plug-in charging for electric bus fleets, offering rapid turnaround times and improved vehicle availability. This paper investigates whether battery swapping can be integrated into U.S. public transit bus operations without disrupting existing timetables. A Seattle based public transit agency- King County Metro is taken as a case study in this paper. Using General Transit Feed Specification data from King County Metro, a MATLAB model was built that reconstructs blocks and layovers, extracts dwell-time opportunities, and performs block distance and block time analyses to understand operational rhythms. A mathematical energy model was developed that maps route mileage, efficiency, and layover availability to swap decisions, using a look-ahead rule that defers exchanges whenever the next feasible layover can still be reached while respecting a minimum state-of-charge. The workflow estimates how many swaps

Vadlapatla, Taraka Rishi, Jankord, Gregory, D'Arpino, Matilde

Multi-Body Dynamics Control Integration Tool Application in Hybrid Vehicle System Identification

2026-01-0417To be published on 04/07/2026

The multi-body dynamics (MBD) model and the MATLAB Simulink model can be integrated to create a control-integration model. Using a high-fidelity MBD model to represent the vehicle as the plant, this integrated model can be used to analyze vehicle system physics and develop control strategies. For hybrid vehicles, this process is more complex because the powertrain and other vehicle systems are often built as separate MBD models. This paper describes a method for integrating a powertrain model developed in AMESIM, a vehicle model developed in SIMPACK, and a control model developed in MATLAB Simulink. The resulting integrated model was then used to perform frequency sweep analysis to identify driveline system properties. In particular, the driveline frequency and the amplitude of the transfer function between motor speed and motor torque are critical parameters. By applying active damping control to the driveline system, the peak amplitude and driveline vibrations can be reduced. The

Xing, Xing, Mathew, Vino

Optimization Strategies of Gliding Arc Plasma-Based Ammonia Cracker towards On-Board Applications

2026-01-0292To be published on 04/07/2026

Ammonia has emerged as a viable hydrogen energy carrier owing to its superior hydrogen density, mature industrial utilization. However, ammonia faces critical challenges including inadequate ignition characteristics and sluggish combustion kinetics, necessitating supplementary high-reactivity fuels for optimizing combustion. Onboard ammonia cracking technology resolves this problem through on-demand hydrogen in-time production. Among existing decomposition methods, gliding arc plasma (GAP) demonstrates exceptional promise for on-board crackers given its decent hydrogen conversion rate and transient response capability. Prevailing research predominantly employs experimental methodologies wherein macroscopic parameters inadequately proxy microscopic electric field characteristics, thereby impeding mechanistic insight. This study establishes a quasi-1D numerical model to interrogate how electric field parameters modulate ammonia-to-hydrogen conversion yield and system energy efficiency in

Dong, Guangyu, LI, Xian, ZHOU, Yanxiong, XU, Jie, Li, Liguang

Multi-Physics Topology Optimization and Machine Learning Surrogates for Lightweight Cast Automotive Parts

2026-01-0510To be published on 04/07/2026

This paper proposes an integrated optimization framework for lightweight cast vehicle structures by combining Multi-Physics Topology Optimization (MPTO) with machine learning (ML)-driven surrogate modeling. Conventional structural optimization approaches often rely on simplified load cases—such as 3 g inertial loading, frame torsion, or bending—rather than capturing the true complexity of performance-critical conditions like crashworthiness and fatigue durability. As a result, designs optimized for a single criterion frequently degrade in other performance areas, leading to costly redesigns or over-engineered solutions. The proposed methodology introduces MPTO through four structured steps that progressively build design fidelity. Step 1 establishes a baseline primitive topology optimization workflow using a standardized package checklist, ensuring that the design domain, boundary conditions, constraints, and key manufacturing rules are consistently defined as the foundation for

Kim, Hyosig, Senkowski, Andres, Gona, Kiran, Saroha, Lalit, Boraiah, Mahesh

A Holistic Approach to Pre-Chamber Design, Integration, and Operational Optimization

2026-01-0310To be published on 04/07/2026

As internal combustion engines continue to play a critical role in hybrid on-road and numerous non-road applications, there is a continued push to increase efficiency and minimize tailpipe emissions. However, reduced investment in new engine architectures means retrofittable technologies are favored to continue incremental performance improvements to existing engine platforms. To maintain the relatively low capital cost of engine-based powertrains, these technologies must be low-cost and compatible with the diverse mix of fuels that may be encountered across various market segments in the future. Pre-chambers have shown significant potential for improving spark-ignited engine performance across a wide range of engine sizes, from motorsport applications to stationary power, and operating conditions, from stoichiometric operation to ultra-lean. Understanding the degree to which this central combustion technology must be tailored to optimize its performance with a variety of fuels and

Peters, Nathan, Pothuraju Subramanyam, Sai, Hoth, Alexander, Bunce, Mike

Leveraging Large Language Models for CAD Automation and Multi-Objective Optimization of Automotive Heat Exchangers

2026-01-0506To be published on 04/07/2026

The design of automotive heat exchangers is a critical challenge, requiring the careful balance of thermal performance, aerodynamic efficiency, structural integrity, and manufacturability. Traditional design processes rely heavily on manual CAD modeling and iterative simulations, which are both time-intensive and computationally demanding. Recent advances in artificial intelligence, particularly in Large Language Models (LLMs) and Transformer-based architectures, open new possibilities for automating these tasks. This work introduces a novel framework that integrates LLMs with CAD kernels to generate parametric heat exchanger geometries directly from natural language descriptions. The models are automatically exported into standard formats for computational fluid dynamics simulations and seamlessly incorporated within a multi-objective optimization pipeline. By coupling natural language-driven CAD automation with advanced optimization techniques, the framework enables rapid exploration

Chaudhari, Prathamesh, Tovar, Andres

Transformer-Based Deep Learning Framework for Efficient Prediction of Automotive Wind Noise

2026-01-0222To be published on 04/07/2026

Aerodynamic wind noise is a critical challenge in modern automotive development, particularly with the rise of vehicle electrification and intelligent mobility, where cabin acoustic comfort is a key quality metric. While reliable, traditional methods like wind tunnel experiment and CFD simulations are both costly and time-consuming. To address this, we propose a novel transformer-based framework for rapid and accurate wind noise prediction. Several model improvements, including the physical attention, geometry wave number embedding, hybrid FPS-random down sampling method and frequency separation output heads are properly employed to reduce the GPU mermory cost and improve the prediction accuracy. This framework is pre-trained on a large-scale transient flow field dataset of 1,000 diverse vehicles generated using Delayed Detached Eddy Simulation (DES). From a vehicle's point-cloud coordinates, the model directly predicts the surface pressure spectrum on the driver-side window and the

TANG, Weishao, Liu, Mengxin, QIN, Ling, Duan, Menghua, wang, chengjun

Simplified Thermal Runaway Propagation Modeling to Enable Mass-Optimized Battery Pack Design

2026-01-0404To be published on 04/07/2026

This paper presents a simplified approach to model thermal runaway propagation in a multi-cell battery pack, with the goal of designing a safe and lightweight pack for mass-sensitive applications. The key parameters which characterize single-cell thermal runaway, including heat release profile, apparent cell emissivity and mass loss, were extracted from empirical nail penetration tests. This characterization was used to drive a three-dimensional thermal model of a 19-cell hexagonal sub-pack with a center trigger cell. To enable rapid design exploration, a symmetry-based computationally simplified domain was used for a full-factorial Design of Experiments (DOE) varying cell spacing, epoxy thickness, heat spreader thickness, and cup geometry. The DOE results were used to identify dominant heat-transfer mechanisms, capture main and interaction effects, and determine mass-efficient design levers governing peak-neighbor cell temperature during propagation. Insights from the DOE study

Kalyankar, Apoorv, Owen, Elliot, Strohmaier, Kyle, Mardall, Joseph

Assessing Energy Use, Cost, and Emissions of Small Regional Rail Vehicles: Methodology and Case Study on a German Track

2026-01-0419To be published on 04/07/2026

The decarbonization of regional rail remains a pressing challenge in the European Union, where nearly half of the 200,000 km network remains unelectrified. Lightweight, single-car railbuses historically provided efficient service on such lines, but modern replacements are scarce. This work evaluates a standardized 30-ton, 16 m railbus platform for unelectrified regional service, focusing on propulsion system design and trade-offs between range, cost, and emissions. A MATLAB/Simulink drive-cycle model was developed to simulate energy consumption under realistic operating conditions. The Erfurt–Rennsteig route in Germany (130 km round trip, gradients up to 6 %) was selected as a representative case study. The model incorporates sub-models for traction motors, lithium-ion batteries (LFP and LTO), fuel storage, fuel cells, and ICE gensets across multiple fuel options (diesel, gasoline, methane, ethanol, methanol, HVO, FAME, and hydrogen). Battery lifetime is estimated using a combined

Ahrling, Christoffer, Tuner, Martin, Gainey, Brian, Torkiharchegani, Amir, Scharmach, Marcel, Hertel, Benedikt, ALAKÜLA, Mats

EV Propulsion Drive Converter SiC Power Device Over-Temperature Protection under Low Motor Speed Operation

2026-01-0436To be published on 04/07/2026

Monitoring power device temperature in an electric vehicle propulsion drive converter is extremely important to achieve full power delivery within the maximum power capability envelope. Usually, on-die temperature sensors are installed on Si-IGBT power devices in electric vehicle propulsion drive converters to enable monitoring device temperature and achieve over-temperature protection. Currently, SiC MOSFET is a promising power device in power converters of electric drives because of its lower loss, higher switching speed, higher voltage capability, and higher junction temperature limit in comparison with widely used Si-IGBT. However, SiC MOSFET is a more expensive device, so the present technology trend is trying to reduce its chip size. Installation of an on-die temperature sensor on SiC MOSFET will significantly increase its cost and complexity. So presently, there is no junction temperature sensor installed in SiC MOSFET due to which there is great difficulty protecting SiC MOSFET

Thongam, Jogendra Singh, Ge, BAOMING, Bradford, Steven, Kulkarni, Milind

CFD Modelling of Cell Formation Gas and Venting During the EV’s Battery Manufacturing Process

2026-01-0384To be published on 04/07/2026

The electric battery is a critical component of electric vehicles (EVs), where high power demands pose significant operational challenges. One such challenge is gas generation within the porous anode layer, which can lead to pressure buildup inside the battery. The complex interfacial dynamics at the microscale play a crucial role in determining the effectiveness of gas venting and the resulting pressure evolution. This study addresses this issue through a two-pronged simulation approach. First, gas generation within a representative anode microstructure is investigated using a Volume of Fluid (VOF) framework that resolves tortuous flow passages. The simulations reveal that gas generation in such microstructures can lead to pressure rises of several thousand Pascals, with interfacial behavior primarily governed by surface tension effects. Second, a high-level battery pack simulation is performed using a porous media approach to evaluate system-scale gas venting and localized

Mahyawansi, Pratik J., Schlautman, Jeff, Viswanath, Priyanka, Srinivasan, Chiranth

A Comprehensive Modelling tool for Heavy-Duty Hybrid Fuel Cell/Battery Powertrains

2026-01-0430To be published on 04/07/2026

The global transition towards sustainable transportation is driving the development of efficient, low-emission propulsion systems. Battery-electric solutions are effective in urban contexts but face limitations in heavy-duty and long-haul applications due to the size and weight of the required energy storage. Hybrid battery/fuel cell powertrains offer a promising alternative for such use case, reducing vehicle mass and charging times while maintaining high energy efficiency. This study presents a zero-dimensional MATLAB/Simulink model of a parallel hybrid fuel cell/battery system for heavy-duty vehicles. The model encompasses the main vehicle subsystems, including the fuel cell stack with auxiliaries, the battery pack, the electric drive, the transmission and vehicle longitudinal dynamics, coordinated through a rule-based energy management strategy. Two representative vehicle configurations were analysed: a Group 2 urban delivery vehicle, and a Group 5 long-haul truck. Model validation

Montecchi, Gianluca, Martoccia, Lorenzo, D'Adamo, Alessandro

CAE-based Electro-Thermal Characterization and Design Optimization of Automotive Inverter Busbars

2026-01-0642To be published on 04/07/2026

High power density demands in automotive inverters need precise thermal management to ensure efficiency, reliability and extended component lifetime. This paper presents a systematic, integrated electro-thermal workflow utilizing Computer-Aided Engineering (CAE) tools to characterize and optimize internal inverter components by directly linking electrical performance to thermal behavior. The methodology comprises three main steps: first, detailed frequency-dependent electrical parameters (RLCG, Z, S) and current density maps are extracted using computational calculations by ANSYS HFSS and ANSYS Q3D Extractor. Second, these parameters inform comprehensive power loss calculations (conduction, dielectric, switching), which are then mapped onto detailed 3D thermal fields using Computational Fluid Dynamics (CFD) through ANSYS Icepak. Finally, the results are analyzed to identify critical components and inform targeted design modifications. The workflow's effectiveness is demonstrated

Oliveira, Felipe, Aguiar, Cláudio, Justino, Marco, Tarrago, Vinicius, Almeida, Oberti

Extreme Scale Automotive Aerodynamic Simulations

2026-01-0597To be published on 04/07/2026

Achieving an optimal balance between simulation accuracy and computational efficiency remains a central challenge in automotive aerodynamics. While the adoption of AI and machine learning (ML) methods in vehicle development is expected to grow significantly, the demand for highly scalable, computationally efficient, and accurate computational fluid dynamics (CFD) methods persists. The emergence of GPU technology presents new opportunities to deliver cost-effective, high-fidelity, scale-resolving simulations to industrial users. A comprehensive evaluation of Simcenter STAR-CCM+’s parallel scalability and accuracy across extensive CPU and GPU resources was executed on the Frontier supercomputer at Oak Ridge National Laboratory. Steady-state and transient aerodynamic scalability simulations were executed using the DrivAer notchback vehicle configuration. Simulation accuracy was evaluated through transient simulations employing the SST-DDES turbulence model on an initial mesh of 200

Larsson, Torbjörn, Grover, Ronald O., Landi, Simone, Altmann, Peter, McManus, Liam, Dowding, Steven

Efficient casting system design through simulation for AlCoCrFeNi high-entropy alloy

2026-01-0176To be published on 04/07/2026

This study depicts the design, simulation, and fabrication of an equiatomic Al–Co–Cr–Fe–Ni high entropy alloy (HEA) through casting using Siemens NX CAD and ADSTEFAN simulation software. The part and gating system, including the sprue, runner, pouring cup, and riser, were modeled to generate the STL file and optimization was done through iterative simulation. Fluid flow and solidification analyses were performed to simulate the filling temperature, cold shut, and soundness of the casting. The optimized gating design is expected to provide uniformity in the temperature distribution, avoid defects during solidification and to eliminate shrinkage cavities in the cast alloy. HEA was fabricated by utilizing the validated gating system using vacuum induction melting of high-purity elemental granules. Further microstructural investigation using methods like SEM and EDS showed a uniformity of Al, Co, Cr, Fe, and Ni in near-equiatomic proportions. The optimization of riser-fed design

Vashistha, Saurabh, Rawat PhD, Pankaj, singh PhD, Shailesh, Pathak, Sunil

Flame Velocity and Combustion Behaviour of Ammonia–Hydrogen Blends: A Combined Experimental and Numerical Study

2026-01-0320To be published on 04/07/2026

Ammonia is gaining importance as a viable energy vector for decarbonising the maritime sector. However, ammonia flame speed is much slower than that of traditional fuels, and this can result in incomplete burning, decreased engine efficiency, and increased undesirable emissions such as unburned ammonia (NH₃). While blending hydrogen with ammonia has been shown to mitigate some of ammonia's combustion drawbacks, the underlying mechanisms driving these improvements are still not fully understood. This study explores these processes to inform better optimisation. A 1.2 L optically accessible constant volume combustion chamber equipped with a wall-mounted surface spark plug was used to investigate the combustion characteristics and apparent flame velocity of ammonia-hydrogen blends. Chamber pressure and temperature were measured using high-frequency piezoresistive pressure transducers and K-type thermocouples. Flame propagation was captured at 9,000 frames per second using a Photron

Bodur, Tuna Murat, Bowling, William, La Rocca, Antonino, Cairns, Alasdair

Machine Learning for Predicting Rotor and Brake Fluid Temperatures in Hill Descent Events

2026-01-0159To be published on 04/07/2026

High thermal loads on brake systems during extended descents followed by vehicle soak pose significant safety and durability risks. Excessive rotor or fluid temperatures can cause loss of braking efficacy, fluid degradation or evaporation, thermal fade, and accelerated component wear. This study uses time history data of brake disc and fluid temperatures collected during controlled hill descent events and subsequent soak (park) periods, and simultaneously logs environmental conditions (ambient temperature, humidity, wind speed and direction) and brake specifications (rotor, caliper geometry, pad material, fluid type, and vehicle mass). Seventy six test runs from a dedicated validation program are used, each providing time history records that form the basis of our analysis. From these records we extract phase specific samples (descent and soak phase) and engineer compact descriptors, such as start and peak temperatures, environmental factors, rolling statistics and contextual metadata

poojari, Uday Kumar, Westphalen, Jan, Venugopal, Narayana

Study of suppression properties on traffic waves of periodic vehicular platoons with typical platoon configurations

2026-01-0167To be published on 04/07/2026

To investigate the suppression characteristics on traffic waves in the periodic configurations vehicular platoons, this paper analyzes the bandgap properties and frequency transmission characteristics of three typical platoon configurations: predecessor-following (PF), bidirectional (BD), and predecessor-leader-following (PLF). First, dynamic models of periodically structured platoons are developed for each configuration. Then, taking the BD periodic platoon as an example, the spacing error transfer matrix is derived by the lumped mass method, and a bandgap computation approach is formulated based on Bloch’s theorem. Building on this, the frequency transmission characteristics of periodic platoons using PF, BD, and PLF configurations are numerically solved and analyzed. Finally, numerical simulations are conducted to assess the suppression effect of platoons on traffic waves, comparing the bandgap properties and frequency transmission characteristics across different configurations

Yang, Xiujian, Zhuang, Qingyuan, Wu, Ruiqiong, Wang, Shenyi

Study of Approaches for Enhanced Traction Control Response for Hybrid Powertrain Architectures

2026-01-0415To be published on 04/07/2026

Traction Control functionality requires fast and accurate response by powertrain system for wheel slip control. Close loop control effort of wheel slip shall be greatly reduced with more accurate feedforward torque. This paper explores the method of achieving requested traction control torque with better accuracy for torque converter-based hybrid electric powertrain architectures using improved dynamic modeling of torque converter. This method uses input inertia compensation involving feedforward and closed loop control modeling of torque converter and includes conversion of traction control intervention request through appropriate domains. Commanded torques by hybrid controller at wheel with and without these approaches are compared in Model in the loop simulation environment to identify optimum approach.

Karogal, Indrasen, Ober, Mitchell, Banuso, Abdulquadri, Sharma, Ashay

Predictive AEB Activation for Secondary Collision Risk Reduction

2026-01-0518To be published on 04/07/2026

Although the evaluation criteria of New Car Assessment Programs (NCAP) continue to evolve, they still predominantly focus on one-to-one collision scenarios. However, accident analyses indicate that in real-world traffic environments, particularly at intersections with multi-lane arterial roads, complex situations involving multiple vehicles are common, and there exists a significant risk of secondary side collisions occurring immediately after Autonomous Emergency Braking (AEB) activation. This study aims to predict and mitigate such secondary collision risks by proposing a control strategy that adjusts braking timing and stopping position based on the motion information of multiple crossing vehicles. A control algorithm was developed to estimate the likelihood of secondary collisions and determine optimal intervention parameters. The proposed system was evaluated using a sensor-equipped test vehicle and numerical simulations. Results demonstrated a tendency to reduce secondary

Kobayashi, Fumiya, Fukuda, Kentaro, TANI, HIROAKI

A Cooperative AFS/DYC Control for FWDIEV Based on Dissipative Energy Method

2026-01-0619To be published on 04/07/2026

To enhance the lateral stability of four-wheel-drive intelligent electric vehicles (FWDIEV) under extreme operating conditions, this paper proposes a cooperative control strategy integrating active front steering (AFS) and direct yaw moment control (DYC) based on dissipative energy method. A nonlinear three-degree-of-freedom vehicle model is established to analyze the evolution of the vehicle state phase trajectory. A quantitative lateral stability index is constructed using dissipative energy to accurately evaluate the vehicle's lateral dynamics. Utilizing dissipative energy and its gradient information, a time-varying stability boundary is defined under dynamic constraints, and adaptive weighting coordination between the AFS and DYC systems is designed to achieve coordinated control of front steering angle and additional yaw moment. A feedforward–model predictive control (FF-MPC) framework is developed, in which a feedforward module generates compensation based on driver intent to

Zhao, Kun, Zhao, Zhiguo, Wang, Yutao, Xia, Xue, Chen, Xi, Hu, Yingjia

Hybrid Prediction Model of Wheel-Traction Force on Soft Soil Based on High-Precision Contact Angle

2026-01-0594To be published on 04/07/2026

Traction force plays a critical role in the design of off-road vehicles and the evaluation of terrain trafficability. The accuracy of the traditional Bekker-Wong semi-empirical formula for predicting wheel traction force depends on the precision of parameters such as the entry contact angle, departure contact angle, and maximum stress angle. However, the calculation of these angles is often influenced by multiple empirical factors, which significantly reduces the accuracy of traction force prediction.To improve prediction accuracy, this study proposes a hybrid traction force prediction method that integrates Abaqus simulation with a semi-empirical model. First, a wheel-soil interaction simulation model was established in Abaqus using the FEM-SPH coupling method. Then, computer vision-based image feature extraction techniques were applied to process the simulation result contours, obtaining accurate values for the entry contact angle, departure contact angle, and maximum stress angle

Cao, Ruibin, Wang, Kaidi, Shen, Yanhua, Liu, Zuyang

Correlating CFD and experimental vehicle aerodynamics using multi-fidelity simulation approaches

2026-01-0595To be published on 04/07/2026

The automotive industry faces several challenges requiring faster product development, where numerical simulations and digitalization are key enablers to reduce time to market and development costs. Numerical methods require both short turnaround times and high-fidelity results. Capturing small differences across vehicle designs, by accurately predicting complex flow phenomena is crucial for aerodynamics optimization. The disruptive and fast development of GPGPU computing hardware, promising accelerated turnaround times at lower costs, found its natural position in this landscape. This paper describes simulation approaches with increasing fidelity applied to a set of variants of a Stellantis estate production car: these include geometrical, yaw angle and ride height changes, and all were tested in wind tunnel test facilities. Correlation between aerodynamics CFD simulations using Simcenter STAR-CCM+ and wind tunnel measurements is verified by comparing drag trends, pressure probes and

Landi, Simone, Altmann, Peter, Cannavacciuolo, Ciro, Johannesson, Mani, Borowiec, Grzegorz, RIBES, Charles, Guzman, Arturo, Miretti, Luca

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