Browse Topic: Management and Organizations

Items (49,665)

The History of the International Powered Lift Conference Across Five Decades of V/STOL Progress

2026-01-0662To be published on 07/01/2026

This year, SAE International hosts the 2026 edition of the International Powered Lift Conference (IPLC), which focuses on the latest developments in vertical and/or short takeoff and landing (V/STOL) aircraft research, concepts and programs. IPLC is a joint technical meeting, held approximately biennially, co-sponsored by the American Institute of Aeronautics & Astronautics (AIAA), the Royal Aeronautical Society (RAeS), SAE International and the Vertical Flight Society (VFS). Because each technical society hosts IPLC only once a decade, staff turnover at the societies creates a lack of knowledge and historical context of the event. This paper provides a formal record of the history and legacy of the IPLC, and its progress in highlighting the technical and programmatic progress of V/STOL over the ages.

Hirschberg, Michael J.

Vibration Synthesis and Seat Dynamics Compensation for Single-Axis Seat Vibration in Driving Simulators

2026-01-0673To be published on 06/20/2026

Realistic seat vibration reproduction is essential for delivering authentic haptic cues and enhancing driver immersion in driving simulators. Unlike direct playback of road recordings, simulator applications require vibration synthesis that responds interactively to driver inputs and vehicle dynamics. Reproducing these vibrations at the seat is complicated by actuator bandwidth, cross-axis coupling, and the dynamic behavior of the seat structure, which can alter the intended target response. This work presents vibration synthesis and seat dynamics compensation methods implemented on a dual-axis seat vibration reproduction system equipped with horizontal and vertical actuators. Frequency Response Functions (FRFs) were measured to characterize the system dynamics under both single-axis and dual-axis excitations, capturing direct responses as well as cross-axis effects. Run-up, coast-down, and combined excitation tests were then used to evaluate performance under more operational

Muthu Chaiphas, Joshua Daniel, cuenca, Jacques, Bianciardi, Fabio, Colangeli, Claudio, Deckers, Elke, Denayer, Hervé, Janssens, Karl

Development and Standardization of a Compensation Method for Measurement Equipment Effects to Improve Blocked Force Accuracy in Physical Tires

2026-01-0698To be published on 06/20/2026

Although the tire system is not developed in-house but supplied by tire manufacturers, it is a critical component that significantly affects overall vehicle performance, including NVH. To ensure the supplied tires meet vehicle performance targets, our company provides detailed development guidelines, defining block force—closely related to road noise—as a key NVH performance index. As virtual tire development is introduced, manufacturers will submit block force data from virtual models with initial tire designs, enabling early prediction of tire and vehicle-level performance. However, this approach is still under evaluation, with unresolved challenges such as the absence of clear block force target-setting methods and limited accuracy in current virtual evaluations. To address this, we propose using block force measurements from mass-produced or prototype tires as a basis for data-driven target setting and as a reference to enhance virtual model accuracy. Since block force data is

ahn, youngeun, Kang, Min Kyoo, Kim, Yong Dae, Reichart, Ron, Kim, Yonghun, Han, Inhyuk, JI, Unjin, Lee, JeongHwan

A Modular Method for Certified Virtual Validation of NVH Performance in Automotive Platforms

2026-01-0684To be published on 06/20/2026

This work presents a modular engineering methodology (DigPhBalance - Digital Physical Balance) for the virtual validation of Noise, Vibration, and Harshness (NVH) performance in automotive development. The approach addresses the inefficiency of repeated physical testing across vehicle variants by introducing a structured two-phase process—Launcher and Reskin—centered on quantitative performance indicators with formal acceptance thresholds. In the Launcher phase, a digital replica of the base vehicle is built and iteratively correlated with physical test data. Validation is governed by objective indicators of confidence, conformity, and correlation, each evaluated against predefined thresholds. Once validated, the model becomes a certified reference, enabling its reuse across derivative configurations in the Reskin phase. Physical testing is only required if indicators fall below threshold, with a final gate test on pre-series vehicles ensuring industrial robustness. DigPhBalance

Celiberti, Lucia, Camia, Andrea

A Self-Learning Framework for NVH CAE Analysis

2026-01-0718To be published on 06/20/2026

The vibro-acoustic performance of a vehicle is a critical factor in customer perception of quality and comfort. Optimizing for Noise, Vibration, and Harshness (NVH), particularly road noise, presents a persistent challenge in the automotive development cycle. While modern FEM analysis is essential, the increasing complexity and sheer volume of CAE simulation data often overwhelm manual interpretation, potentially leading to prolonged development times or sacrifices in resulting comfort quality. To address these challenges, this paper introduces the application of CDH/ACE (Autonomous Computational Experiments), a framework that combines conventional CAE simulation workflows with machine learning in an iterative cyclic process. This creates a self-learning and self-correcting system that autonomously defines, performs, and learns from computational experiments. By building predictive models from simulation data, the framework intelligently guides the design exploration to achieve

Visser, Rene

AI-Based Package Tray Design Method for Controlling Acoustic Modes in a Sedan

2026-01-0716To be published on 06/20/2026

• In this study, we propose a methodology for predicting the acoustic modes and natural frequencies of a sedan using artificial intelligence and demonstrate the feasibility of controlling its acoustic characteristics by modifying the hole distribution of the package tray. In typical sedan structures, the cabin cavity and trunk cavity are acoustically coupled through holes in the package tray. The distribution of these holes significantly affects the natural acoustic modes and frequencies of the vehicle. However, once the exterior shape of the vehicle is finalized during the design phase, options for structural modifications to mitigate noise issues caused by these modes become extremely limited. To address this challenge efficiently, we develop a deep learning-based neural network model trained on data derived from a simplified acoustic analysis model of a sedan that includes a package tray. Finite element analysis is performed to generate acoustic modes and natural frequencies, which

Lee, Jin Woo, Cho, Jaeho, Nam, Younsic, Han, Yongha

Active Learning Supported Metadata Extraction from NVH Simulations

2026-01-0720To be published on 06/20/2026

Simulations can only be searched, reused and leveraged as trainings data for machine learning methods if they possess suitable metadata. Manually obtaining this metadata is time-consuming and requires expert knowledge. Consequently, there is often a lack of metadata, and this prohibits the reutilisation of simulation data. Therefore, automated frame works for metadata extraction are essential to quickly, effortlessly and cost-efficiently obtain this information. At present, there are no existing toolboxes for Finite-Element-Simulation data and current Large Language Models are not usable. Nevertheless, machine learning methods are a promising solution for this task. However, classical supervised machine learning methods cannot be used due to the high effort for labelling. Therefore, classical rule-based extraction algorithms are an alternative for fundamental metadata extraction. For more enhanced tasks, where more context is necessary, they are not usable. Active Learning is the

Luegmair, Marinus, Gröttrup PhD, Sören

Spatial Optimisation of Inner Dash Acoustic Performance using Reciprocal Holography

2026-01-0712To be published on 06/20/2026

As acoustic requirements for NVH trim components become more demanding – driven by cost, weight, and increasingly, sustainability – there is growing pressure to optimise performance across multiple criteria. In complex components such as the inner dash, performance is often assessed in isolated. However, this fails to reflect real-world conditions, where contributions from ancillary systems, the instrument panel, and variable noise sources such as the power unit, gearbox, and tyres are significant. Traditional Transmission Loss (TL) measurements provide averaged results that can overlook spatial variation and lead to suboptimal design choices. To address this, an approach is presented that uses reciprocal holography to map the spatial TL performance of an inner dash. This method enables a more representative and targeted design process for achieving effective acoustic performance in fully built vehicle environments. Furthermore, the spatial Transmission Loss data can be combined with

Harry, Evan, Eandi, Giacomo

Validation of an adaptive order Finite Element model for transmission loss simulation of trimmed automotive structures

2026-01-0707To be published on 06/20/2026

Vehicle electrification and increasing demands for driving comfort present significant challenges for designing effective noise control treatments in modern vehicles. Lightweight, low-emission designs often compromise acoustic efficiency. One way of compensating this is through the use of multi-layer ‘trim’ material configurations to mitigate noise across a wider frequency range. Traditional 3D finite element models, while accurate and even needed to capture the full dynamic behaviour, become computationally prohibitive for complex automotive structures like firewalls, which feature intricate shapes, high curvature, and material compression. This computational burden limits design exploration and timely noise performance predictions. This paper introduces the use of an innovative adaptive higher-order finite element method to address these challenges. The method utilizes a fixed finite element discretization of trim components and surrounding acoustic fluid, incorporating an automated

Van Genechten, Bert, Vansant, Koen, Purohit, Bimal, Effinger, Veronika

Bridging Simulation And Reality: Incorporating Seal Contact Detachment Into Stick–Slip Prediction

2026-01-0714To be published on 06/20/2026

Preventing stick–slip in virtual vehicle development combines virtual displacement data with experimental sliding tests. If the calculated relative displacement at a contact exceeds the distance of two stick–slip impulses, that contact could be a potential noise source. Such risks can be mitigated only by design changes or alternative sealing concepts. Stick–slip tests provide the experimental reference, showing how often a frictional pairing produces impulses over a defined sliding distance. The basis for this assessment is Multibody Dynamics (MBD) simulations, which compute component deformations during virtual drives and derive relative displacements at contact interfaces (e.g. in the sealing gap between body and closure), representing the theoretical sliding distance that enables stick–slip effects. Yet, this combined approach has overlooked a critical factor: the deformability of automotive seals. Due to their elastomeric properties and geometry, seals can elastically follow

Strangfeld, Martin, Fritz, Susanne, Weber, Jens, Rosell, Anneli

Standardized Equivalent Tire Load measurements delivered by suppliers to an OEM enabling consistent and comparable simulation input.

2026-01-0699To be published on 06/20/2026

Part- or component-level tests are commonly performed by Tiers and OEMs to investigate the NVH behavior and loading mechanisms. However, because test bench dynamics differ from those of the actual vehicle environment, correlating measured sound, acceleration, and forces between bench and vehicle often proves challenging. Blocked forces offer a way to address this issue, as they provide test bench– and vehicle–independent load representations, effectively enabling different Tiers to deliver consistent load data, which OEMs can then to better tune excitation and noise transmission on their vehicles. This paper focuses on 2 test bench compensation techniques, involving pure test and a simulation models of the tire, to obtain accurate blocked-forces. The compensation techniques are validated on four testbenches of different companies.

Reichart, Ron, de Klerk PhD, Dennis

Alcohol-Based Energy Carriers in Transportation: Status, Standards, and Outlook

2026-37-0042To be published on 06/09/2026

The global transport sector accounts for approximately 28% of total energy consumption and 15.8% of greenhouse gas emissions worldwide, with road transport alone responsible for 11.8%. As the world seeks pathways toward carbon neutrality, methanol and ethanol emerge as promising alternative energy carriers capable of leveraging existing logistics infrastructure while reducing dependence on fossil fuels. This review examines the production pathways, lifecycle considerations, and costs associated with both alcohols. Global methanol production reached 111 million metric tons in 2022, and global ethanol production totaled approximately 93.3 million metric tons in 2024. In comparison, the yearly production of gasoline and diesel amounts to over 2 billion metric tons per year. The review evaluates fuel requirements across multiple transport modes, including passenger vehicles, light- and heavy-duty vehicles, maritime shipping, aviation, and rail, and assesses regulatory frameworks governing

Fitz, Patrick, Fellner, Felix, Rößlhuemer, Raphael, Härtl, Martin, Jaensch, Malte

Passenger Car Powertrain for Voltages above 800V

2026-37-0020To be published on 06/09/2026

The EU-funded innovation project High-Voltage fast-charging Efficient electric vehicle Powertrains (HiVEP) develops new technologies for mass-market electric vehicles (EVs) by advancing architectures operating above 800 V. These architectures integrate silicon carbide (SiC)-based power electronics, rare-earth-free electric machines with active winding reconfiguration, high C-rate batteries, and optimised thermal management systems. HiVEP aims to deliver sub-10-minute fast charging, reduce energy consumption by at least 25%, extend range by 20%, and cut system costs by up to 20% in volume production. This article expands on the project objectives, methodological approach and expected key innovations as well as technical, environmental, and societal impacts. The discussion situates HiVEP within the European research and innovation landscape, emphasising its role in accelerating adoption of sustainable mobility solutions.

Schernus, Christof, Nada, Shady, Neuhaus, Christoph, Ewald, Jens, Swierc, Daniel, Kallur-Krishnamoorthy, Rajesh, Vasiliadis, Harilaos

Rapid Fuel Distribution Prediction in RCCI Marine Engines Using CFD-Informed, Active Learning Framework

2026-37-0017To be published on 06/09/2026

Accurate prediction of in-cylinder fuel distribution (FD) is fundamental to reduced-order combustion modeling and emissions prediction, yet remains computationally prohibitive with high-fidelity CFD alone. This work develops a CFD-informed machine-learning surrogate for spatial FD in a large-bore diesel engine, based on a Wärtsilä W20 injector and representative engine conditions. A fully coupled injector–spray–engine CFD framework under engine-like RCCI inert conditions determines the needle-lift profile and resolves the combined effects of injector geometry, needle dynamics, and operating conditions on in-cylinder flow, capturing physical phenomena not reproducible by isolated free-spray simulations. A high-fidelity database is generated using Latin Hypercube Sampling, from which FD is extracted at 15 CAD before top dead center within an annular multi-zone (MZ) representation consistent with reduced-order combustion models. A multi-output Random Forest (RF) surrogate, augmented with

Moradi, Jamshid, Salahi, Mahdi, Heidarabadi, Shadab, Andwari, Amin, Konno, Juho, Wik, Christer, Mikulski, Maciej

A Parametric Cradle-to-Grave Life Cycle Assessment Tool for Comparative Evaluation of Passenger Car CO₂ Emissions

2026-37-0040To be published on 06/09/2026

This work presents the development of a flexible and user-oriented software tool for the cradle-to-grave Life Cycle Assessment (LCA) of passenger cars, enabling robust comparisons of greenhouse gas emissions across heterogeneous vehicle configurations. The tool supports informed decision-making by quantifying and visualizing environmental impacts associated with alternative mobility choices over the full vehicle life cycle, including production, use, maintenance, and end-of-life stages. A key contribution of the proposed framework is its high degree of configurability and readability. All parameters describing the vehicle and its usage can be freely modified, including powertrain type, dimensions and weight, ownership profile (new or second-hand vehicles, partial ownership periods, leasing scenarios), annual mileage, vehicle lifetime assumptions, and the carbon intensity of fuels or electricity sources. Country-specific energy mixes are explicitly considered, allowing the same vehicle

Gastaldi, Chiara, Cibrario, Luca

Investigation into Cooling Loss Reduction Associated with Changes in In-Cylinder Flame Distribution by Offset Orifice Nozzle

2026-37-0003To be published on 06/09/2026

Recently, an increase in compression ratio has been widely recognized as one of the essential technologies for improving the thermal efficiency of heavy-duty diesel engines. However, a higher compression ratio tends to result in increased cooling loss, which could diminish the thermal efficiency gains. Therefore, novel approaches which can reduce cooling loss while maintaining or improving thermal efficiency are required. In our previous studies, it was found that an offset orifice nozzle, in which the orifices are drilled with a small offset from the radial center of the nozzle, improves thermal efficiency and reduces cooling loss simultaneously. However, key factors contributing to the significant reduction in cooling loss have not been sufficiently clarified. This study aims to investigate the mechanism of cooling loss reduction associated with changes in flame distribution when using an offset orifice nozzle in heavy-duty diesel engines, based on in-cylinder combustion observations

Mukayama, Tomoyuki, Enomoto, Yoshiteru, Mikami, Naotaka, Nomoto, Shigeru, Uchida, Noboru

Integration of energy consumption simulation in optimal charging planning for battery electric long-haul trucks

2026-37-0019To be published on 06/09/2026

Vehicle fleet decarbonization is a key objective for the coming years, with electrification representing the primary pathway to achieving the targets set by the European Union. The share of battery electric trucks in new registrations has been gradually increasing especially in light and medium size trucks. The replacement rate of diesel long-haul trucks with zero emission trucks is still low due to challenges posed by added complexity and limitations of battery charging. Depot overnight charging is not sufficient to cover the energy needs of a truck covering large distances and careful planning of the route using public charging infrastructure is crucial for an optimized route minimizing extra costs and range anxiety. The current work aims to develop a methodology to propose the optimal charging locations for a given route of a battery electric truck based on nearby stations along the route. Our study uses an open-source optimization algorithm for the fixed route vehicle charging

Perdikopoulos, Michail, Doulgeris, Stylianos, Livitsanos, Georgios, Kazakis, Thomas, Mellios, Giorgos, Ntziachristos, Leonidas

An open-source framework to Automate Virtual Optimization of Internal Combustion Engine Calibration Maps through shape-based strategy

2026-37-0001To be published on 06/09/2026

The automotive industry is facing increasingly stringent regulatory constraints, driving the need for faster and more efficient powertrain development. This results in higher systems complexity, making internal combustion engine calibration progressively more challenging to meet performance and emissions targets. This, combined with the manual nature of traditional calibration workflows, leads to a time‑consuming process that heavily relies on human expertise. Although virtualization can reduce development time and costs, the overall workflow remains largely dependent on manual decision‑making and iterative refinement. In this context, this work presents a virtual calibration framework based on a genetic algorithm, aimed at the automated optimization of engine calibration maps to satisfy performance and emissions constraints, while reducing manual effort. Each calibration map is represented through a polynomial parameterization. Specifically, a generic three‑dimensional polynomial with

Romano, Gianvito, Aglietti, Filippo, Spedicato, Tonio, cozza, Ivan Flaminio, Capra, Andrea

Representative Driving Cycles for Light Commercial Vehicles Based on Their Usage Profiles

2026-01-0751To be published on 06/01/2026

This study describes a methodology for synthesizing representative driving cycles for light commercial vehicles. The focus is on taking the usage profiles of these vehicles into account in the driving cycle synthesis. In this methodology, representative routes are simulated using the example of light commercial vehicles in the craft sector. The results of these simulations are representative speed distributions and representative height fluctuations. These results are then used as target values for the actual driving cycle synthesis. Furthermore, measurement runs are carried out with a light commercial vehicle to create a database of real-world driving data. The measurement runs include different urban, rural, and motorway sections and cover a total distance of approximately 510 km. Routes with flatter and more challenging elevation profiles are driven. During the measurement runs, the speed signal and the altitude signal are measured. These signals are then processed and cut into

Heilmann, Oliver, Grabow, Andreas, Cortès, Sven, Schlick, Michael, Stoll, Tobias, Kulzer, André Casal

Synergistic Integration of Advanced Combustion and Air-Path Technologies to Maximize ICE Efficiency in Hybrid Applications

2026-01-0733To be published on 06/01/2026

This work investigates the efficiency potential of reciprocating internal combustion engines (ICE) for light-duty vehicle propulsion within the current state of the art and a five-year technological horizon. A validated one-dimensional engine model was extended to integrate multiple advanced technologies, including active prechambers for ultra-lean gasoline combustion, thermal coating, an electric turbocharger capable of supplementary turbine work recovery, variable compression ratio, and variable valve timing implementing a Miller cycle. These enhancements are systematically evaluated to explore their individual and combined effects on engine performance and efficiency. Parametric studies quantify the influence of heat transfer, air–fuel ratio, compression ratio, and other key variables on engine output, enabling the generation of enhanced engine performance maps. These maps reflect synergistic interactions between parameter sets and serve as predictive tools for assessing optimized

Pla, Benjamin, Dolz, Vicente, Serrano, Jose R., Gómez-Vilanova, Alejandro, Oliva, Fermin, Cardenas, Maria, Ariztegui, Javier

Enhancing Efficiency in Megawatt-Scale EV Charging via Adaptive Multi-Agent Control of DC/DC Converters

2026-01-0769To be published on 06/01/2026

The rapid adoption of electric vehicles (EVs) demands high-power charging solutions that are efficient, scalable, and reliable. This work introduces a comprehensive simulation framework for megawatt-scale charging systems, focusing on the integration and control of multiple DC/DC converters. With the primary objective of maximizing overall system efficiency during megawatt-scale charging operations. The study addresses key challenges in multi-converter operation, including load distribution, synchronization, and system efficiency. A multi-agent adaptive control strategy was implemented to dynamically optimize operating points and allocate charging currents across converters in real time so that each participating converter operates at its optimal operating point where the maximum efficiency is delivered. This multi-agent adaptive control strategy allocates not only the individual optimal operating points of the multiple DC/DC converters but rather determines the optimal number of

Salah, Alia, Abu Mohareb, Omar

Feasibility study on the mechanical adjustment of the vibration behaviour of a powertrain test rig for validating longitudinal vehicle shuffle

2026-01-0735To be published on 06/01/2026

Vehicle manufacturers use Hardware-in-the-loop (HiL) approaches to validate overall vehicle characteristics, including those dependent on the powertrain, at an early stage of vehicle development. A powertrain test rig is a typical example. In the specific setup, the vehicle engine and side shafts are mechanically coupled to the load machines of the test rig, eliminating the physical influence of the rims, tires and vehicle body. Adapting a specimen to the test rig changes some characteristics. This affects the specimen's vibration behaviour, making it more challenging to validate comfort-related characteristics. A particular example is longitudinal vehicle shuffle; the powertrain's first torsional natural frequency causes it. The natural frequencies of the real vehicle and device under test differ significantly, so a road-matching approach is not directly feasible. To account not only for tire-road contact but also for the missing vehicle mass, some scientific studies propose a purely

Hübner, Carl, Prokop, Günther

Production-Ready Automated ECU Calibration using Residual Reinforcement Learning

2026-01-0780To be published on 06/01/2026

Electronic Control Units (ECUs) have played a pivotal role in transforming motorcars of yore into the modern vehicles we see on our roads today. They actively regulate the actuation of individual components and thus determine the characteristics of the whole system. In this, the behavior of the control functions heavily depends on their calibration parameters which engineers traditionally design by hand. This is taking place in an environment of rising customer expectations and steadily shorter product development cycles. At the same time, legislative requirements are increasing while emission standards are getting stricter. Considering the number of vehicle variants on top of all that, the conventional method is losing its practical and financial viability. Prior work has already demonstrated that optimal control functions can be automatically developed with reinforcement learning (RL); since the resulting functions are represented by artificial neural networks, they lack

Kampmeier, Andreas, Badalian, Kevin, Koch, Lucas, Lee, Sung-Yong, Andert, Jakob

Methodological framework for the derivation of objective criteria for ride comfort assessment

2026-01-0765To be published on 06/01/2026

In recent years, the automotive industry has faced increasing pressure to accelerate development cycles and reduce associated costs, while simultaneously meeting rising standards of ride comfort driven by the ongoing integration of autonomous driving functionalities. Consequently, it has become essential to ensure that ride comfort attains a high degree of maturity at the very early stages of the automotive development process. This necessitates the establishment of objective criteria that enable the reliable estimation of subjective ride comfort, utilizing simulation-based assessment methods. This study introduces a methodological framework designed to systematically translate the manufacturer specific subjective perception and assessment of ride comfort into objective descriptions using a dynamic driving simulator. The framework is conceived as a generic approach, enabling the comprehensive application to a wide spectrum of subjective ride comfort phenomena, while being specifically

Stroesser, Simon, Zwosta, Tobias, Angrick, Christian, Neubeck, Jens, Wagner, Andreas

Multi-Agent LLM workflow for Regulatory-Driven Requirement Generation in Automotive Software Development

2026-01-0787To be published on 06/01/2026

The increasing regulatory complexity in automotive development places significant pressure on engineering teams to derive complete and correct requirements. This paper presents a multi-agent-based large language model (LLM) workflow designed to support requirement extraction from technical specifications and regulatory documents in compliance with automotive requirement guidelines. The approach structures the requirement derivation process across collaborating agents that interpret specification and regulatory text, generate candidate requirements for the early engineering activities, and cross-validate their outputs to improve consistency and traceability. To evaluate the applicability of the workflow in an industrial context, we applied it to the draft Euro 7 emissions regulation. The agents produced requirements for relevant functional domains, which were subsequently reviewed by domain experts at FEV. The evaluation focused on correctness, completeness, and coverage. Results

Abdalla, Abdelrahman, Schäfers, Lukas, Schmidt, Fabian, Schaub, Joschka, Lee, Sung-Yong, Andert, Jakob

From Wind Tunnels to an AI-Supported Data-Driven Development Ecosystem How Software Transforms Aerodynamic Testing into a Strategic Asset

2026-01-0767To be published on 06/01/2026

Despite advances in CFD, wind tunnel testing remains indispensable for aerodynamic validation, correlation, and homologation. Increasing configuration complexity, shortened development cycles, and stringent result robustness and documentation requirements demand a shift from isolated facilities to integrated, data-driven ecosystems within the overall development and company-wide test processes. We present a software-centric approach integrating wind tunnel operations into a strategic element of the Digital Thread. By orchestrating test planning, execution, data acquisition, and documentation within a unified framework, experimental data becomes reusable across projects and traceable for compliance and homologation. The interaction between CFD and physical testing is important. Such systematically improves simulation models with wind tunnel tests. And CFD results guide efficient test matrix definition. Extended measurement methodologies include automated actuation of active aerodynamic

Jacob, Jan D.

Optimization of a Halbach array for torque ripple reduction in a Yokeless And Segmented Armature axial flux machine

2026-01-0754To be published on 06/01/2026

This paper presents the optimization of a Halbach magnet array applied to an axial flux machine (AFM) in a 12-pole, 18-slots yokeless and segmented armature (YASA) topology, evaluated in the torque–speed characteristics diagram. AFMs offer significant advantages in terms of compact design and high torque density compared to other permanent magnet machine topologies. However, noise, vibration, and harshness (NVH) performance is strongly influenced by cogging torque, electromagnetic torque ripple, and tooth forces. While Halbach magnet arrays are well established in high-performance radial flux machines, only limited research has investigated their influence in AFMs. A Halbach array concentrates magnetic flux on one side of the magnet arrangement, leading to increased air-gap flux density and a more sinusoidal magnetic field distribution. By using a Halbach array, the magnetic field distribution in the air gap becomes more sinusoidal, thereby reducing harmonic components. Previous

Müller, Karsten, Schulz, Fabian, Bremer, Martin, Burkhardt, Yves, De Gersem, Herbert

Vision-Language Model Training for Code Generation from Requirements and Model-Based Artifacts

2026-01-0770To be published on 06/01/2026

Recent advancements in Vision-Language Models (VLMs) have opened new possibilities for bridging the gap between Systems Engineering artifacts and automated code generation. Traditional Large Language Models (LLMs) are primarily trained on textual data and generic code repositories, which limits their ability to interpret graphical engineering artifacts such as Simulink block diagrams or system architecture models. In safety-critical domains like the automotive industry, these graphical models are central to development workflows and must remain closely aligned with textual requirements and implementation code to ensure traceability, compliance, and functional correctness. This paper proposes a VLM-centered multimodal training framework for code generation that integrates textual requirements, graphical model-based artifacts, and annotated source code into a unified learning process. By leveraging models which combine vision encoders with language backbones, the approach enables the

Padubrin, Marcel, Kulzer, Andre Casal, Guerocak, Erol

Multi-Agent LLM Pipeline for Systems Engineering Automation

2026-01-0777To be published on 06/01/2026

The increasing complexity of modern software-intensive systems, particularly in the automotive domain, demands new approaches to bridge the gap between high-level engineering specifications and executable, safety-compliant code. This need is amplified by the rapid transition toward software-defined vehicles (SDVs), where highly dynamic, updateable software functions significantly enlarge the scope and frequency of engineering activities and require scalable, transparent, and adaptive development processes. While recent advances in Large Language Models (LLMs) have demonstrated strong capabilities in automating tasks such as requirements analysis, code generation, and documentation, their deployment in safety-critical engineering workflows remains challenging due to the need for transparency, traceability, and controlled decision-making. This paper presents a modular multi-agent LLM pipeline that automates key steps of the systems engineering lifecycle - from requirement structuring and

Padubrin, Marcel, Kulzer, Andre Casal, Guerocak, Erol

Development of a digital twin of an electrical powertrain in a testbench environment for efficiency estimations

2026-01-0744To be published on 06/01/2026

The aim of this work is to develop a modular digital twin of an electric powertrain based on novel machine learning (ML)-based model structures and a systematic, component-oriented architecture. This results in a modular, real-time-capable digital twin that precisely maps the efficiency characteristics of the entire drivetrain and can be used in development and testing processes. The further goal here is to enable virtual testing, which can be used for frontloading and thus both prevent errors and increase the speed of product development. Based on a comprehensive set of measurement data from a test bench, a multi-stage procedure is implemented that integrates data acquisition, physically informed feature selection, modeling at the component and subsystem level, and hybrid coupling strategies. The digital twin captures inverter, electric machine, and mechanical transmission stages and generates physically consistent predictions of key variables such as torque, speed, power factors, and

Kopp, Lennart, Proksch, Daniel, Ockert, Niels, Karthaus PhD, Carsten, Kley, Markus

Wear Resilience Characteristics of 2% Silicon Nitride Strengthened AZ31 Magnesium Composite

2026-26-0740To be published on 06/01/2026

To develop magnesium matrix composites, ceramic silicon nitride (Si3N4) particles are used to strengthen the magnesium (AZ31) matrix by adding 2 wt.%. The composite is developed by disintegrated melt deposition vacuum stir casting method. Microstructural studies reveal the presence of Si3N4 particles and their uniform distribution. A L9 orthogonal array planned using Taguchi’s experimental design is chosen for three wear parameters axial load (AL), rotational speed (RS), and time duration (TD) for trials as per G99 standard in pin-on-disc apparatus. Experimental outcomes show a rise in axial stress; wear loss increases dramatically. Because the area of contact shrinks as sliding speed increases, wear loss diminishes dramatically. When the AL is low, CoF increases, and when the AL is large, CoF drops. When the sliding speed is increased, CoF decreases. To optimize multiple responses effectively, the TOPSIS method (Technique for Order of Preference by Similarity to Ideal Solution) was

Senthilkumar, N., Dhinakar Raj, C K

Integrating Formal Methods into Model Based Systems Engineering(MBSE) to Mitigate Requirement Escapes

2026-26-0712To be published on 06/01/2026

The industry is undergoing a significant digital transformation in the way system requirements are defined, communicated, and managed. Major original equipment manufacturers (OEMs) are moving toward fully model-based development processes, with plans to deliver requirements exclusively in the form of system models. It is no longer sufficient to manage requirements using traditional document-based approaches; instead, organizations must adopt tools and processes that enable the consumption, interpretation, and implementation of model-based requirements. Preparing to meet this industry direction is critical to maintaining competitiveness and alignment with our customers’ expectations. Model-Based Systems Engineering (MBSE) plays a central role in this transition. MBSE enables engineers to define, analyze, and manage system requirements, architectures, behaviors, and verification strategies within a cohesive model-based framework. It supports traceability, promotes consistency across

Gupta, Chandan, Nakkeeran, Rupashree

Agentic Digital thread for self-healing non-conformities in aerospace product manufacturing lifecycle

2026-26-0763To be published on 06/01/2026

Aircraft manufacturing involves a complex ecosystem where quality, traceability and compliance are paramount. Managing non-conformities both before and after manufacturing requires a digitally connected data framework that ensures seamless data flow, decision-making and corrective actions across different functions like sourcing, materials, engineering and production. The advent of agentic AI brings more autonomous solutions through drastically reduced human prompts. This paper presents a comprehensive Agentic Digital thread framework for managing pre and post manufacturing non-conformities, enabling real-time visibility and control from design to delivery. The Agentic digital thread transforms from being passive connectivity layer into living intelligence ecosystem that continuously learns, adapts, and improves in real time Pre-manufacturing nonconformity management focuses on early detection of issues through Agentic AI in sourcing, materials and design through integration of PLM

Veluri, Sastry, Gopala Krishnan, Kannan

Predictive Obsolescence Management

2026-26-0780To be published on 06/01/2026

This abstract is towards Aerospace Sustainability - End-of-Life Management for Aerospace Assets. In the field of Aerospace, which has a long Life-Cycle process [20-30Years], Component Obsolescence has become a major problem as it prevents Maintenance & sustenance of a product with committed life-cycle period. Obsolescence Management plays a vital role by deriving strategic plans on proactive obsolescence where the system needs to be supported for several decades. This abstract analyzes the obsolescence challenges in the Aviation industry especially in Avionics System impacted by component obsolescence and present the possible proactive obsolescence management in terms of Engineering, Technology, and business/cost elements. The Obsolescence problem cannot be avoided but the impact of obsolescence and mitigate the risk can be minimized by planning and managing response. The obsolescence risk assessment for the Bill Of Materials (BOM) is a paramount activity to manage obsolescence

Dharmananyala, Rohith, Munirathnam, Krishna, Marokeyfrancis, Joisyjose, Sadashivaiah, Nagesh, Kondamari, Harshitha

Integrating Human Factors and Predictive Modeling for Enhanced Aviation Safety: A Data-Driven Approach to Risk Mitigation and System Design

2026-26-0795To be published on 06/01/2026

The evolution of the aviation industry from manual control to highly automated and autonomous systems has brought human factors to the forefront of safety, design, and operational efficiency. This paper presents an integrated analysis combining machine learning techniques with statistical modeling to assess the impact of human-system interaction on aviation incident outcomes. The first phase of this research involved a longitudinal dataset covering aircraft design evolution, training programs, and incident records. Using classification algorithms, including XGBoost, the study achieved an accuracy of 81.82% in predicting incident likelihood. Performance metrics such as F1-scores (0.85 for “Incident” and 0.78 for “No Incident”) highlighted the model’s robustness in identifying risk patterns associated with human factors and system complexity. In the second phase, 50 plus detailed aviation incident reports were analyzed using logistic regression, t-tests, and Cook’s Distance to identify

Valiyaparambil, Praveen

AI-Led Sustainability Strategy: Driving Product Value from Birth to Disposal

2026-26-0781To be published on 06/01/2026

Product development in Aerospace, Oil & Gas, Turbomachinery and Aviation sectors are associated with complex engineering challenges and long lead time to materialize products. More so in Aerospace and Aviation sectors, unlike other sectors, they are highly cost intensive, consuming few millions of dollars for introducing minor modifications to billions of dollars for a major modifications along with regulatory constrains. In addition, the product development cycle is spread over years ranging from a couple of years for minor modification to nearly a decade or more for major modifications. The profitability of the product is derived through its Safety, Reliability and Availability in operation. The product is maintained in service until it sustains value in its operation and is discarded if the value deteriorates and the cost of upkeep exceeds the product value. Product Value in the present context is not the price tag but encompasses intangible elements like brand value, reputation etc

Srinivasan, Karthik, G.V.V., Ravi Kumar, Vaderahobli, Devaraja Holla, Bhate, Ujwal, Veluri, Sastry

TRANSFORMING AIRCRAFT MRO WITH AGENTIC AI AND CONTEXT ENGINEERING

2026-26-0788To be published on 06/01/2026

Aircraft Maintenance, Repair, and Overhaul (MRO) operations are highly complex, involving coordination among multiple stakeholders including airlines, MRO providers, OEMs, and regulatory authorities. A significant challenge in this space is managing unplanned events such as Aircraft on Ground (AOG) conditions, where delays can lead to major financial losses to airlines and safety risks. Engineers must quickly diagnose the damage, evaluate compliance against regulatory limits, coordinate with OEMs, and make critical decisions—all while navigating a fragmented ecosystem of disconnected systems, diverse document types, and time-sensitive processes. This paper presents a real-world, intelligent MRO solution that addresses these challenges through the use of Agentic AI and context engineering. The system is designed to automate and augment key MRO workflows such as damage detection, repair pathway selection, compliance verification, and supplier coordination. At its core, the solution is

Abburu, Sunitha, G.V.V., Ravi Kumar, Poovalingam, Sundaresan, Vaderahobli, Devaraja Holla

Numerical Analysis of Pre-Ignition Factors in Hydrogen Engines

2026-01-0740To be published on 06/01/2026

Numerical analysis was conducted to investigate abnormal combustion, a major challenge in efforts to improve hydrogen engine efficiency. Focusing on two factors that induce abnormal combustion—surface reactions and lubricating oil—numerical analysis examined the potential for each to trigger abnormal combustion. Furthermore, since it was confirmed that the self-ignition prediction using a detailed chemical reaction mechanism deviates from experiments at temperatures around 800K, attempts were made to improve this issue. As a result, it was confirmed that surface reactions affect the chemical species ratio near the wall surface but have little effect on flame propagation. Regarding lubricating oil, two possibilities were investigated: the lubricating oil itself self-igniting and becoming an ignition source for the hydrogen mixture, and deposits generated from the lubricating oil generating heat and becoming an ignition source. We investigated two possibilities: the lubricating oil

Moriyoshi, Yasuo, Yamane, Taichi, Wang, Zhiyuan, Kuboyama, Tatsuya

Application of Deterministic and AI-Based Methods to Accelerate the Component-Level Development Process in Electric Vehicles

2026-01-0766To be published on 06/01/2026

The global automotive landscape is undergoing a significant paradigm shift driven by the rapid development cycles of emerging competitors, leaving traditional European OEMs with a critical time-to-market gap. To bridge this gap, automotive engineering must pivot from traditional hardware-based processes toward agile, digital data-driven methodologies. This paper presents a comprehensive feasibility study on the implementation of data-centric approaches, using both Artificial Intelligence and deterministic methodologies, with focus on accelerating component development. It provides a detailed assessment of various AI-based and deterministic methodologies at specific stages of the product development process, targeting both: 1. Product design 2. Product development process The aim is to enhance component-level performance, quality, and functionality, while simultaneously optimizing workflows, increasing process and development efficiency, and facilitating knowledge reuse throughout the

Bode, Jana Pascal, Kröll, Sarah, Vohwinkel, Nikolaus, Paetzold-Byhain, Kristin

Identifying and mitigating resonance effects in HV Powernets

2026-01-0742To be published on 06/01/2026

HV Power nets of electric vehicles consists of various HV components such as batteries, inverters, auxiliaries and cables. During in-vehicle testing, multiple failures of an auxiliary inverter were observed, caused by resonance issues within the component filter. Initial investigations revealed that these resonances, absent during manufacturer testbench evaluations, were influenced by the vehicle power net and its impedance characteristics. To better understand the underlying causes and identify preventative measures, extensive simulations were performed. The results demonstrate a diminishing influence of the power net capacitance when significantly larger than the component capacitance. Also they highlight the critical impact of cable inductance on the component resonance frequency when comparable to the component’s inductance. A simplified electrical equivalent circuit was used to derive an equation predicting the resonance frequency as a function of the component’s capacitance

Schmiel, Fabian, Aurand PhD, Tobias, Koehnlechner, Benjamin, Zimmer PhD, Markus

Methodology for Battery System Optimization and Evaluation Using Format Flexible Pouch Cells

2026-01-0749To be published on 06/01/2026

Electrification using battery systems is one of the most relevant solutions regarding ecological challenges within multiple application cases such as mobility, tools or stationary power supply. Nonetheless besides recent achievements in some cases battery systems are still lacking behind operational requirements compared to conventional propulsion systems, therefore limiting the potential of electrification. Especially when purpose design possibilities are limited. Besides improving properties of cell materials, better usage of the available installation space offers potential for overall optimization of the battery system. The development of battery systems is complex, as it involves multiple system levels and domains, along with a wide range of design options and architectures. Battery cells that can be manufactured in flexible formats offer the potential to make more efficient use of available installation spaces. At the same time, these additional degrees of freedom increase design

Müller-Welt, Philip, Bause, Katharina, Spohn, Hannes, Albers, Albert

Interpretable Tire Force Modeling for Formula Student Vehicle Dynamics and Lap Time Applications

2026-01-0762To be published on 06/01/2026

Accurate tire models are a key enabler for vehicle dynamics simulation, control design, and lap time optimization, particularly in the context of Formula Student race cars, where vehicle setups and tire characteristics differ significantly from production vehicles. State-of-the-art tire models, such as Pacejka’s Magic Formula, provide high accuracy but rely on predefined functional structures and a large number of parameters, which limits interpretability and adaptability to new tire compounds. This paper presents a data-driven approach for deriving compact and physically interpretable tire force models using symbolic regression. The proposed method employs an intelligent tree search to systematically explore the space of mathematical expressions and identify models that optimally balance prediction accuracy and structural simplicity. In contrast to black-box machine learning approaches, the resulting models consist of explicit mathematical expressions that enable physical

Anselment, Marcel, Borowski, Julian, Rudolph, Stephan

An Analysis and Comparison of Free-Space Detection Approaches and Their Evaluation for Heavy-Duty Vehicles

2026-01-0763To be published on 06/01/2026

The detection of free space plays a fundamental role in ensuring the safe and efficient operation of heavy-duty vehicles, particularly in environments where the available area to maneuver is severely constrained, such as construction zones, rest areas, or loading docks. An accurate estimation of free space is essential to prevent collisions, maintaining operational continuity and minimizing vehicle downtime. As observed from the reviewed literature, despite the large number of proposed free-space detection methods, there is no concise and established definition about how free space should be determined, represented, and inferred, nor agreement on the semantic classes to be considered. This heterogeneity complicates systematic comparison and benchmarking across approaches. This paper presents a structured survey and methodological analysis of recent free-space detection and semantic segmentation approaches across automotive LiDAR-, camera-, and radar-based perception systems, as well as

Martinez, Cristian, Peters, Steven

Mind the Gap: Quantifying Behavioral Fidelity in CARLA using Naturalistic Drone Data

2026-01-0771To be published on 06/01/2026

The rapid advancement of autonomous driving systems, particularly SAE Levels 3 and 4, requires rigorous validation strategies to ensure safety during handover and fallback procedures. Although virtual environments provide the necessary scalability, the simulation-reality gap poses a significant risk to the reliability of human-in-the-loop (HITL) assessments. This paper presents a comprehensive, empirical investigation into the transferability of human driving behavior from the Car-Learning-to-Act (CARLA) simulation environment to real-world traffic situations. Using the exiD dataset, which comprises high-precision, drone-recorded, naturalistic trajectories from German highways, we systematically reconstructed distinct traffic scenarios. These scenarios included lane changes, cut-in maneuvers, and curve navigation. Twenty-five participants navigated these scenarios in a driving simulator. The study employed a robust, multidimensional analytical framework to compare the simulated

Rebling, Patrick, Alphan, Metehan, Nenninger, Philipp

Augmented Reality and Multimodal Interfaces for Astronaut Training and In-Orbit Operations

2026-26-0796To be published on 06/01/2026

Augmented Reality (AR) and multimodal human–machine interfaces (HMI)—combining visual overlays, voice, gesture, eye-tracking, and biometric sensing—are now maturing into flight-relevant technologies capable of transforming astronaut training and in-orbit operations. These interfaces reduce task time, lower error rates, and mitigate cognitive load, thereby strengthening both crew autonomy and mission safety. For India, the timing is critical: Gaganyaan human spaceflight program is approaching decisive crewed missions in the late 2020s, and the Bharatiya Antariksh Station (BAS) is targeted for the 2030s. AR/MMI can be positioned not as an accessory but as a core enabling system for training throughput, real-time crew support, and resilient station operations. This paper provides (i) an overview of India’s current scenario, including Gaganyaan astronaut training ecosystems, IIT Madras collaborations on AR/VR, and Vyommitra humanoid readiness; (ii) synthesis of global case studies—NASA

Yadav, Anoop Singh

Digitalization of Mechanical Labs for Enhanced Traceability and Virtual Verification

2026-26-0715To be published on 06/01/2026

Digitalization is the process of leveraging digital technologies to transform business operations, processes, and models, enabling organizations to improve efficiency, create new value, and enhance customer experiences. It is essential as it enables data-driven decisions and reduces product development time. It’s easier to Digitalize new products however, transforming existing products and processes is a challenging task, as constituents are in various phases of lifecycle. Also, the existing/ legacy data acts as a starting point for future programs. Currently, teams are spending hours to weeks finding the right processes and data, costing $~14,000 per test based on labor hours. To tackle this challenge, Mechanical labs are digitalizing their data and processes alongside physical tests via 3DEXPERIENCE application to capture data in digital models and ensure traceability for which Requirement Functional Logical Physical (RFLP) framework is leveraged. This traces Requirements to its

Karpur, Anoop, Inapakolla, Bharat Kumar, Harris, Jason

System and Method of Strain Emulation using a Beam Load Cell Test Setup

2026-26-0743To be published on 06/01/2026

This novel method deals with emulation of Strain of a Structural Measurement System which includes software validation, acceptance tests and training. Current methods for simulating strain and force data for developing and verifying data acquisition (DAQ) software typically rely on costly electronic simulators or specialized hardware, making it challenging and expensive for developers, researchers, and small organizations to test their solutions under realistic conditions. To verify DAQ software, multiple specialized hardware solutions are deployed, that include Electronic Simulators, Commercial DAQ Modules and Hydraulic/Pneumatic test rigs. These technologies pose a challenge with limited flexibility and scalability options for small-scale prototyping, especially in budget-constrained scenarios. The sensors on these equipment may or may not be company approved inducing acceptance challenges. Our invention is an inexpensive, scalable, and mechanically simple alternative. Using a 3D

Murthy, Harsha, Bhat Venkatesh, Aditi, K Padmanabhan, Rahul, Madhu, Sheetal, Garag, Naveen

Automated Flight Maneuver Recognition for Intelligent Flight Simulator

2026-26-0713To be published on 06/01/2026

The rapid growth in the number of aircrafts and pilots emphasizes the need for an AI-enabled training framework that can offer precise, automated examination of flight maneuvers, thereby optimizing the pilot’s training efficiency and minimizing iterations of the conduct of flight maneuvers thereby reducing the training time of the pilot for a flight. This paper introduces a novel hybrid methodology to intelligently detect manoeuvres performed during a flight using signal processing and machine learning techniques. A general framework is developed that can be used for all kinds of flight phases and aircraft types. The framework is implemented in a two-step approach. Firstly, Continuous Wavelet Transform (CWT) of the signals of interest for different manoeuvres are generated, to find the time at which a manoeuvre happens during a sortie. CWT looks at the energy levels in the transformed signal and uses smart thresholding technique to pick out only the parts with high energy, to extract

Sahu, Akash, C, Poornima, C, Aravindh, Kaliyari, Dushyant, TK, Khadeeja Nusrath

Automating Compliance Workflows in Aerospace Systems: A Scalable Framework for Specification Review and Requirement Mapping

2026-26-0714To be published on 06/01/2026

Compliance verification in aerospace systems often relies on labor-intensive workflows that demand extensive manual effort to produce structured review documentation and requirement matrices. These processes can span dozens of hours per review, are vulnerable to inconsistencies due to non-standardized annotations, and depend heavily on individual interpretation of fragmented technical sources. With a growing backlog of review tasks and a steady influx of new requests, the need for scalable automation has become increasingly urgent. This study presents a modular automation framework designed to streamline compliance assessments through intelligent document parsing, requirement extraction, and matrix generation. The system integrates optical character recognition, computer vision, and natural language processing techniques to process both scanned and digital documents. By digitizing data across multiple hardware configurations and automating extraction from diverse technical records, the

Mirani, Harsh, Baviskar, Yash G.

Enabling Circularity in the Aerospace Maintenance Ecosystem through Generative-AI Driven Back-to-Birth Traceability of Life-Limited Engine Parts

2026-26-0719To be published on 06/01/2026

Circular economy principles are increasingly central to aerospace sustainability strategies, aiming to extend asset life, improve asset valuations, and enhance benefits to stakeholders in the part ownership and maintenance lifecycle. In aircraft engines, achieving circularity hinges on the safe reuse, repair, and recirculation of high-value components. Life-Limited Parts (LLPs) are among the most critical in this context, but their reuse is strictly contingent on complete Back-to-Birth (BtB) traceability. Any gap in BtB records—often due to fragmented data across multiple airline operators, shop visits, document formats, and time expanse—renders otherwise serviceable LLPs unusable, leading to premature scrappage and lost circular value. This paper presents a Generative AI (GenAI)-driven methodology to reconstruct and validate complete LLP BtB histories from heterogeneous, unstructured, and legacy maintenance datasets. By combining aerospace domain-trained language models with embedded

Bhate, Ujwal, Jain, Dilip Kumar, Kulkarni, Ninad, Kalaiyarasan, Aravindh, Jha, Ashish, Shenoy, Karthik

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