Browse Topic: Systems engineering

Items (1,702)

Implementation Strategies of Requirements Engineering in Commercial Aircraft Airframe Forward Design

2026-99-0183

7/31/2026

Focusing on the requirements engineering activities, this study analyzed the problems in the implementation process of the forward design practice of commercial aircraft airframe, introduced the breakthrough methods, including the convergence and integration with the traditional design process, the supporting work organization model, process optimization, and specification, and proposed the airframe stakeholder need capture model based on the theory of systems engineering. Practice has shown that the requirements engineering implementation strategy introduced in this paper can effectively resolve conflicts and redundancies between the requirements system and the original top-level document system requirements. It ensures clear requirements sources, sufficient basis, reasonable allocation, controllable changes, adequate change assessments, clear design status, and controllable design risks. It effectively overcomes human resource bottlenecks during the early stage of requirements engineering implementation while cultivating talent reserves for systems engineering implementation, saving approximately 23.5 person-years in labor costs. It significantly optimizes non-value-added processes, reducing approximately 100 reports. It unifies the team’s understanding of requirements work, improves coordination efficiency, and significantly improves the requirements validation rate between aircraft-level and system-level requirements by an average of approximately 46%. It assists stakeholders and engineers in systematically and scientifically capturing product requirements during the design phase, with original product design specifications covering approximately 70% of subsystem specifications on average. Given its generality across the airframe forward design domain, the airframe requirement management paradigm established by this implementation strategy holds significant importance for the comprehensive and in-depth application of systems engineering methods in commercial aircraft development.

Sun, Luyan, Chang, Liang

Advancing Reliability Centered Maintenance: A Robust Statistical Approach for MTBF Estimation in Aero-Engine Fleets via Subgrouping Strategy and Dual Weibull Modeling

2026-99-0213

7/31/2026

Fleet heterogeneity, from manufacturing variations and diverse operating conditions, complicates reliability analysis by obscuring true failure patterns in aero-engines. This is a critical challenge in an industry as inaccurate Mean Time Between Failures (MTBF) estimates threaten safety and inflate operational costs, by forcing a choice between inefficiently conservative maintenance or the risk of in-service failures. Conventional analysis often fails by pooling all fleet data. To address this, our paper presents an analytical framework that improves predictive accuracy by filtering, rather than aggregating statistical noise. The methodology uses a Randomized Block Design (RBD) and ANOVA hypothesis test to screen a diverse dataset and isolate statistically homogeneous subgroups. This filtration identifies a core fleet with a consistent failure signature, providing a purified dataset for modeling. This refined data is then modeled using both Weibull and the Exponentiated Inverse Weibull distributions to ensure the results are robust and not model-dependent. Applying this framework to a 25-engine dataset that experienced 66 failures, we isolated a stable failure pattern, yielding a primary MTBF of 171.16 hours and a cross-validated MTBF of 176.35 hours. The close 3% convergence between these models validates our approach. By providing a dependable MTBF, this work establishes a stronger foundation for data-driven Reliability Centered Maintenance (RCM). It empowers maintenance planners to move toward evidence-based intervals, safely extending engine time-on-wing, optimizing spare parts inventory, and significantly reducing direct operational costs for airlines.

Jubaid, Mayin Uddin, Bebe, Gibson, Bigyen, Musa Pethuel, Anik, S M Kullul Mehedee, Yasmin, Ashrafi, Sahran, Mohamed Sideek Mohamed

Construction of a Digital Management Platform for Intelligent Workshops in the Electronics Industry

2026-99-0117

7/31/2026

Under China’s intelligent manufacturing strategy, manufacturing enterprises are expected to achieve digital and networked operations by 2025, with full digital transformation by 2030. Intelligent factories, the core of this transformation, rely on interconnected, integrated, and data-fused systems. This paper focuses on the micro-assembly intelligent workshop at the Nanjing Research Institute of Electronics Technology, which produces micro-circuit modules for large-scale complex electronic systems. The workshop combines discrete and process manufacturing modes, presenting unique challenges for digital management. A digital management platform based on a five-layer architecture (device, network, data, application, and decision layers) is proposed to address multi-dimensional business needs, including production scheduling, logistics, execution, and decision optimization. A hierarchical workflow structure of the workshop, consisting of a main workflow and several sub-processes, is in-depth studied and designed. The platform is constructed based on requirements analysis and workflow design of the workshop and integrates systems such as MES, APS, WMS, and SCADA, supported by AI-driven big data analytics. This study offers a practical framework for advancing digital transformation in the electronics industry.

Zhang, Jian, Wang, Jiafeng, Guo, Yongzhao

Design of a Digital Twin Interactive System for a 3D Printer

2026-99-0859

7/30/2026

This paper takes a 3D Printer proposed by the project team in the early stage as the research object, constructs a digital twin entity including 3D models and data models in order to develop a digital twin interactive software. By activating the real-time correlation between 3D models and data models, valuable data exchange can be achieved between the digital twin entity and the physical entity, and valuable data can be used to drive both to refresh their operating status.

Li, Qi, Wu, WenKai, Lang, ZhiQi, Jiao, HongCheng, Jing, Tao, Zhao, HanTao, Dong, Shen, Shi, Lei

Research on Model State Machine-Based Simulation and Validation of Civil Aircraft Ground Deceleration Functional Architecture

2026-99-0834

7/30/2026

The implementation of the ground deceleration function in civil aircraft represents a critically complex process that deeply relies on the seamless collaboration of multiple onboard systems, including but not limited to braking, thrust reversal, spoiler, and steering systems. The operational logic governing these systems is highly intricate, characterized by tightly coupled interactions, stringent safety requirements, and a vast array of diverse physical and logical interfaces. This inherent complexity makes it exceptionally difficult to gain a thorough, system-level understanding of the implementation mechanisms and collaborative principles solely through traditional means of examining extensive, yet often fragmented, design documentation. The limitations of document-based analysis frequently lead to unforeseen integration conflicts, which are typically discovered late in the development cycle, resulting in substantial rework costs and project delays. To address this pervasive industry challenge, this paper selects the aircraft ground deceleration function as a representative case study and proposes an innovative, simulation-based validation methodology. This approach systematically utilizes model state machines to create a dynamic digital representation of the system-of-systems, enabling rigorous validation of aircraft deceleration requirements under various operational scenarios. By adopting this model-based systems engineering (MBSE) paradigm for mechanism representation, our approach effectively captures the nuanced coordination, timing dependencies, and dynamic interactions within the multi-system operational logic. It thereby facilitates the intuitive identification, analysis, and resolution of potential design flaws, including logical conflicts, deadlocks, race conditions, and uncovered or ambiguous requirements. Consequently, the method not only provides a robust framework for validating the aircraft’s function-related design requirements with greater confidence but also offers crucial, data-driven support for the iterative optimization and evolution of the overall functional architecture. The fundamental value proposition of this research lies in its transformative capability to convert implicit design knowledge and assumptions—originally scattered across voluminous documents, specifications, and expert minds—into an integrated set of executable, observable, and analyzable formal models. This digital thread enables systems engineers and designers to identify deep-seated integration and coordination issues proactively during the early conceptual and detailed design stages, rather than relying on discovery during the late, costly integration and testing phases. By shifting validation left in the development V-cycle, this approach significantly reduces the risk of major design changes and associated cost overruns later in the project lifecycle. Ultimately, it effectively enhances the overall maturity, safety, certifiability, and operational reliability of complex aircraft function development, paving the way for more efficient and predictable engineering processes.

Wang, Mingqian, Yu, Qiao, Yu, Miao, Tang, Chao

Efficiency Modeling, Experimental Validation, and Machine Learning-Assisted Model Calibration of a Two-Speed E-Drive Transmission for Construction Equipment

02-19-03-0019

7/27/2026

This study details the development and experimental validation of a high-fidelity one-dimensional (1D) simulation model for a two-speed transmission designed for off-road vehicles, such as tractors and backhoe loaders used in agricultural and civil engineering applications. The model, implemented in the AMESim platform from Siemens, integrates physics-based loss sub-models for all major components, including gears, bearings, seals, and fluid drag (churning) losses. After development, the model was rigorously validated against test bench data, with efficiency measurements taken across various speed, torque, and oil level combinations, demonstrating a strong correlation with experimental results. A detailed analysis enabled the quantification of the contribution of each loss mechanism, identifying the countershaft gears and input shaft bearings as the primary contributors. Furthermore, a Machine Learning (ML)–based calibration framework, employing Bayesian Optimization, was implemented to reduce discrepancies between simulation and experiment and to generate a synthetic dataset for the creation of fast-executing surrogate models. The study concludes that the proposed methodology constitutes an effective tool for efficiency analysis and optimization during early design stages, establishing a foundation for future integration with ML techniques and the development of digital twins.

Ferreira, Tiago Simao, Fallahi, Farzad, Kedziora, Slawomir, Hichri, Bassem, Kiefer, Jean-Daniel

A Virtual-Physical Mapping Model for Aluminum Alloy Pitting Corrosion

2026-99-1611

7/24/2026

Corrosion critically damages structural strength and affects the structural safety, so there is an urgent need for a method that can accurately model and predict corrosion. Digital twin technology offers new methodologies for corrosion research. This study develops a digital twin-enabled virtual-reality mapping model for simulating aluminum alloy pitting corrosion. The model accounts for the effect of temperature on corrosion and establishes temporal correlations between field conditions and simulations through damage factor (DF) analysis coupled with detailed fatigue rating (DFR) methodology. Experimental validation using 7A04 aluminum specimens confirms the model’s reliability, with maturity analysis demonstrating its applicability in aircraft corrosion research. Through numerical simulation methods, this study simulates the evolutionary law of pitting corrosion development, reflecting the level of structural pitting corrosion damage. This investigation establishes a fundamental theoretical framework for condition monitoring and lifetime prediction of aircraft components affected by pitting corrosion.

Lv, Shengli, Liu, Chenglong, Sun, Jingjue

Digital Twin Simulation and Verification System Design and Implementation for Satellite Production Lines

2026-99-1804

7/17/2026

In this paper, we focus on satellite production lines and design and implement a digital twin simulation and verification system for them. This is to improve manual documentation efficiency and provide sufficient process controllability in the small satellites’ batch production and assembly testing. We built a layered architecture. This allows the system to dynamically interact with AIT data management systems, structured process systems, and equipment data by fusing multi-source data. We also develop functional modules that combine lightweight 3D model visualization, dynamic simulation engines, and hybrid scheduling optimization algorithms. These modules can perform twin simulation, execute processes, intelligently schedule production, manage work reporting, conduct intelligent analysis, trigger anomaly alarms, and perform system management. We also dynamically simulate complex workflows like satellite transfer and automated assembly. These workflows are then verified using 3D virtual scene modeling and physical engines. We use time-series analysis to improve scheduling accuracy and multidimensional dynamic monitoring and hierarchical response to enhance production stability. In practice, the system can provide visualized control over the full process of satellite production. This greatly improves assembly efficiency and process controllability. It can also be an extensible digital way for aerospace manufacturing. The use of hierarchical architecture design and multimodal data fusion can be further applied in the complex equipment intelligent manufacturing.

Zhao, Fenghua

Privacy-Preserving UAV-Based Traffic Data Acquisition Platform

2026-99-0595

7/10/2026

This paper puts forward a Privacy-Preserving UAV-Based Traffic Data Acquisition Platform to address 1) privacy leakage, 2) limited scenario coverage, and 3) low traffic data utilization efficiency in urban traffic monitoring environments. Our system integrates three innovations: 1) Dynamic Privacy Masking (DPM) and Dual-Track acquisition (DTC), which hides sensitive information (e.g., faces, license plates or LPL) in real-time while preserving critical traffic data (e.g., vehicle density, speed), 2) traffic data Localization (DL) and Privacy-Enhanced Federated Learning (FEFL), enabling cross-regional collaboration without raw traffic data sharing by perturbing neural network updates with differential privacy (DP), and 3) Ground-Air Collaboration (GAC) and VPF (VPF), combining UAVs with ground sensors and digital twins (DTs) to cover blind spots (e.g., tunnels, extreme weather). Experimented on UA-DETRAC and CitySim traffic data-sets, the platform achieves 92% privacy compliance (GDPR/PIPL), 87.5% mAP accuracy, and 85% road network coverage, outperforming other methods (e.g., FedUAV, static blurring). It supports applications such as traffic flow optimization, accident prevention, and regulatory alignment.

Zhang, Shilin, Yan, Ming

Development of a Digital Twin of an Electrical Powertrain in a Testbench Environment for Efficiency Estimations

2026-01-0744

7/1/2026

The aim of this work is to develop a modular, real-time-capable digital twin of an electric powertrain based on machine learning (ML)-based model structures and a systematic, component-oriented architecture with a focus on efficiency estimation in test bench environments. 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 measured and derived test bench data, 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 consistent predictions of key variables such as torque, speed, power factors, and subsystem as well as overall drivetrain efficiency. The methodology enables a systematic comparison of black box, dark grey box, grey box, and bright grey box architectures with respect to prediction accuracy, information content, and real-time capability. The methodology provided uses new model structures that explicitly integrate physical dependencies while also using ML models to map nonlinear effects. The hybrid architectures presented have been shown to significantly reduce the measurement effort while achieving nearly identical model quality and surpassing purely physics-based models in terms of accuracy, robustness, and real-time capability. For the final bright grey-box architecture, average relative efficiency errors below 1 % are achieved while maintaining real-time execution rates. The study shows that bright grey box-models in particular offer a best-case compromise between the requirements of information content, error quality, and synchronization rate, thus representing a methodological advance over conventional digital twins, which are often created at the component level. The shown methodology provides an implementable framework for digital twins of electric powertrains in industrial test environments.

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

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

2026-01-0770

7/1/2026

Recent advancements in Vision-Language Models have opened new possibilities for bridging the gap between Systems Engineering artifacts and automated code generation. Traditional Large Language Models 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 Vision-Language Model-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 model to jointly learn the structural semantics of engineering diagrams and the linguistic and syntactic patterns of requirements and code. This alignment allows the model to generate code that is not only syntactically correct but also semantically consistent with both textual specifications and graphical designs. We evaluate the approach on a representative automotive dataset consisting of requirements, Simulink block diagrams, and C/C++ implementations. Preliminary results demonstrate that incorporating visual model representations significantly improves code correctness, requirement alignment, and structural consistency compared to text-only baselines. These findings highlight the potential of Vision-Language Models to enable more accurate, adaptive, and domain-compliant code generation, paving the way for the integration of VLMs into future model-based software development workflows.

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

Multi-Agent LLM Architecture for Systems Engineering Automation

2026-01-0777

7/1/2026

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, 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 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 Large Language Model (LLM) pipeline that automates key steps of the systems engineering lifecycle - from requirement structuring and compliance checking to code and test generation - using specialized LLM agents orchestrated within a unified architecture. A central contribution of this work is the integration of a Human-in-the-Loop subsystem, which introduces configurable review checkpoints at critical stages such as requirements analysis, compliance assessment, code generation, and test creation. The human-in-the-loop module enables engineers to approve, reject, or modify intermediate results, ensuring human oversight, enhancing trustworthiness, and enabling adherence to functional safety standards. The system supports heterogeneous input formats and provides end-to-end traceability through structured outputs and detailed monitoring of performance metrics including model usage, token consumption, and automation efficiency. Initial evaluations indicate that the combination of multi-agent specialization and human-in-the-loop-guided oversight can significantly reduce engineering effort while maintaining the transparency and reliability required for regulated domains. By embedding controllable human supervision into the LLM-driven pipeline, this work offers a practical and scalable architecture for integrating Artificial Intelligence (AI) automation into safety-critical systems engineering processes, with particular relevance to automotive software development.

Padubrin, Marcel, Kulzer, André Casal, Guerocak, Erol

Validation of an Adaptive Order Finite Element Model for Transmission Loss Simulation of Trimmed Automotive Structures

2026-01-0707

6/20/2026

Vehicle electrification and increasing demands for driving comfort present significant challenges for designing effective noise control treatments (NCTs) in modern vehicles. Lightweight, low-emission designs often compromise acoustic efficiency. A popular and efficient way of compensating for this is through the use of multi-layer ‘trim’ material configurations to noise radiating surfaces 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. To overcome these limitations, this paper presents an innovative adaptive higher-order finite element method to evaluate the sound transmission loss (STL) of automotive, including the effect of poro-elastic and viscoelastic soundproofing materials. To show its capabilities, a digital twin was developed for a STL test setup for a production vehicle firewall with and without NCT. We present simulation results for different firewall configurations, comparing them against experimental data for the panel STL levels and relative improvements due to a NCT modification. The findings demonstrate the method's accuracy, efficiency, and applicability to real-world automotive engineering problems and also shed light on the trade-offs between model idealization and fidelity of the digital twin.

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

Framing Rules of the Road Compliance for Driving Automation Systems from an Engineering Standpoint

DRRC-WP-01-20266/18/2026

Rules of the road were created to enable safe, predictable, and efficient road use by governing both individual vehicle operation and interactions among road users. Driving automation systems must be capable of complying with rules of the road to operate lawfully on public roads. Human drivers often rely on simplified guidance, such as state driver’s handbooks, together with tacit knowledge developed through experience and social norms to generalize behavior across jurisdictions. By contrast, driving automation systems must reasonably and explicitly account for the substantial volume of applicable legal requirements within its operational design domain (ODD). Accordingly, relevant legal requirements must be converted into explicit objective logic that can be utilized by driving automation systems. This paper proposes a method to address how driving behavior-related rules of the road can be consistently applied in engineering practice in a harmonized fashion across industry. Specifically, while rules of the road are expressed in natural language—often with subjective and context-dependent terms—driving automation systems require those rules to be interpreted and translated into unambiguous, testable engineering requirements. To address this, this white paper articulates key challenges and outlines systems-engineering approaches for engineering interpretation of rules of the road and their translation into objective requirements suitable for verification. Validation is also discussed as the process for ensuring that the requirements themselves remain appropriate over time.

Digital Road Rules Consortium

ARP4754B Clarification Notice

AIR4757 (Current)

6/11/2026

The purpose of this AIR is to provide additional information on some areas of ARP4754B/ED-79B that may need additional clarification in order to be put into practice. This document should be used in conjunction with ARP4754B/ED-79B. The contents are recommendations and should not be construed to be regulatory requirements. This document may be revised with additional information as ARP4754B/ED-79B is put into practice.

S-18 Aircraft and Sys Dev and Safety Assessment Committee

A Review of Welding Techniques in Metal Additive Manufacturing: Advancements, Challenges, and Future Directions

05-19-04-0029

6/10/2026

The present review evaluates recent advances in the development of Welding-Based Additive Manufacturing (WBAM) technologies using arc, high-energy density, solid-state, and hybrid welding systems by providing an interdisciplinary assessment of technological aspects, sensing, process optimization, and multi-process strategies. It is concluded that, in spite of considerable progress in process optimization and control, there exist numerous paradoxes associated with relationships among process conditions, structure, and properties, especially those related to heat input effects on material microstructure and performance. An important finding is the fragmentation of predictive modeling approaches, where physics-based and data-driven methods remain inadequately integrated, limiting generalizability and accuracy. Another important conclusion is related to the dominance of the effect of thermal history and multi-physical phenomena on the mechanical performance of the material produced by WBAM technologies. Besides, the complexity and contradiction in defect generation mechanisms, monitoring, and evaluation methodologies restrict the development of process standardization and certification. New directions in intelligent fabrication based on artificial intelligence and digital twins are identified.

Santhana Babu, A.V., John Rajan, A., Mishra, Aishwary, Chakravarthy, P., Jayabalakrishnan, D.

A Comprehensive Review of System Risk in Automotive Crash Safety

15-19-02-0010

6/9/2026

Occupant protection has been at the forefront of risk evaluation regarding vehicle crashworthiness design. However, the vehicle is a member of a larger transportation system with varied stakeholders. This article identifies an opportunity for assessing risk in a crash event through emerging safety science paradigms. Conventional Safety I and Safety II frameworks handle well-defined hazards but falter with uncertainty, variability, and emergent behaviors in real crashes. A comprehensive literature review was performed on peer-reviewed research to situate automotive crash safety risk within the Safety III paradigms. The review addresses two questions: (1) How is “risk” defined across the crash safety literature and adjacent safety science domains? and (2) What limitations arise from these definitions in practice? Findings show a dominant probabilistic framing alongside a minority of system-oriented interpretations. Current crash safety practice lacks a coherent, system-level definition of risk that integrates uncertainty and knowledge strength, leading to fragmented methods and limited alignment with modern safety science. Based on this synthesis, the article proposes guiding principles for Safety III-aligned guidelines and recommendations that integrate consequences, uncertainty, and knowledge strength to improve transparency, traceability, and adaptability in crash safety decision-making.

Rye, Patrick J.

An Open-Source Framework to Automate Virtual Optimization of Internal Combustion Engine Calibration Maps through Shape-Based Strategy

2026-37-0001

6/9/2026

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 map-specific order encodes the shape of each map, ensuring smoothness which directly impact on drivability. Accordingly, the calibration problem is reformulated as the optimization of a compact set of polynomial parameters that uniquely define the full set of calibration maps, rather than individual set-point. Each candidate solution is assessed by generating the corresponding calibration maps and simulating the engine behavior through a neural-network-based digital twin, providing predictions of operating conditions, hardware limits, performance metrics, and emissions. The proposed framework was validated on a passenger-car diesel engine, considering a reduced yet representative set of calibration maps, including main injection start of injection, air mass, boost pressure, and injection rail pressure. The objective of optimization was the minimization of brake mean fuel consumption, subject to an upper bound constraint on nitrogen oxides emissions. The global optimization process explored approximately 106 different calibration candidates within about 36 hours, leveraging parallel computation on a standard laptop. The results indicate that the procedure can deliver multiple near-optimal preliminary calibration solutions, providing an effective starting point for subsequent manual finetuning.

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

Can Large Language Models Enable Sustainable Driving Behavior? A Study on Large Language Model-Based Eco-Driving

13-07-02-0006

6/9/2026

Large language models (LLMs) have shown remarkable capabilities for perceiving driving environments and making interpretable, logical decisions for autonomous driving. However, their potential for more comprehensive driving strategies, especially concerning energy efficiency, remains underexplored. Most existing studies primarily focus on driving safety, which may inadvertently increase energy consumption. To address this issue, this study explores the use of LLMs as high-level controllers to jointly optimize driving safety and energy efficiency. A textual prompt is designed for the LLM, incorporating few-shot examples that describe scenarios, states, and actions. The LLM processes the scenario and state prompts describing the surrounding traffic environment. It generates a high-level control signal, which is then translated into low-level vehicle motion commands in a high-fidelity traffic simulator with realistic physics, vehicle dynamics, road slopes, and network topology. Experiments in campus-scale digital twin car-following scenarios demonstrate that the proposed LLM-based framework achieves an average reduction of 4.16% in energy consumption compared to the reinforcement learning paradigm, while maintaining driving safety and providing interpretable high-level decision-making. This study highlights the potential of LLMs for longitudinal eco-driving applications under the evaluated simulation settings, extending previous LLM-based autonomous driving research that primarily focused on safety to also consider energy efficiency.

Wang, Haoyu, Li, Zhenning, Wang, Siying, Zhou, Zijing, Zhang, Xiang, Yang, Zhifeng, Ou, Shiqi (Shawn), Qi, Hao

Risk-Driven Program Management Framework for Avionics Development: Integration of Safety, Certification, and Cybersecurity Standards

2026-26-0716

6/1/2026

Modern avionics programs contend with escalating complexity driven by concurrent safety certification, cybersecurity compliance, and multi-standard regulatory demands. Traditional program management approaches treat risk management as a parallel support function rather than a central governance mechanism, resulting in reactive responses that fail to prevent cost and schedule erosion. This paper introduces the Risk-Driven Program Management Framework (RD-PMF), an eight-phase governance model that embeds quantitative risk assessment, standards-risk mapping across DO-178C, DO-326A, ARP4754A, and ARP4761A, real-time digital dashboards, and earned value management within core program decision-making. The framework integrates probabilistic schedule analysis using Monte Carlo simulation with continuous risk exposure monitoring to enable proactive, data-driven governance. RD-PMF is demonstrated through a representative avionics program scenario modelled on a flight control system development effort with a 24-month baseline schedule, $15 million budget, and 27 identified risks. Simulation parameters, informed by the authors’ professional experience in avionics program management and published industry benchmarks, illustrate framework applicability within industry-typical ranges. Five targeted risk mitigation strategies, with a combined investment of $1.27 million addressing certification review delays, requirements volatility, supplier delays, hardware-software integration, and cybersecurity threats, reduced aggregate risk exposure by 77 percent (64.7 to 15.1 schedule-weeks). The demonstration yields an 11 percent schedule performance index improvement (SPI: 0.88 to 0.98), a 6.5 percent cost performance index improvement (CPI: 0.92 to 0.98), schedule variance reduction from 8.0 to 1.2 weeks, and a 2.5-month acceleration in projected completion. Return on investment analysis shows 2.22x gross (1.22x net) on mitigation spending, with total quantified benefits of $2.82 million. These results illustrate a measurable shift from reactive program control to proactive, risk-informed governance suited to next-generation aerospace development programs.

Rahul, Saurabh, Benikireddy, Raghunatha

Kubota's SVL110-3 brings new tech and service features to CTL segment

26TOFHP06_13

6/1/2026

Kubota introduced the new SVL110-3 compact track loader at CONEXPO 2026 in Las Vegas. The SVL110-3 delivers 112.7 gross horsepower (84.0 kW), an increased torque output of 279 lb-ft (378 Nm) compared to previous models and a rated operating capacity of 3,700 lb (1,678 kg). The SVL110-3 is capable of 45 GPM (170 L/min) of auxiliary flow while operating with the same traveling speed and compact footprint as its predecessor, the SVL97-3. Kubota states that this increase in auxiliary capacity enables contractors to operate high-demand attachments like trenchers, cold planers and skid cutters at full performance without compromise.

Wolfe, Matt

Hybrid Digital Twin for Aero-Engine Bearing Prognostics: A Physics-Informed Machine Learning Approach to RUL Prediction

2026-26-0718

6/1/2026

Unscheduled maintenance due to the failure of critical components, such as aero-engine rolling element bearings, is a leading cause of costly Aircraft-on-Ground (AOG) events; consequently, current time-based maintenance practices are inefficient and prone to risk. This paper develops a resource-efficient Hybrid Digital Twin (HDT) model for an engine bearing, focusing on the dynamic prediction of spall growth due to Rolling Contact Fatigue (RCF), thereby enabling a condition-based maintenance paradigm. The HDT architecture integrates two core models: (1) a physics-informed model that uses established life and fatigue theory to define initial degradation thresholds, and (2) a data-driven Recurrent Neural Network (RNN), specifically a Long Short-Term Memory (LSTM) network, for dynamic degradation rate modeling. The methodology utilizes a Monte Carlo simulation coupled with RCF progression equations to generate a large, high-fidelity synthetic run-to-failure dataset under varying operational loads, accurately simulating realistic mission profiles. This approach addresses the critical "data scarcity" challenge in aviation. To ensure operational reliability, the framework incorporates Uncertainty Quantification (UQ) using Monte Carlo Dropout and addresses the "Sim-to-Real" gap through Transfer Learning on the NASA IMS bearing dataset. The HDT demonstrates a significant improvement in prognostic accuracy, achieving a Root Mean Square Error (RMSE) reduction of over 71% compared to baseline models. Furthermore, a cost-benefit analysis suggests a potential fleet savings of $240,000 per 100 engines by avoiding false negatives. This computationally efficient approach supports the Digital Engineering Transformation theme by providing a scalable blueprint for the virtual qualification of critical mechanical components.

Mohamed, Abbas

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

2026-26-0712

6/1/2026

The aerospace industry is undergoing a significant digital transformation in the way system requirements are defined, communicated, and managed. Major OEMs are moving towards fully model-based development processes, with plans to deliver requirements exclusively in the form of 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. However, MBSE itself does not ensure that the requirements defined within the model are complete or consistent. Without rigorous validation techniques, even well-structured models can carry forward poorly defined or conflicting requirements — leading to errors that propagate throughout the development lifecycle. This work proposes an approach that integrates formal methods into MBSE workflows by enabling completeness and consistency checks of SysML-based requirements within Cameo Systems Modeler. The method bridges Cameo Systems Modeler with formal analysis tool by transforming modeled requirements in Cameo into analyzable formal specification. The transformed formal requirements allow engineers to identify missing, conflicting, or unreachable requirements early in the development lifecycle, while also aiding automated test generation.

Gupta, Chandan, Nakkeeran, Rupashree

Digital Twin Modeling of Electro-Hydraulic Asymmetric Actuator System with Parameters Identified under High-Speed Conditions

2026-99-0747

5/15/2026

The electro-hydraulic asymmetric actuator system is widely used in high-precision fields such as aerospace, robotics, and exoskeletons. As application scenarios evolve toward higher speeds and greater precision, the nonlinear characteristics and multi-physics coupling behavior of these systems become increasingly prominent. The accuracy of their modeling and identification directly impacts the effectiveness of system dynamic performance evaluation, control strategy design, and predictive optimization. Therefore, this paper combines the system structure and transmission characteristics to carry out digital twin modeling and parameter identification research under high-speed conditions. First, a coupling model based on motor-load characteristics and flow characteristics is established; then, the least squares method is proposed to identify the frequency domain Bode response of the motor-controlled pump system and the time domain nonlinear parameters of the hydraulic transmission system; finally, the two models are organically combined to establish a Simulink-AMESim co-simulation model, and the accuracy of the constructed nonlinear model is verified through simulation and experimental comparison. The experimental results show that the speed response error is within 0.5%, and the position tracking error is within 0.5mm. This method can accurately model the electro-hydraulic asymmetric actuator system under high-speed conditions.

Wang, Hao, Zhang, Xin, Ma, Tianbing, Wang, Jian, Zhang, Tao, Wang, Lei, Shi, Yongping, Wang, Chao

Automotive Engineering: May 2026

26AUTD055/14/2026

Forvia Hella ready with ADB, but NHTSA test stands in the way A demonstration ride shows the glare-free, game-changing power of adaptive driving beams, already available in Europe. An approval test from NHTSA is proving difficult for OEMs to pass. Sharper validation without brute force How CERTUS reshapes AV testing. Simulation-driven battery development From material selection to system-level performance. How simulation unlocks efficient and innovative motor design Engineers are still at the heart of the development process as simulation tools become great levelers. Engineering in the second quarter of the 21st Century Building a trusted digital twin and decision-centric simulation ecosystem. Engineering in the second quarter of the 21st Century Building a trusted digital twin and decision-centric simulation ecosystem. Independent materials testing for OEM validation How validated data provides the foundation for approved components. Editorial All the ways: Learning via print in a digital era The Navigator Uber wants a piece of every robotaxi Bosch Shows off its first U.S. electrolyzer in support of hydrogen research Engineering better reusable bulk containers for the industry The dawn of agentic autonomy in factories Some Automakers Retreat from North American EV Market Enabling certified GoogleTest for safety-critical embedded software Toyota expands all-electric bZ 'family' First Drive: 2026 Subaru Outback Wilderness Product Briefs Spotlight: Testing & simulation, semiconductors Q&A TMMK president: Solar and bright, quieter factory floor help production

An MBSE Methodology for the Pre-Concept Design of a Fast Rotorcraft

F-0082-2026-0296

5/5/2026

Future military operations are expected to take place in highly dynamic, contested and multi-domain environment, where speed, flexibility and survivability are essential. Fast rotorcraft are emerging as critical asset to meet future operational requirements, offering a hybrid solution that bridges the gap between conventional helicopters and fixed-wing aircraft. Given the increasing complexity of both operational requirements and system architectures, a Model-Based System Engineering (MBSE) approach has become fundamental to support concept design. However, MBSE is often neglected in the earliest phases of aircraft concept definition and proposal process, when business and mission requirements are agreed between Contractor and Supplier, due to the fast pacing of Parties interactions with respect to the time and effort required to perform modelling activities. This prevents nurturing the benefits of MBSE in this crucial phase, and it generates omissions in the systems engineering data to perform design validation in future phases. This paper describes a tailored methodology to make MBSE feasible to support project requirements agreement and initial aircraft sizing.

Turco, Luigi, Sabato, Pietro, Wisniewski, Robin, Mazza, Giuliano, Lilliu, Cristian

Application of Model Based Systems Engineering (MBSE) Product Line Engineering (PLE) to Realize Family of Systems (FoS) Objectives

F-0082-2026-0100

5/5/2026

Now that Modular Open Systems Approaches (MOSA) are being incorporated into the development of weapon systems that are acquired by the U.S. Department of War (DoW), attention is turning to transitioning disparate standalone weapon systems into an enterprise portfolio of weapon systems, a Family of Systems (FoS), whereby the effective management of a common constraining, or reference, architecture can aid in realizing the objective of 'develop once, reuse many times.' This is particularly challenging when enduring fleet legacy weapon systems are involved in addition to new development systems. Model Based Systems Engineering (MBSE) methodologies and techniques have now become the norm in system developments. It is, therefore, imperative to effectively employ MBSE techniques in establishing a FoS. This paper proposes an MBSE-based Product Line Engineering (PLE) method for implementing FoS architectures that enables controlled architectural variation while preserving enterprise reuse and architectural consistency.

Zook, Keith, DuBois, Tom

Simulation-Based Route Analysis and Digital Twin Integration for Electric Aircraft and eVTOL Design & Operations

F-0082-2026-0065

5/5/2026

The rapid expansion of electric aviation and eVTOL operations introduces tightly coupled challenges related to energy‑constrained aircraft design, battery and thermal management, mission planning, and the generation of certification‑relevant evidence. This paper presents an integrated simulation workflow developed by AVL, Unisphere, and blueflite that combines high‑fidelity electric powertrain and battery models with a guidance‑level, digital‑twin‑based 4‑D trajectory simulation driven by historical weather and operational constraints. At each mission time step, the trajectory layer provides time‑resolved environmental and routing conditions, while the system‑level models compute instantaneous power demand, state‑of‑charge evolution, and thermal response, enabling mission feasibility assessment under realistic wind, temperature, and airspace effects. The workflow is calibrated and validated using flight telemetry from blueflite's active eVTOL cargo aircraft development, ensuring alignment between simulation assumptions and real‑world mission execution. The validated framework is subsequently applied to seasonal route studies and large‑scale virtual flight campaigns spanning multiple regions and years, enabling statistically robust assessment of energy margins, thermal behavior, and mission‑duration variability. The results demonstrate how integrated, traceable simulation can bridge conceptual design and real‑world electric flight operations, supporting informed decision‑making by OEMs and operators in aircraft design, validation, and deployment planning.

Schneider, Jürgen, McClearen, James, Anger, Michael

Improvement and Demonstration of a Machine Learning-Based Digital Twin Framework for Rotorcraft Life Assessment Using In-Service Operational Data

F-0082-2026-0060

5/5/2026

Traditional safe-life methodologies for rotorcraft structural components rely on deterministic safety factors to account for uncertainty in loads, material properties, and operational usage. While effective for ensuring safety, these approaches lead to early retirement lives and reduced aircraft availability. This paper presents an updated digital twin-based probabilistic framework for rotorcraft component fatigue life assessment that integrates a probabilistic stress–life (S-N) material model, machine learning-based load estimation from flight data, and Monte Carlo uncertainty propagation. The approach is demonstrated for a critical location on the CH-146 Griffon main rotor yoke. Compared with earlier work, the present study advances the framework through independent validation of the load-estimation model and application to available in-service flight data from multiple mission categories. A probabilistic sensitivity analysis is used to examine the separate and combined effects of material variability and load-estimation uncertainty on fatigue life, cumulative probability of failure, and hazard rate. For the CH-146 demonstration case, the results indicate that the material fatigue strength uncertainty has a major impact on the lower tail of the life distribution and the corresponding reliability-based life, whereas load-estimation accuracy uncertainty has a secondary influence on risk metrics. The application of the digital twin framework to operational, search and rescue, and training mission data further shows that mission-specific usage variability plays an important role in the evolution of fatigue damage accumulation and structural risk. Overall, the proposed framework provides a more informative basis for risk-based rotorcraft life assessment by explicitly quantifying uncertainty and incorporating aircraft-specific operational data. The study is intended as a step toward validation of the framework rather than a completed operational deployment.

Asaee, Zohreh, Bombardier, Yan, Renaud, Guillaume

Multiphysics Modeling and Performance Assessment of Ultrasonic Welding for Thermoplastic Composites

F-0082-2026-0120

5/5/2026

Ultrasonic welding (UW) provides a rapid and efficient method for joining composite components by inducing resin flow through thermally driven diffusion and crystallization at the bonded interface. However, in the absence of a multiphysics modeling framework or a digital twin approach, current practice still depends on extensive trial-and-error testing to determine key welding parameters such as vibration amplitude, weld time, weld pressure, hold time, and downspeed. While in-situ thermal cameras can monitor surface temperatures, the internal temperature at the bonded interface is often significantly higher, introducing the risk of thermal degradation and inconsistent bond quality. To overcome these limitations, GEM developed a high-fidelity multiphysics model to establish a quantitative relationship between process parameters and the evolving temperature field within welded thermoplastic parts. The model integrates coupled mechanical, thermal, and acoustic physics to simulate high-frequency vibrations and static pressure, capture the generation and spatial distribution of heat, and represent the temperature-dependent viscoelastic response that governs bond formation. A validation test matrix was designed by systematically varying weld time and vibration amplitude. Through-thickness temperature distributions were measured using infrared thermal imaging, enabling direct comparison with model predictions. Upon validation, the model was applied for process tailoring, allowing precise control of temperature distribution to achieve target bond strength. This integrated modeling and validation approach demonstrated substantial benefits, including reduced design iterations, accelerated process optimization, and improved quality and performance of welded composite structures.

Walthers, Mark, Li, Rui, Wei, Qingxuan, Lua, Jim

Strategic Planning for Modernizing the Black Hawk Using Model-Based Systems Engineering (Presentation Only)

F-0082-2026-0324

5/5/2026

This presentation outlines the U.S. Army’s H-60M Black Hawk modernization approach to address evolving operational needs and the demand for agile acquisition. To accelerate capability delivery, the Utility Helicopters Project Office leveraged a Modular Open Systems Approach and Model-Based Systems Engineering to establish a Modernized H-60M System Model aligned with Capability Program Executive Aviation’s Enterprise Architecture Framework. This authoritative model captures comprehensive mission-driven requirements to enable phased, incremental technology insertions instead of a monolithic, multi-year development cycle. A Capability Assessment Model (CAM) Framework will extract key capabilities and architecture drivers from the model, translating rigorous digital engineering into agile industry solicitations. Using Model-Based Requests for Information, the CAM Framework will allow the Army to systematically evaluate solutions, execute data-driven trade-offs, and rapidly field “minimum viable capabilities.” Ultimately, this strategy ensures the Black Hawk remains an adaptable, mission-critical asset, delivering continuous capability improvements to the warfighter at the speed of relevance.

Grant, Travis, Winters, Kyle, Carter, Casey

The Applicability of Model-Based Systems Engineering Tools in the Process of Modernizing Legacy Systems (Presentation Only)

F-0082-2026-0342

5/5/2026

The UH-60 Black Hawk — manufactured by Sikorsky Aircraft Corporation — is a twin turbine engine, single rotor, semi-monocoque fuselage rotary wing helicopter used primarily for Utility (tactical transport of troops, supplies, and equipment) purposes. In August of 2024, an experimental effort known as Transformation in Contact was called for, where systems would be more simple, intuitive, low signature, and iterative. This effort, along with the implementation of MBSE, has become a critical component for evaluating and refining technologies that could be needed without delay. This paper will serve to provide the collective results of the digital thread being developed for the Black Hawk as well as explore the efforts and processes utilized for this design. In particular, how the application of a Modular Open Systems Approach (MOSA), integration of a digital backbone, and utilization of the Capability Program Executive (CPE) Aviation Enterprise Architecture Framework (EAF) has enabled a cohesive standard for the rapid technology insertions while reducing cost, increasing efficiency, and improving the overall maintenance and sustainment for the aircraft.

Peters, Kayla, Dainard, Tony, Hayes, Jason, Joyce, Monica, Wileman, Brian, Dixon, Wesley

Computer-Aided VTOL Design: from Slide Rulers to Artificial Intelligence

F-0082-2026-0198

5/5/2026

Vertical Take-Off and Landing (VTOL) aircraft represent one of aviation's most complex design challenges, balancing lift, thrust, stability, and control within an inherently unsteady aerodynamic environment. Since the 1940s, computational methods used to design VTOL systems have undergone a profound transformation, progressing from hand-drawn airflow approximations and wind-tunnel testing to today's high-fidelity digital twins, computational fluid dynamics (CFD), and AI-assisted optimization. The evolution of these methods mirrors the broader technological shift from empirical design toward simulation-driven innovation. The greatest transformation in VTOL design of the past 80 years is the shift from material and mechanical innovation to computational and cognitive design. Modern aircraft are as much products of computation and data as of metal and composites. As electric propulsion, autonomy, and digital twin technology converge, the next generation of designs, particularly configurations inspired by power systems such as hybrid-electric, hydrogen, battery only, will extend this century-long trajectory into a new paradigm: sustainable, intelligent, and continuously self-optimizing VTOL flight.

Stanzione, Kaydon

A Practical Approach for Integral Process of Aircraft Systems Validation and Verification in Compliance with ARP4754

F-0082-2026-0165

5/5/2026

Given the necessity of performing System Certification according to SAE ARP4754, accepted as guideline by aeronautics certification authorities for development of aircrafts and complex systems, the need to define a robust and adaptable system requirements Validation and Verification (V&V) process has become a priority. SAE ARP4754 compliant processes shall be applied for certification of new complex systems, as well as to existing ones. Defining suitable and compliant processes for projects that were already in an advanced development stage when compliance to ARP4754 became mandatory is even more challenging with respect to the application to new projects, as the need of rearranging existing certification documentation naturally arises. This paper illustrates a process compliant with ARP4754 guidelines to achieve the System level requirement V&V. The presented process – based on the Function-Based Systems Engineering (FuSE) – has been applied to the civil certification of the Fly-By-Wire Flight Control System (FCS) of the AW609 tiltrotor by Leonardo Helicopters and has been reviewed by the Federal Aviation Administration (FAA).

Cardili, Nicole, Parolini, Maurizio, Zanetti, Valerio, Simonetti, Filippo

Digital Twin-based Integrated Airspace Management for Urban Air Mobility Services: Architecture, Intent Modeling, and Deployment

F-0082-2026-0215

5/5/2026

Urban Air Mobility (UAM) represents a paradigm shift in metropolitan transportation, introducing electric vertical takeoff and landing (eVTOL) aircraft into dense urban ecosystems. This transformation is driven by advances in electrification, digital infrastructure, and integrated airspace management. According to the U.S. Department of Transportation's Advanced Air Mobility National Strategy 2025, UAM is expected to become a cornerstone of multimodal urban transport, with commercial operations projected in multiple U.S. cities before 2030 [1].

Namuduri, Kamesh, Sampath, Arunkumar

Engineering in the second quarter of the 21st Century

26AUTD05_05

5/1/2026

Building a trusted digital twin and decision-centric simulation ecosystem The automotive industry has been experiencing significant change and transformation. Electrification, software-defined vehicles, advanced driver assistance systems, and increasing electrical system integration are fundamentally reshaping how vehicles are designed and validated. As integration complexity continues to increase, the expectations for design cycle times are being compressed. Programs that once relied on extended validation timelines are now expected to deliver the same level of confidence in a fraction of the time. Traditional engineering workflows were built around sequential design phases, iterative simulations, and heavy reliance on physical validation. Design concepts were documented, prototypes were constructed, tests were performed, and results were compiled in reports and specifications that informed the next iteration. That approach worked well when systems were less complex and product life cycles were longer. In recent years, the volume of data, the speed of development, and the interconnected nature of modern vehicle architectures demand a different approach.

Patterson, Jeremy

The dawn of agentic autonomy in factories

26AUTD05_11

5/1/2026

The global automotive industry has reached a new era. If 2025 was defined by the cautious exploration of “experimental pilots” and the collection of vast data lakes from connected vehicle fleets, 2026 marks the year that data finally gains a mind of its own within the assembly plant. We are witnessing a transition from passive automation to integrated, agentic autonomy. This is a shift that moves beyond simple programmed robotic arms and toward systems capable of independent reasoning and real-time optimization. This evolution is not just a technical upgrade; it is a fundamental restructuring of how vehicles are built, de-risked, and scaled in an increasingly volatile global economy.

Panigrahi, Dijam

Optimizing Virtual Scenario Testing for Autonomous Vehicles with Large-Scale Scenario Generation

2026-01-0050

4/7/2026

The validation of Advanced Driver Assistance Systems (ADAS) and Automated Driving (AD) Systems, especially at higher automation levels such as SAE Level 3 or 4, demands the testing of a vast array of scenario variants far exceeding the scope of standard safety specifications like Euro NCAP (The European New Car Assessment Programme). Autonomous vehicles require thorough real-world testing to ensure automotive safety. However, public road tests are costly and risky. Instead, virtual scenarios - digital twins of real environments - offer a safe, cost-effective testing alternative. Exhaustive simulation across this high-dimensional scenario space, which includes variations in actor behavior, environmental conditions, and event characteristics, is computationally infeasible. We propose a constraint-solving approach to address this challenge that leverages mathematical and geometric techniques to analytically assess the existence and validity of scenario variants prior to simulation. Two primary methods are explored: (1) random or sequential generation of scenario variants with a pre-simulation pruning step to eliminate invalid cases, and (2) direct generation of valid variants by solving constraint systems that ensure the desired events occur under specified conditions. Importantly, maintaining an effective balance between these two approaches is central to our methodology, as the optimal mix depends on the specific testing goals and requirements. This framework implemented using MATLAB®, Simulink®, the Automated Driving ToolboxTM, and the Euro NCAP Support Package®, systematically reduces the scenario space by excluding impossible cases. Our approach aims to significantly reduce reliance on extensive simulation and enable more targeted and efficient validation for safety compliance.

Karve, Omkar, Saurav, Saket, Purwar, Prabhanshu

In-Use Life Digital Twin (Redundant Storage) and Swappable Use Case for Battery Durability

2026-01-0397

4/7/2026

As regulatory frameworks for zero-emission vehicles (ZEVs) and battery electric vehicles (BEVs) continue to evolve, there is growing emphasis on monitoring battery durability and usage throughout the vehicle lifecycle. These regulations increasingly specify the use of data monitors and tracking mechanisms to assess battery health and performance. In addition, regulations require anti tampering mechanisms especially for monitors that have external write access. Historically, regulations focused primarily on vehicle warranty; however, with the introduction of battery durability monitors, clarity is needed for the new battery durability monitors. More specifically if the battery durability monitors track with the lifetime of the vehicle or if they follow the lifetime of the battery. Furthermore, current regulations provide no guidance on high-voltage (HV) traction battery service strategies or methods to protect monitors from tampering by external customers. This paper will classify battery durability tracking parameters (DIDs) according to whether they align to the lifetime of the vehicle or the battery itself. Building on this classification, a service strategy is proposed that considers typical vehicle architectures: when the battery management Electrical Computer Unit (ECU) is fully integrated with or separated from the high voltage traction (HV) battery. The outlined service strategy not only supports regulatory compliance, but also enhances data integrity by mitigating the risk of tampering with monitored parameters through a Digital Twin framework. More specifically, the Digital Twin framework introduces redundant storage of critical information in multiple storage locations such as ECUs and then a mechanism for correlating that critical information to determine a mismatch. This approach anticipates future requirements for tamper-proofing and ensures secure, reliable tracking of battery durability metrics through redundant ECU storage.

Laskowsky, Patricia, Bunnell, Justin, Zettel, Andrew, Albarran, Josue

Vehicle Test Platform with Experimentally Correlated Tire Model for Torque Vectoring Control

2026-01-0622

4/7/2026

This paper presents a testing platform for the development of lateral stability control systems in independent motor electric vehicles (EVs). A 10 degree of freedom (DOF) vehicle simulation and a radio control test vehicle are constructed to enable controls validation scalable to full size vehicles. These vehicle simulations, or ‘digital twins’, have been widely adopted throughout the automotive industry due to their lower operating costs and ease of implementation. Virtual models are not perfect representations of reality, however, and physical testing is still necessary to validate systems for use in the real world. This is especially true when testing safety-critical features such as stability control. As a result, a simulation environment working in conjunction with a test vehicle represents an optimal hybrid approach. In this work, a high fidelity vehicle model is constructed in the Matlab/Simulink environment. To capture the effect of suspension, the digital twin is capable of modeling all angular and linear degrees of freedom of the vehicle body. The vehicle model must also estimate wheel forces during high-sideslip maneuvers. The Pacejka Magic Formula is used for its accurate representation of tire behavior in highly transient driving scenarios. This vehicle model describes the behavior of a physical vehicle. For this purpose, a 1/5 scale radio controlled vehicle with independent rear wheel propulsion is designed and assembled. All physical parameters of the test vehicle required by the vehicle model are estimated through direct measurement or estimation through test maneuvers. Magic formula coefficients are estimated from GPS, inertial, and odometry measurements collected throughout defined test maneuvers. Vehicle model behavior is then benchmarked against the test vehicle. An S-curve maneuver is performed in simulation and experimentation to ensure accuracy and consistency across transient and steady state behavior. In future work, focus will turn to creating an ADAS control system which re-stabilizes a vehicle after a collision using torque vectoring.

Petersen, Nicholas Conner, Robinette, Darrell

MBSE-Driven XIL Workflow for Energy Evaluation of Automated Vehicles: From Simulation to VIL Testing

2026-01-0289

4/7/2026

With the increasing market penetration of automated vehicles, there is a critical need for credible and repeatable methods to quantify their energy impacts. This paper presents a Model-Based Systems Engineering (MBSE)-driven Anything-in-the-Loop (XIL) methodology for quantifying the powertrain energy consumption and potential savings from various controls for automated vehicles in realistic road scenarios while preserving high-fidelity powertrain behavior. The novelty of this approach lies in its use of a unified MBSE backbone (AMBER: Argonne National Laboratory’s [Argonne’s] MBSE-centric platform for transportation energy analysis) to automate the seamless and traceable progression from pure simulation to Vehicle-in-the-Loop (VIL) testing. This work utilizes Argonne's multi-vehicle simulation tool, RoadRunner, which automatically constructs closed-loop road scenarios (road geometry, vehicle sensors, other vehicles, and traffic controls) and connects them to Argonne’s validated, high-fidelity vehicle and powertrain models in Autonomie. The MBSE backbone in AMBER organizes requirements, interfaces, plant and controller models, and test scenarios into a single set of models that is maintained across pure simulation, Software-in-the-Loop (SIL), Processor-in-the-Loop (PIL), and VIL stages. Each stage has a clear role: simulation enables rapid development and validation of advanced models or controls across a large number of scenarios; SIL supports standalone algorithm verification and scenario down-selection; PIL validates real-time execution, inputs/outputs, and timing on the target processor; and VIL provides closed-loop evaluation with a real vehicle under controlled laboratory conditions. AMBER’s automated build and configuration enable rapid retargeting across platforms and repeatable scenario reproduction, making validation fast and cost-effective. To demonstrate its practical application, the workflow is used to validate the functionality and quantify the energy savings of an eco-driving control against a calibrated human driver model. Experiments show strong repeatability and consistent energy gains for the eco-driving strategy while preserving trip time, yielding average energy savings of 7.8% across the evaluated scenarios. Overall, the MBSE-guided XIL workflow shortens development time and reduces test cost by limiting on-road testing and lowering integration risk before track evaluation, while producing credible, closed-loop energy assessments traceable from requirements to test evidence.

Jeong, Jongryeol, Sharer, Phillip, Di Russo, Miriam, Das, Debashis, Zhang, Yaozhong, Karbowski, Dominik

Digital Twin Aerodynamics Model Validation for HALO Wind Tunnel

2026-01-0610

4/7/2026

A simulation-based aerodynamics model of the Honda Automotive Laboratories of Ohio (HALO) Wind Tunnel, a three-quarter open-jet (ground plane) configuration opened in 2022 for full-scale automotive testing, was initiated to support data fusion for more accurate surrogate models in vehicle engineering programs. The objective was to demonstrate that a matched set of boundary values between the physical wind tunnel and the three-dimensional numerical model yield correct responses for several key flow field quantities, starting with the baseline empty tunnel case: (1) streamwise static pressure distribution, (2) evolution of the free shear layers downstream of the nozzle exit plane, and (3) ground-plane boundary layer development. Pressure-based measurement probes were deployed in these regions using a four-axis overhead traverse to acquire validation data in the large facility, including instrument verification between a 14-hole probe and Pitot-static rake. Detached eddy simulation (DES) and Reynolds-Averaged Navier Stokes (RANS) turbulence models were evaluated for the numerical approach. This work describes the three-dimensional model setup and presents these data comparisons.

Patel, Sajan, Disotell, Kevin, Eagles, Naethan

An Integrated Methodology for Sustainable Mobility Infrastructure Planning: The Atlanta Aerotropolis Case Study

2026-01-0097

4/7/2026

By the early 2020s, more than 4.5 billion people have been living in urban areas worldwide, compared to just 1 billion in 1960. Rising growth in urban populations present challenges to infrastructure and transportation systems. Higher traffic levels and reliance on conventional vehicles have contributed to heightened greenhouse gas (GHG) emissions, rising global temperatures, and irreversible environmental degradation. In response, emerging transportation solutions—including intelligent ridesharing, autonomous vehicles, zero-tailpipe-emission transport, and urban air mobility—offer opportunities for safer and more sustainable transportation ecosystems. However, their widespread adoption depends not only on technological performance and efficiency, but also on integration with current infrastructure, safety, resilience to unexpected disruptions, and economic viability. A dynamic agent-based System-of-Systems (SoS) transportation model is developed to simulate vehicle traffic and human movement for assessing mobility solutions against different demand scenarios and possible disruptions within a well-defined metropolitan area. The analysis adopts the concept of an airport city—a cluster of residential, commercial, and industrial spaces surrounding major airports—as a representative urban context. Using the Atlanta Aerotropolis as a case study, this work introduces an interactive, parametric decision-support methodology for evaluating the impact and benefits of future mobility options, as part of transportation master planning. Given the multi-objective and multi-stakeholder nature of transportation planning (e.g. local government, urban planners, engineers, and technology providers), the proposed approach leverages simulation-enabled digital twins of mobility solution alternatives to analyze traffic performance across multiple criteria, including energy consumption, emissions, affordability, accessibility, and connectivity within the broader urban infrastructure. The study reveals cost-benefit trade-offs among mobility solutions in the context of disruptive scenarios, such as the 2026 FIFA World Cup hosted by Atlanta, GA. The results highlight the importance of deploying a mix of mobility options over the city’s transportation network to maximize sustainability while maintaining resilient operations.

Rana, Vishva, Balchanos, Michael, Mavris, Dimitri, Valenzuela Del Rio, Jose

AI Model Creation and System Verification of “Telemotion” Infrastructure-Cooperative Autonomous Driving System Using Digital Twin

2026-01-0255

4/7/2026

The concept of the vehicle has changed as a result of many innovations over the last decade in the fields of connected, autonomous/automated, shared, and electric (CASE) technologies. At the same time, labor shortages in Japan are becoming more serious due to a decline in the working population. To help resolve these issues, a remote-controlled autonomous vehicle driving system called Telemotion has been developed that automates the movement of vehicles in production plants. This system is an autonomous driving and transportation system in which the recognition, judgment, and operation functions of driving are handled by a control system outside the vehicle that communicates wirelessly with the vehicle. This system utilizes artificial intelligence (AI) and other advanced technologies to realize safe unmanned autonomous driving, and is already in operation in production plants. Currently, efforts are under way to build a digital twin environment and conduct AI learning using computer graphics (CG) to configure the system and improve the accuracy of the AI models with the aim of expanding its use to other factories. Within this digital twin environment, it is possible to examine previous tasks by reproducing the vehicles, processes, cameras, and vehicle movements present at a production site. Utilizing this digital twin enabled a significant reduction in the labor required to implement the system.

Hatano, Yasuyoshi, Iwazaki, Noritsugu, Nagafuchi, Yuhei, Iwahori, Kento, Tanaka, Atsushi, Uezu, Satoru, Kanou, Takeshi, Inoue, Go, Okamoto, Yuki, Oka, Yuhei, Kakuma, Daisuke, Chiba, Hiroya, Egashira, Kazuki, Ishikuro, Megumi, Sawano, Takuro

Designing Military Vehicles for a Battlefield Characterized by Drones

2026-01-0262

4/7/2026

The modern battlefield is increasingly characterized by the use of small drones. As such, military vehicles must now be designed to account for this threat. This paper presents a model-based systems engineering approach to identify vehicle vulnerabilities and generate new vehicle requirements to mitigate them. This approach uses a standard set of System Modeling Language diagrams. A vehicle’s primary roles are captured in a series of use cases. Each use case is characterized by a sequence of activities performed by the vehicle. These activity sequences are captured in an activity diagram, which are used to wargame how a drone can exploit the vehicle at each phase. Each potential exploitation is assigned likelihood and severity scores, which feed into a risk index. This risk index is then used to prioritize each vulnerability. From these vulnerabilities, a set of operational requirements are derived, which then informs the development of system requirements. As the system matures, the physical system architecture can also be used to identify drone vulnerabilities. In particular, the small payloads carried by drones are most effective when targeting interfaces. Internal block diagrams and domain diagrams are used to evaluate each interface to determine its vulnerability to drone attack, which can then be incorporated into the design requirements. This paper applies the methodology to an autonomous pontoon bridging system intended to move military vehicles across a wet-gap. A number of key vulnerabilities are identified, leading to a series of new design requirements.

Ells, Alec, Werntz, Bryson, Saulsberry, Taylor, Wilkinson, Cooper, Mittal, Vikram

A System of System Design Framework for Multi-Domain Vehicles

2026-01-0263

4/7/2026

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 subsystem performance within a framework of overall system operation, which includes possible tradeoffs. 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 and water. A host ground vehicle, an unmanned aerial drone, an unmanned marine drone and an unmanned tracked vehicle constitute the family of multi-domain vehicles which will be used for the search and rescue mission. A digital twin for this family of vehicles will be created to support numerical design studies. The System of Systems approach will enable tradeoffs in vehicle and family design to be evaluated using optimization tools. To visualize the designs, tradespace analysis tools will be key to identifying the tradeoffs and performance at the system level and the individual vehicle level. A case study is undertaken to simulate the trajectory of an aerial drone for a search and rescue operation and calculate its - ilities for such a scenario. In the future, the same exercise will be performed for three other models highlighted above and incorporate their -ilities to incorporate into the subsequent steps of optimization and tradespace analysis. This paper showcases the System of Systems approach and highlights the advantages and challenges faced in implementing such an approach for the purposes of achieving a specific mission through the collaborative and diverse vehicles used.

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

Enhancing Track Vehicle Design Engineering Through Digital Twins: A Data-Driven Approach

2026-01-0266

4/7/2026

Digital Twin technology can significantly improve the engineering product design process, especially when considering ground vehicle applications. Data-driven computer studies can assist engineers and key stakeholders in evaluating performance, durability, and other system design tradeoffs. To enable this process, the availability of relevant, numerically generated, laboratory, and/or field data is required. Proper data use enables the digital exploration of “what-if” scenarios, reducing necessary field testing and allowing for the examination of hard-to-test operating conditions. When considering the Digital Twin toolset, a collection of models and simulations are assembled to supplement virtual testing endeavors. These models include surrogate, CAD/CAE, and others. In this paper, an off-road track vehicle design is reviewed through the fusion of numerical and field data to evaluate future design enhancements. Preliminary results demonstrate that subtle feature upgrades can produce measurable performance gains without compromising listed requirements and specifications. The proposed design framework establishes a methodology for virtual engineering practitioners. In addition, a simulation is able to generate design and solution space visualizations for the assessment of design tradeoffs, optimizing three Key Performance Indices (KPIs) or Key Design Specification (KDS) objectives.

Suber II, Darryl, Bradley, Andrew, Singh, Shubhendra, Turner, Cameron, Castanier, Matthew P., Wagner, John

Standardization of On-Vehicle Measurement of BEV Thermal System Energy Consumption and Range Impacts

2026-01-0132

4/7/2026

This paper presents research and digital twin modeling results to support work on a methodology to properly account for the energy consumed by the thermal system of a BEV, for use within both existing Petroleum-Equivalent Fuel Economy (PEFE) calculations, and the proposed addition of hot and cold weather range values to the consumer-facing Monroney label [1]. Properly accounting for thermal system impacts would incentivize minimizing energy consumption of these systems, since 1) BEV PEFE is a direct input to an OEMs overall CAFE performance, and 2) the values on the Monroney label has some impact on consumer vehicle choice. The impetus for this work was Final Rules issued by the EPA and NHTSA in early 2024 eliminating A/C Efficiency Credits for BEVs from the 2027 MY, thus eliminating regulatory incentives to minimize energy consumption of these systems. Higher energy consumption will produce a number of negative secondary effects, including higher real-world greenhouse gas emissions, reduced vehicle range, greater strain on the nation’s electrical grid, and higher vehicle mass leading to reduced vehicle safety - should OEMs opt to merely install larger batteries to address cold and hot weather range impacts instead of implementing lower energy-consuming technology. The results from the analysis, which ideally would be confirmed with follow-up vehicle tests, show that for a baseline, PTC-heat based system, thermal system energy consumption represents 19.2% of the total energy consumed by a BEV on an annual basis, using an ambient-VMT weighted approach. It seems to be the technical equivalent of “straining at a gnat while swallowing a camel” to focus so much time and energy on identifying incremental improvements in energy consumption from the propulsion-portion of a BEV, while by comparison ignoring the system that according to this analysis can account for nearly 20% of the total on an annual basis.

Taylor, Dwayne

Structuring Cross-Disciplinary Development: A Systems Engineering Framework for ASPICE-Compliant Mechatronic and Cyber-Physical Engineering

2026-01-5025

4/2/2026

Mechatronic and cyber-physical systems emerge from interdisciplinary design efforts, integrating software (SW), electronics, and mechanical components. Developing such systems places high demands on organizations and processes, particularly regarding efficient collaboration across domains. A key challenge lies in establishing organizational structures and workflows which allow cross-discipline work and at the same time ensure compliance with regulations and adherence to standards such as Automotive Software Process Improvement and Capability Determination (ASPICE). In response, the authors have developed an Engineering Process Framework (EPF) grounded in International Council on Systems Engineering (INCOSE) systems engineering principles. The EPF provides a structured approach for system development and therefore defines company-wide processes and methods. This paper presents the development of the EPF’s functional logic and its implementation within a tool landscape. Furthermore, a selected process is used to illustrate how the EPF supports development engineers in their daily work.

Gehrt, Jan-Jöran, Granrath, Christian, Caglayan, Ebru, Reckeweg, Thomas, Richert, Felix

SAE TOMORROW TODAY - SAE J3311: Smarter Vehicle Power Management

135603/25/2026

When it comes to today's vehicles, every bit of energy matters. Wasted power can reduce EV driving range, add weight, and increase costs across the supply chain. That's where SAE Standard J3311, comes in. Instead of constantly running systems at full power -- or each automaker using its own proprietary strategy -- SAE J3311 promotes efficient, fine-tuned energy use across the entire vehicle. Listen in as we sit down with SAE J3311 committee members Donald Gignac, Automotive Solutions Architect, Silicon Mobility; Maria Soledad Elli, Sr. Data Scientist, Torc Robotics; and Simone Palombi, Senior Systems Engineer, General Motors, to discuss how creating a common, industry-wide approach to smarter power management can unlock longer range, lighter vehicles, lower costs, and faster innovation across EVs and internal combustion engines alike. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.

Patterson, Lori

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