Browse Topic: Systems engineering

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Integrating MBSE with High-Fidelity Crash Simulation2026-01-75429/22/2026
Modern electrified ground vehicles introduce complex, multi-domain safety requirements, such as post-crash thermal runaway prevention, that expose the traceability limitations of Document-Based Systems Engineering (DBSE). This paper proposes a four-layer, bidirectional digital thread architecture that integrates Model-Based Systems Engineering (MBSE) with high-fidelity, non-linear Computer-Aided Engineering (CAE) crash simulations. Leveraging SysML, System-Theoretic Process Analysis (STPA), and Python-based orchestration middleware, the framework automates the translation of descriptive safety requirements into explicit finite element boundary conditions. The architecture programmatically extracts key performance indicators from massive binary solver outputs and injects them back into the SysML environment for automated compliance verification. Demonstrated through a simplified electric vehicle side-pole impact case study utilizing LS-DYNA and a 1D thermal model, the framework successfully eliminates manual data handoffs, accelerates multidisciplinary design optimization, and ensures robust, risk-driven requirement traceability across the engineering lifecycle.
Rye, Patrick J.
An Agentic AI Vision for Combat Vehicle Architecture Model2026-01-75279/22/2026
This position paper presents
Dattathreya, Macam
Human Digital Twin Architecture for Ground Vehicle Scenario Integration2026-01-75059/22/2026
Ground vehicle commanders operate in scenarios which bare high cognitive load. They must be reactive to time-critical events where attention is divided between a variety of sensors, crew members, the physical world, and digital displays, which can result in missed situational cues. This paper presents a Human Digital Twin (HDT) architecture which provides real-time, embodied AI assistance to commanders in a military ground vehicle simulation scenario. The system integrates a data pipeline for combining a MetaHuman avatar in Unreal Engine with multi-modal data ingestion and a large language model (LLM). In addition, a retrieval-augmented generation approach grounds the LLM with mission-specific context, and a Big Five personality framework for prompt design constructs a consistent agent persona throughout the scenario. The architecture is demonstrated with a prisoner of war camp scouting mission, in which the HDT selectively intervenes when needed to alert the commander to critical events when missed. A system latency evaluation is provided to demonstrate viability for real-time integration. Results show the potential of integrated HDT systems to improve situational awareness and decision support in high stakes ground vehicle operations.
McCarthy, Martin, Mohammed, Abdul Mannan, Gallagher, Reese, Neumann, Carsten, Bruder, Gerd, Reiners, Dirk, Cruz-Neira, Carolina, Paul, Victor
Decision Engineering: Applying Lean Production Principles to the Lifecycle Decision Factory2026-01-75259/22/2026
The Army’s transformation mandate is unambiguous: deliver warfighting capability 25–30% faster. Every Tier 2 metric published in support of that mandate – days between milestones, days to complete the requirements process, days to complete contracting, days to complete testing – is really a decision throughput measurement. Yet the systems engineering (SE) discipline that governs those timelines has no formal production framework for producing decisions. This paper proposes Decision Engineering as the framework. Grounded in lean production theory, applied to information work and anchored to the defense acquisition policy structure of DoDD 5000.01 and DoDI 5000.02, Decision Engineering reconceives SE as the discipline of designing, operating, and continuously improving the lifecycle decision production system. It introduces a formal decision ontology comprising six decision states (Latent, Declared, Active, Deferred, Closed, Reopened), four topology relationship types (Precedes, Enables, Constrains, Triggers), and five diagnostic biomarkers that measure production system health. The paper presents the Decision Factory model and describes how its application to ground vehicle system programs and across the Army acquisition enterprise can accelerate capability delivery without sacrificing rigor. Defense acquisition programs do not fail for lack of engineering rigor or digital tooling. They fail because the production system that converts information into decisions is ungoverned – no inventory count, no flow discipline, no throughput measurement. Decision Engineering names this production system, provides instruments to measure its health, and offers a practitioner framework for running it better.
Alexander, Eric, Foglesong, Matthew, Berklich, Louis (Bill)
Physics-Constrained Procedural Generation of Synthetic Environments for the Robust Validation of Autonomous Maneuver and Logistics2026-01-75499/22/2026
The validation of Autonomous Ground Vehicles (AGVs) and intelligent logistics planners is frequently compromised by the ”Sim-to-Real” gap, where simulation environments fail to replicate the physical friction of operational deployment. Ideally, valid test cases must enforce strict mobility constraints and impose realistic sustainment penalties; however, many current generation tools rely on idealized terrain interactions and infinite-resource assumptions. We present a real-time procedural framework designed to generate high-friction validation environments that stress-test the robustness of the System Under Test (SUT). The architecture integrates gradient-based terrain analysis with a stochastic contested logistics model. It ingests synthetic heightmaps to precompute mobility corridors, ensuring that every generated evaluation episode adheres to vehicle-specific traversability limits. Simultaneously, a logistics kernel enforces fuel consumption scaled by terrain gradients and models supply chain interdiction as a parameterized Bernoulli process. We validate this framework through a ”Digital Twin” methodology, demonstrating that terrain-aware generation eliminates invalid initialization states (0% mobility violations) while the logistics model induces operationally relevant failure modes in the SUT. This unclassified, open-architecture approach supports DoD Verification, Validation, and Accreditation (VV&A) requirements by providing deterministic, reproducible edge cases for autonomous system evaluation.
Soykan, Bulent, Rabadi, Ghaith, Bochenek, Grace, Paul, Victor J.
Multimodal Digital-Twin Simulations for Off-Road Autonomous Vehicles2026-01-75509/22/2026
Research in autonomous driving has largely focused on structured, on-road environments in densely populated urban areas, leaving off-road autonomy comparatively underexplored despite its importance for applications such as search and rescue, agriculture, and defense. Progress in developing solutions for off-road autonomy is heavily constrained by the high cost, safety risks, and limited coverage of real-world off-road data collection, particularly for rare terrain-induced failure cases. To address these challenges, we introduce a large-scale simulation platform for off-road autonomous driving generated in the Unity3D engine. Our simulator provides indefinite data from multiple RGB cameras, LiDAR, GPS, and IMU sensors to support research in long-duration applications. Our platform also facilitates research in risk-aware planning through dynamic weather, lighting, and annotations for sequence-level failures like collisions and weather-induced sensor blockages. By combining long-duration temporal coverage, multimodal sensing, and procedurally generated terrain diversity, our simulator facilitates progress for systematic evaluation of learning-based autonomous driving models in unstructured, safety-critical environments.
Ross, Timothy, Boone, Julia, Afghah, Fatemeh
Time-Dependent Power Flow Modeling for Non-Passive Loads in Battery Electric Propulsion Architectures2026-01-75639/22/2026
Traditional Linear Circuit Analysis (LCA) relies on steady-state voltage assumptions that are fundamentally incompatible with battery-exclusive propulsion architecture. Whilst LCA remains valid for hybrid systems, where an auxiliary source actively regulates the State-of-Charge (SOC), it fails when analyzing non-passive, constant-power loads. In pure electric architecture, the time-dependent decay of the discharge voltage forces a continuous, non-linear increase in current to satisfy mechanical power demands. A time-dependent power flow methodology is introduced to resolve this theoretical divergence. Using the conservation of energy operating explicitly within the power domain, the time-dependent coupling of current and voltage can be modeled. This approach supersedes steady-state approximations for higher fidelity predictions for component efficiency, system heat generation, and battery capacity requirements for pure electric propulsion systems. Citation: N. Ingarra, K. J. Kobus, J. G. Kobus “TIME-DEPENDENT POWER FLOW MODELING FOR NONPASSIVE LOADS IN BATTERY ELECTRIC PROPULSION ARCHITECTURES” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 11-13, 2026.
Ingarra, Nicholas A., Kobus, Krzysztof (Chris) J., Kobus, Jadon G.
Initial Thermal Evaluation of 422 Martensitic Stainless Steel Piston in a High-Output Diesel Engine2026-01-75649/22/2026
A piston manufactured with a crown comprised of grade 422 martensitic stainless steel and skirt manufactured from 4140 steel was instrumented with fifteen thermocouples and a wireless telemetry system. Piston temperature data were collected at five engine operating conditions and compared to two additional instrumented pistons with crown and skirt both made of 4140 martensitic steel, which is traditionally used for heavy-duty diesel applications. Thermal finite element modeling was used to predict the increase in operating temperature of the 422 piston relative to the 4140 piston and help understand instrumentation uncertainty. Previous research of candidate high-temperature alloys indicated that 12Cr martensitic steel alloys, such as alloy 422, offer several potential benefits when used in a diesel piston application, including increased high-temperature oxidation resistance and strength. The potential benefits of alloy 422 may however be partially negated by the expected increased piston operating temperature due to the alloy’s lower thermal conductivity. In this work 422 alloy resulted in no statistically significant change in piston temperatures relative to the baseline 4140 steel during engine testing. The 422 alloy is poised to offer a dual durability advantage because the initial results show it can achieve superior oxidation resistance without operating at the higher temperatures that would accelerate such degradation. Maximum piston temperature capability is expected to be a critical design limit in next generation diesel engines with greater power density, lower heat rejection, and improved fuel economy. Citation: E. Gingrich, et. al., “Initial Thermal Evaluation of 422 Martensitic Stainless Steel Piston in a High-output Diesel Engine,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 11-13, 2026.
Gingrich, Eric, Tess, Michael, Grunin, Arkady, Korivi, Vamshi, Sebeck, Katherine, Pierce, Dean, Wang, Yiyu, Muralidharan, Govindarajan, Pillai, Rishi, Haynes, James A., Will, Kurt
Mobilizing Innovation: Venture Capital Alignment for Defense with an Autonomous Construction Case Study2026-01-75609/22/2026
Defense acquisition often struggles to match the pace of private investment, slowing the transition of mature commercial technologies into military use. This paper examines how aligning government acquisition with venture-oriented business models can increase industry participation, accelerate fielding, and reduce government program office risk. Using autonomous construction as a case study, it highlights how commercial investment has advanced autonomy while traditional procurement limits adoption. The paper outlines approaches such as non-traditional partnerships, phased acquisition, and performance-linked revenue structures to improve flexibility, leverage private capital, and expand the Defense Industrial Base while speeding operational capability delivery. Citation: Mazzara, M., San Nicolas, A., Gadea, J., Himmel, M., Kruger, J., Gill, C., & Simon, A., Soylemezoglu, A., Netchaev, A., Nottage, D., Klein, J. “Mobilizing Innovation: Venture Capital Alignment for Defense with Autonomous Construction Case Study” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA Michigan Chapter, Novi, MI, August 11–13, 2026.
Mazzara, Mark, Nicolas, Austen San, Gadea, James, Himmel, Max, Kruger, John, Gill, Charles “Spuck”, Simon, Andrea, Soylemezoglu, Ahmet, Netchaev, Anton, Nottage, Dustin, Klein, Jordan
Adopting Cots Technology for UGV Wheel Drive System2026-01-75619/22/2026
As the defense industry prioritizes speed of play to allow our warfighters to maintain a decisive edge over our adversaries, creativity is needed to leverage COTS effectively. This paper presents a case study of a fast-paced workflow leveraging modeling and simulation, targeted risk testing, thermal characterization, and accelerated life testing. Citation: K. May, J. Costa, J. Boyd, “Adopting COTS Technology for UGV Wheel Drive System,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 11-13, 2026.
May, Ken, Costa, Joao, Boyd, Jake
Deriving Instantaneous Electro-Thermal Parameters and Heat Generation from Constant-Current Discharge Data2026-01-75629/22/2026
Early-stage Battery Thermal Management System (BTMS) design can be constrained by limited manufacturer data, resulting in the use of steady-state thermal assumptions. An analytical methodology is introduced to extract the real-time cell resistance, instantaneous efficiency, and transient heat generation directly from standard constant-current discharge curves, and Open Circuit Voltage (OCV) profiles. By evaluating the time-dependent voltage differential against current, equivalent cell resistance and transient heat generation are computed without explicit ohmic measurement or calorimetric testing. This enables direct, real-time coupling of electrical and thermal models. Application to an NMC chemistry cell demonstrates concentration losses dominate below a 20% State-of-Charge (SOC), increasing transient heat generation, and decreasing instantaneous efficiency. Extracting the time-dependent electro-thermal parameters provides the required quantitative inputs for benchmarking cells & accurately sizing BTMS cooling capacities. Citation: N. A. Ingarra, K. J. Kobus, J. G. Kobus, “Deriving Instantaneous Electro-Thermal Parameters and Heat Generation from Constant-Current Discharge Data,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 11-13, 2026.
Ingarra, Nicholas A., Kobus, Krzysztof (Chris) J., Kobus, Jadon G.
Modeling the Operational Performance Impacts of a Vehicle Integrated Power Kit (VIPK) on a Modern Medium Tactical Wheeled Vehicle2026-01-75679/22/2026
Michigan Technological University (MTU) was awarded a competitive prototype project to develop a Vehicle Integrated Power Kit (VIPK) for multiple variants of the Family of Medium Tactical Vehicles A2 (FMTV A2). The VIPK provides high voltage power export, expeditionary power for silent watch capability in low load cases, and interoperability with tactical microgrids. To support VIPK development, MTU created a vehicle model to quantify the impacts of VIPK integration, accelerate design decisions, and predict vehicle performance. This model was calibrated using experimental data, and the calibrated model was then used to compare vehicle performance with and without VIPK installed. Power flow diagrams were utilized to understand the energy pathways during vehicle operations. This paper details how the VIPK system affects performance and analyzes its power flow under select operational conditions. Citation: B. Goodenough, H. Schmidt, J. Naber, P. Dice, D. Subert, K. Meyers, “Modeling the Operational Performance Impacts of a Vehicle Integrated Power Kit (VIPK) on a Modern Medium Tactical Wheeled Vehicle,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 11-13, 2026.
Goodenough, Bryant, Schmidt, Henry, Naber, Jeffrey D., Dice, Paul, Subert, Dave, Meyers, Kevin
Methods for Designing Rotating Components for Compliance to ISO 1940 Balance Requirements using Generic 3D CAD Software2026-01-75659/22/2026
The benefits of specifying balance requirements in terms of an ISO 1940 balance quality grade instead of traditional mass-distance based requirements are discussed along with methods to convert ISO 1940 balance quality grades into permissible imbalance limits at the bearing supports. Methods are developed to determine the expected imbalance values at bearing supports using mass property data from generic 3D CAD software packages without the need for Finite Element Analysis. Practical exercises are presented using these methods to assess a part’s compliance to ISO 1940 while still in the conceptual 3D CAD design stage. These practical exercises cover the selection of appropriate geometric tolerances to ensure part balance without the need for post fabrication balancing as well as the design of nonsymmetrical components for ISO 1940 balance compliance. Citation: J. Srodawa, “Methods for Designing Rotating Components for Compliance to ISO 1940 Balance Requirements Using Generic 3D CAD Software,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 11-13, 2026.
Srodawa, John
Connecting Standard LI-ION Batteries in Series to form a High Voltage Array2026-01-75669/22/2026
The principles of high voltage (HV) battery array design, which is based on existing, off-the-shelf Li-Ion batteries (e.g., certified Lithium 6T Batteries), is presented. The battery array includes, besides the series connected battery modules, an HV switch controlled by a controller unit and additional safety components. The paper first reviews all the safety hazards associated with Li-Ion batteries and HV systems which generate design requirements and constraints. Then the basic design together with the principal components are described. Citation: O. Kost, “Connecting Standard Li-Ion Batteries in Series to Form a High Voltage Array,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 11-13, 2026.
Kost, O.
Development of a UAV Platform for Remote Inspection and Visualization2026-01-75119/22/2026
This paper details the development of an intelligence and inspection platform consisting of an attritable sub-250g UAV, a ground control station, and a visualization interface for users. The UAV architecture combines onboard obstacle detection and avoidance along with simultaneous localization and mapping to have full autonomous navigation inside of complicated GPS-denied environments. The ROS 2-to-Unreal Engine data pipeline allows for sensor fusion, data cleansing, and initial analysis as well as creation of a high-fidelity real-time 3D digital twin. The visualization interface allows users to easily identify critical features and turn data into intelligence to support decision making by soldiers and first responders.
Lee, Yeen K., Bainard, Sean, Shaughnessy, Michael, Bolger, Matt, Koepp, R. Tucker, Salehzadeh, Roya, Mallory, Stephen, Mynderse, James A., Guillen, Pedro, Hernandez, Margarita
Ai-Powered Safety Intelligence as a Decision Enabler for Ground Vehicle MBSE: The Digital Safety Twin Approach2026-01-75329/22/2026
Model-Based Systems Engineering (MBSE) has become a mandated practice for Department of Defense acquisition programs, yet measured benefits remain elusive. The 2024 Defense Science Board found that less than one percent of published literature actually quantified MBSE outcomes, and flagship ground vehicle programs such as the XM30 Infantry Fighting Vehicle have experienced schedule delays attributed directly to insufficient proficiency with model-based approaches. This paper presents the Digital Safety Twin concept: an AI-powered safety intelligence architecture that addresses three of the most labor-intensive and error-prone MBSE workflows. First, the architecture uses hybrid natural language processing and large language model (NLP/LLM) pipelines to auto-formalize unstructured natural language documents into formally structured, traceable requirements. Second, it auto-generates and continuously maintains traceability relationships across requirements, design elements, hazard analyses, and verification artifacts. Third, it provides continuous safety case completeness and confidence assessment through automated Goal Structuring Notation (GSN) synthesis connected to live evidence sources. The approach is grounded in Systems-Theoretic Process Analysis (STPA), the OMG Risk Analysis and Assessment Modeling Language (RAAML), MIL-STD-882E system safety practice, and the UL 4600 safety case framework. We present the methodology, its alignment to the DoD Digital Engineering Strategy, and its applicability to ground vehicle autonomy programs including next-generation infantry fighting vehicles and robotic combat vehicles. We also discuss the limitations, risks, and cultural barriers that must be addressed for AI-augmented safety engineering to achieve acceptance in mission-critical defense applications.
Wagner, Michael, Santini, Nelson, Balakrishnan, Anoop
Designing a Digital Twin–Enabled Requirement Verification Workflow within an MBSE Framework2026-01-75309/22/2026
Verification of functional requirements in Model-Based Systems Engineering environments remains fragmented across heterogeneous tools and manual processes. This paper presents a digital twin–enabled workflow that supports automated requirement verification through integration of SysML models, executable simulation environments, and verification evaluation functions. Within this scope, the objective is to formalize a verification workflow that preserves architectural abstraction while enabling automated, traceable, and simulation-driven evaluation of functional requirements. The approach establishes a continuous digital thread that maintains traceability between requirements, system architecture, and verification outcomes. The workflow is demonstrated using a differential-drive robotic platform, where sensor data availability and update rate verification are used as representative examples of digital twin-based functional requirement evaluation. Results illustrate the feasibility of incorporating digital twin-driven verification into model-centric engineering processes while maintaining consistent verification feedback within the system model. The demonstration produced both passing and failing verification outcomes, illustrating the workflow’s ability to surface requirement-design mismatches.
Zeki, Omar, Sahebsara, Farid, Torkjazi, Mohammadreza, Hieb, Michael R., Raz, Ali K.
An AI-Ready Decision Digital Thread Schema for Digital Engineering2026-01-75339/22/2026
Digital engineering (DE) and model-based systems engineering (MBSE) improve traceability for requirements, architecture, and verification, but concept decisions—the governance events that turn evolving evidence into binding commitments—are poorly captured. Rationale, assumptions, alternatives, model baselines, and approval conditions are scattered across slides and minutes, limiting auditability, reproducibility, and automation. We propose a Decision Digital Thread (DDT): a typed graph schema that makes decisions governable by linking framing and scope, structured (including set-based) alternatives, uncertainty and risk, immutable evaluation-run provenance with reviewed evidence, and commitment events with machine-actionable conditions, authorized actions, and outcome feedback. DDT serves as the decision system of record and a contract between platform modules and enterprise policy while referencing MBSE/PLM/simulation artifacts via stable identifiers and configuration context. Policy-driven readiness gates block lifecycle transitions when evaluator coverage, evidence review, or bias checks are incomplete. An electric pickup range-extension case demonstrates auditable gates, evidence lineage, and safe AI-agent authority boundaries.
Chinnam, Ratna Babu, Murat, Alper, Rana, Satyendra, Rapp, Stephen H., O’Bruba, Joseph G., McGregor, Michael, Bechtel, James E., Costa, Laura W.
Electrical and Power Quality Performance Evaluation of a SiC Based 500kW High Temperature and High Power-Density Inverter2026-01-75689/22/2026
In pursuit of future high-power capabilities for U.S. military ground vehicles, the transition towards vehicle electrification has been heavily adopted. High power-density and high temperature inverters play a key role in progressing vehicle electrification adoption across the U.S. military. This paper presents experimental results to evaluate the power quality performance of the developed high power-density and high temperature inverter, Enercycle™ DC-1000 Inverter based on silicon carbide (SiC). The DC-1000 inverter is a bi-directional inverter with a power density of 11.4 kW/L, which is capable of operating at 600 Vdc and delivering 500kW continuous output power and transient output power up to 640 kW, enable ground vehicle electrification. The experimental results to evaluate the power quality aspects such as distortion factor, ac voltage ripple, and voltage transient due to step load are presented in this paper. Moreover, challenges and next steps for further improvement of design have been discussed. Citation: A. Sadigh, Iris Shiroma “Electrical and Power Quality Performance Evaluation of a SiC Based 500kW High Temperature and High Power-Density Inverter,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 11-13, 2026.
Sadigh, Arash, Shiroma, Iris
Comparative Analysis of BEV Performance from Simulated Environment and Physical Hardware Platform2026-24-00059/21/2026
The proliferation of simulation environments has accelerated technological progress across various scientific domains by offering a cost-effective and time-efficient framework for data acquisition and analysis. In the automotive sector, high-fidelity modelling of vehicle components and driving scenarios bypasses the logistical constraints associated with hardware procurement and the intensive requirements of large-scale testing infrastructures. However, pre-calibrated or native software models often imply simplified hypotheses, missing relevant aspects of the entire powertrain-to-wheel energy chain. This study presents a comparative analysis of battery performance within a battery electric vehicle (BEV) by synchronizing virtual simulations with experimental hardware at the test bench. The methodology involves the concurrent modelling of the driving environment, the vehicle chassis, and the propulsion system, followed by the execution of identical driving cycles on a physical platform. The experimental setup comprises a fully instrumented BEV featuring an integrated electric motor and battery pack, specifically configured for high-precision signal acquisition. The virtual section starts with the development of a digital twin within a commercial simulation suite, parameterized according to the vehicle specific dynamic and energy requirements. This is followed by the integration of the electric propulsion system and a battery pack model based on the equivalent circuit model method. To ensure high fidelity, the battery model is experimentally calibrated via multi-step pulse discharge tests performed on the physical hardware. Subsequently, various driving scenarios from the simulated environment are translated into speed-time profiles and are replicated on the real vehicle using a PID-controlled actuator on the accelerator pedal. The battery pack that serves the vehicle is monitored during the cycle to collect information on the electrical performance. Finally, a comparison between the simulated and real battery behaviour is performed. This dual approach used in the present work, which compares the simulation accuracy against real-world performance, provides critical insights into the inherent advantages and technical boundaries of digital modelling in electromobility applications.
Sequino, Luigi, Sementa, Paolo, Altieri, Nunzio, Vaglieco, Bianca Maria, Sorrentino, Chiara
Physics-Informed Machine Learning Digital Twin of Electro-Mechanical Brakes for Clamp-Force and Friction Estimation2026-01-08069/14/2026
Recently, there has been a drastic shift in the industry towards wire architectures like steer-by-wire and brake-by-wire. For safe and accurate force control, diagnostics, and consistent performance over the operating envelope, accurate plant modeling of the Electro-Mechanical Brake (EMB) is important. Classical approaches involved linearized dynamic EMB models and the use of the characteristic stiffness curve for calibration at the operating points. These methods often perform poorly over regions where hysteresis, compliance, and friction are strongly nonlinear. Prior research on state or force estimation for EMB has focused on pad contact detection, thermal adaptation, and hysteresis-aware clamp force estimation. However, there are still accuracy gaps in practical applications during transients and under shifting friction regimes. In this work, a digital twin based on Physics-Informed Machine Learning is introduced, following the governing dynamics of the actuator-caliper assembly of EMB while learning (i) a physically significant parameter—system damping (Bsys) and (ii) a non-linear friction term constrained as a function of the actuator motion states and operating conditions. Non-linear friction is captured through gray-box friction formulation and learning unmodeled residual dynamics such as hysteresis and backlash. An EMB test stand is used to collect steps, ramps, holds/engagements, APRBS, and swept-sine excitations, with signals including time-aligned force command, motor torque/current, actuator position/velocity, and pad force measurement from a force sensor for model training. Results demonstrate a decrease in pad-force prediction error, along with non-linear and residual friction estimation. The resulting digital twin can enable sensor-less force estimation, friction compensation design, predictive analytics, and health monitoring through tracking parameter drift and friction signatures.
Rai, Prakhar, Gadhvi, Tirth
Analysis of Influencing Factors of Human-Factor Reliability in Metro Dispatching System Based on Structural Equation Model2026-99-15919/11/2026
As system automation advances, the impact of human factors on human-machine system reliability becomes increasingly prominent. Given that the metro train dispatch system is central to metro operations, analyzing its human reliability aspects is crucial. Although human factor reliability analysis techniques have matured in fields like nuclear power and aviation, research on human-factor reliability analysis for metro train dispatch systems remains in its infancy. Based on this, the study proposes a method for identifying factors influencing human-factor reliability in metro train dispatch systems using exploratory factor analysis, grounded in survey data on such factors. Second, based on the identified factors, a structural equation model was constructed to identify the importance of human reliability factors in metro train dispatch systems. Through goodness-of-fit evaluation, the causal relationships among these factors were ultimately determined. The study indicates that individual, organizational, equipment, and environmental factors are the primary influences on human reliability in metro train dispatch systems, with 20 observable sub-factors under these main categories. The structural equation model results indicate that the relative importance of the four primary factors on human-factor reliability in the metro train dispatch system is: organizational factors > personal factors > equipment factors > environmental factors. This suggests organizational factors exert a relatively greater influence on human-factor reliability. This research provides a basis for enhancing the safety level and management decision-making of metro train dispatch systems.
Li, Xin, Wang, Liang, Tang, Shuo, Yao, Zhenxing
Integration of Configuration Management into the RFLP Process in the Development of Civil Aircraft2026-99-14179/11/2026
In the forward development process of civil aircraft, traditional configuration management, which primarily focuses on the physical implementation end, often leads to inconsistencies between functions, requirements, design configurations, and physical realizations. This study optimized the principles of configuration item identification by refining the logic, timing, and sequence for identifying different types of configuration items. It proposed a product structure centered on the Logical Identification Number (LIN), which explicitly represents the mapping relationships from functional to physical elements. Additionally, the research established the logic for change propagation and validity calculation. Using an air-conditioning refrigeration system as a case study, the model was validated, demonstrating its advantages for improving the efficiency of change-impact analysis, enhancing compliance verification, and ensuring scenario reproducibility.
Xie, Xiang, Meng, Xu, Zhang, Xinyuan, Wu, Binbin
Research on High-Reliability Testing Systems for Intelligent and Connected Vehicle2026-99-11119/11/2026
With the large-scale application of intelligent connected vehicles, the verification of their functional safety and reliability has become a core bottleneck in the industrial development. The traditional real- vehicle road test method can no longer meet the current demand for large-scale test verification due to problems such as high cost, low efficiency, and difficulty in reproducing dangerous scenarios. This paper studies the vehicle-in-the-loop simulation test system based on a digital twin. By constructing a virtual scenario highly consistent with the real world, physical-level multi-source perception signals are simulated and mapped to the system under test to enable high- reliability verification of real vehicles. In terms of lateral and longitudinal control functions, multiple sets of test cases are selected respectively for comparison between road tests and virtual simulation tests. The results show that the accuracy of key indicators is above 90%, which provides practical reference for the subsequent test and verification system of high-level autonomous driving.
Hou, Quanshan, Tang, Ke, Gao, Tian, Chen, Tao, Zhou, Si
Optimization and Control of Mechanical Testing for Batch-Produced Manned Spacecraft2026-99-03128/14/2026
To safely, efficiently, and high-quality complete the mechanical testing of batch-produced manned spacecraft during the China Space Station (CSS) phase, a series of optimization measures were proposed based on system engineering principles. These measures cover the entire mechanical testing process from preparation to implementation, including: establishing a standardized mechanical testing documentation system; reducing the number of mechanical sensors that do not affect result evaluation; pre-identifying and measuring background noise; digitizing test notching and evaluation methods; and standardizing and automating testing procedures. Additionally, targeted measures for test safety and quality control were implemented, including regular inspections of reusable spacecraft components, strict control of test hazards and operational risks, and standardized management of ground support equipment (GSE) through regular inspections. The proposed optimization and control measures have been validated through applications in batch-produced manned spacecraft during the CSS phase. The results show that: the generalization rate of mechanical testing documentation exceeds 80%; the number of mechanical sensors has been reduced by more than 10%; the test preparation period has been shortened by over 4 days; test efficiency has been improved by 30%; the single-direction test duration has been reduced by more than 50%; and the total test cycle has been shortened by 25%. These results indicate that the proposed optimization and control measures are reasonable and feasible, which effectively reduces redundant test operations and items, lowers potential test risks, improves test efficiency, shortens the overall test cycle, enhances test safety, and ensures the high-quality completion of mechanical testing for batch-produced manned spacecraft.
Peng, Huakang, Wang, Mengchen
Modeling Method of Aircraft Hydraulic Pipeline Cleaning Test Equipment Based on MBSE2026-99-03318/14/2026
With the continuous improvement of performance requirements for aviation equipment, the importance and complexity of hydraulic systems as the core carrier of flight control are becoming increasingly prominent. The cleanliness of aircraft hydraulic pipelines directly affects the reliability and flight safety of hydraulic systems, and it is necessary to use specialized cleaning and testing equipment during design and manufacturing to achieve efficient cleaning. The design of traditional cleaning equipment relies on experience-driven development, with mechanical, hydraulic, and electrical systems developed independently. There are problems such as unclear requirement definitions, low efficiency of interdisciplinary collaboration, and lagging validation, making it difficult to achieve the goal of forward design. Therefore, this study introduces Model-based Systems Engineering (MBSE) method in the development process of pipeline cleaning test equipment, proposes a modeling process based on RFLP (Requirements-Function-Logical-Physical), and uses SysML system modeling language to construct a top down design model system for aircraft hydraulic pipeline cleaning equipment. Through requirement analysis modeling, functional behavior definition, and system architecture design, the significant advantages of MBSE method in the development of complex aviation test equipment have been verified, effectively improving the bold design capability and top down design efficiency. MBSE method can not only improve the design efficiency of equipment, but also promote the intelligent and efficient operation of equipment, which has important significance for the development of intelligent manufacturing and electromechanical integration technology.
Zhang, Yuxin, Ma, Zichen, Li, Qi, Song, Guoqiu, Li, Haiwei, Zhang, Jingjing
Design of a Digital Twin for Robot-Assembled Workpiece Transfer Station and Virtual Commissioning with It2026-99-03478/14/2026
In this research, the design of a digital twin system for a Robot-Assembled Workpiece Transfer Station (RAWTS) and virtual commissioning with it were detailed, aiming for debugging high-repeatability, high-precision robotic motions. The system employs a structured three-layer digital twin framework, Physical, Digital, and Information Fusion layers, interconnected via an OPC UA communication architecture to enable real-time virtual-physical data synchronization. The 6-axis industrial robot’s kinematic model is established using the D-H parameter method, and the translational end-effector’s kinematic relationships are configured with defined OPEN/CLOSE poses. A behavior-driven digital twin model is constructed within NX MCD, incorporating lightweight-processed 3D geometry from SolidWorks. Virtual commissioning involves PLC and robot program integration, OPC UA-based signal mapping, and kinematic path planning with reachability validation to avoid singularities and collisions. Key joint angles at critical path points are optimized, and virtual-physical integration debugging is performed, resulting in first-attempt success in physical operation. The study demonstrates that the NX MCD-based digital twin approach effectively validates control logic, optimizes robot trajectories, reduces on-site debugging time, and enhances operational precision and safety, offering a practical reference for digital twin applications in robotic systems.
Zang, Yuping, Wang, Ye, Fu, Hudai, Li, Weiwei, Jiang, Zhiyu, Wang, Dayu
SAE TOMORROW TODAY - How Aerospace Safety Standards Enable the Future of Flight135808/12/2026
Whether it's supersonic travel, commercial space, or AI-enabled aircraft, one message remains constant: safety standards are the common language that enable the future of flight. Listen in as we sit down with Craig McMillan, Model Based Systems Engineering (MBSE) Growth Leader at GE Aerospace, to discuss the intersection of AI, digital engineering, and the growing complexity of modern aircraft. The conversation examines why independent verification, rigorous systems engineering, and collaboration across the aerospace industry remain essential as aircraft become more software-driven and interconnected. 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
A Foundational CFD Validation for an Occupant-Centric HVAC Digital Twin: Paving the Way for Real-Time Control2026-99-10028/7/2026
The heating, ventilation, and air-conditioning (HVAC) systems are one of the main factors that contribute to the building’s energy usage. Achieving an effective balance between reducing energy use and maintaining acceptable thermal comfort is the key challenge in conventional HVAC systems. To overcome this challenge, integrating the occupant-centric controls coupled with digital twins into HVAC systems is another potential technique for this effective balance. For this purpose, computational fluid dynamics (CFD) offers the potential, in combination with other surrogate models for real- time applications to enhance the system's performance further. In general, the CFD is applied to investigate indoor airflow/temperature distributions. These are essential for occupant health, comfort, and energy optimisation for the HVAC design state. The objective of this study is to propose an initial step toward building an occupant-centric HVAC digital twin by validating a CFD model of an office against dense in-situ sensing data. The model has been used to resolve airflow and temperature stratification under conventional HVAC operations, using ANSYS Fluent. The boundary conditions have been derived from measured supply parameters, internal gains, and local weather conditions. The results from this study show that the air velocity and temperature at selected durations follow the same trend with low errors, compared to the sensing and measurement data. The model validation from this study establishes the basis for a weather- aware, occupant-feedback digital twin for larger floorplates and multi-zone systems. To achieve the target of the energy and comfort co-optimisation in Industry 4.0-ready buildings, the future work will focus on surrogate modelling to enable near-real-time inference for closed-loop occupant-centric controls, which will directly support dynamic set-point adjustments and multi-zone system ventilation.
Larpruenrudee, Puchanee, Hellany, Ali, Famakinwa, Tosin, Shrestha, Surendra, Attwater, Roger, Calheiros, Rodrigo Neves
Implementation Strategies of Requirements Engineering in Commercial Aircraft Airframe Forward Design2026-99-01837/31/2026
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 Modeling2026-99-02137/31/2026
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 Industry2026-99-01177/31/2026
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 Printer2026-99-08597/30/2026
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 Architecture2026-99-08347/30/2026
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 Equipment02-19-03-00197/27/2026
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 Corrosion2026-99-16117/24/2026
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 Lines2026-99-18047/17/2026
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 Platform2026-99-05957/10/2026
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 Estimations2026-01-07447/1/2026
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 Artifacts2026-01-07707/1/2026
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 Automation2026-01-07777/1/2026
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
How Industrial Artificial Intelligence and the Digital Twin Accelerate Innovation in Life SciencesTBMG-554276/25/2026
Pharmaceutical and life sciences manufacturers are under growing pressure to compress development timelines, from discovery to commercialization, as demographic, technological and geopolitical trends increase the pace of innovation and disruption. In the face of these challenges, many pharmaceutical manufacturers are finding their traditional processes, which are often built on fragmented data and highly manual workflows, are insufficient.
Training Physical AI with the Digital Twin: A Faster Path to Flexible RoboticsTBMG-554316/25/2026
Automation has been a key part of manufacturing for over a century now, from the simple assembly lines of the past to the advanced, autonomous robotics of today. As the stresses placed on manufacturing systems continue to increase, however, the abilities of automated systems must increase as well. To meet the manufacturing demands of the 21st century, factory robotics must move beyond inflexible, hard-coded orders and gain the ability to quickly adapt to changing conditions — whether they be sudden business demands or new production requirements. This level of flexibility requires artificial intelligence (AI) certainly, but not just any AI; rather AI that can understand and interact with the real world. In other words, physical AI.
Validation of an Adaptive Order Finite Element Model for Transmission Loss Simulation of Trimmed Automotive Structures2026-01-07076/20/2026
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 StandpointDRRC-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 NoticeAIR4757 (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 Directions05-19-04-00296/10/2026
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 Safety15-19-02-00106/9/2026
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.
Can Large Language Models Enable Sustainable Driving Behavior? A Study on Large Language Model-Based Eco-Driving13-07-02-00066/9/2026
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
An Open-Source Framework to Automate Virtual Optimization of Internal Combustion Engine Calibration Maps through Shape-Based Strategy2026-37-00016/9/2026
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
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