Browse Topic: Analysis methodologies

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Application of Machine Learning for the Prediction of Automotive Panel Oil Canning to Accelerate Design Exploration2026-28-0187To be published on 11/05/2026
Oil canning, characterized by localized buckling and surface waviness in thin metal panels, is a critical quality parameter in the automotive industry. Since outer panels serve both structural and aesthetic functions, even minor surface distortions can adversely affect perceived vehicle quality. Traditionally, evaluation of oil canning relies on extensive nonlinear Finite Element simulations. For example, assessing a single hood design variant may requires thousands of nonlinear Finite Element analyses. Considering multiple design iterations across major vehicle panels including hood, roof, doors, fenders, and trunk, the total number of simulations can exceed tens of thousands, leading to substantial computational cost and development time. Consequently, this conventional approach is both time intensive and resource demanding. To address these limitations, advanced Machine Learning techniques are employed to significantly reduce simulation effort as well as accelerate design exploration. Initially, a deep learning based one-dimensional Reduced Order Model is developed to predict load–displacement response and oil canning propensity for a given panel. Indentation points on the hood panel are selected using a space filling Latin Hypercube sampling method. The Machine Learning based one-dimensional Reduced Order Model is trained by using 300-500 indentation points and validated using approximately 40-50 unseen indentation points. The verification is performed on multiple hood geometries. The predicted results demonstrate strong correlation with Finite Element generated load–displacement curves with that of ML based 1-dimensional reduced order model. Furthermore, to enable rapid design exploration across different panel geometries, a dataset of 40 different hood designs is utilized. A geometric deep learning approach is applied to cluster panel geometries, and the resulting representations are used within a 3-dimensional deep neural network tool Ansys SimAI™ to predict oil canning propensity and quantify it using the canning ratio. Validation study is carried out on unseen panel geometries, and the Machine Learning predicted oil canning zones show good agreement with Finite Element identified critical regions. The proposed Machine Learning framework reduces prediction time from hours to minutes, offering a scalable and cost-effective solution for early-stage design evaluation and mitigation of oil canning in automotive panels.
Sarnobat, Rajvardhan Vilasrao, Kumar, Dinesh, Jawale, Akshay, Balaji, Ananya, Kulkarni, Shreya
Smart-ROPS — Machine Learning-Based Rear Occupant Protection System2026-28-0149To be published on 11/05/2026
Children dying in hot cars is not a new problem. Since 1998, more than 1,000 children have died this way in the United States alone — roughly 37 every year. That number has barely been reduced, which tells something alarming: the solutions built so far aren't solving it completely. Door-based reminders are a good example of why. They trigger when a rear door opens, but opening a door doesn't confirm a child was left behind. Single-sensor setups have their own gaps too; no single sensor handles every real-world scenario reliably on its own. Multi-sensor systems get closer, but most use rigid threshold-based rules that fall apart the moment conditions deviate — a sleeping infant barely moves, a child under a blanket reads completely differently on a thermal sensor. This paper proposes Smart-ROPS (Smart Rear Occupant Protection System), an embedded ML architecture that targets these specific failure modes without needing expensive hardware or complex software. Four sensor types feed the system: millimeter-wave radar, CO₂, thermal infrared, and ultrasonic. Data from these passes through three layers — the Perception Layer handles initial sensor fusion, the Processing Layer runs a decision tree as the primary classifier, and when confidence is low, a shallow neural network steps in for ambiguous cases. If a risk is confirmed, the Intervention Layer manages the response: starting with a smartphone alert and cabin ventilation, escalating to emergency services if needed. This is presented as an architectural design contribution — hardware prototyping and real-world testing are left for future work.
Shrimal, Harsh
Overtake Safety Margin Formula Blind Spot Characterization Using a Micro Transformer with Monte Carlo Dropout Uncertainty and CARLA Validation2026-28-0141To be published on 11/05/2026
Overtaking another vehicle on a two-lane road can turn dangerous within a few seconds, especially when the gap to an oncoming vehicle is short or that vehicle is moving fast. Many driver-assistance systems rely on a simple, fast rule called the Overtake Safety Margin formula to decide whether an overtaking maneuver is safe, without running a full traffic simulation. This study checks the formula's reliability using a physics-based simulator that models three vehicles: the car overtaking, the car being overtaken, and the oncoming car. One million generated overtaking scenarios were evaluated using both the OSM formula and the kinematic simulator to identify cases where the formula classified a maneuver as SAFE while the simulator predicted an UNSAFE outcome. These mismatches, called blind spots, represented 3.98% of the evaluated scenarios and were concentrated in high oncoming-speed, short-gap situations, pointing to a specific weakness rather than random error. To catch these blind spots automatically, a small and efficient machine-learning model, a micro-transformer, was trained on the simulator's results. It reached 96.3% overall accuracy and correctly flagged 86.9% of the formula's blind spots, outperforming LightGBM, which detected 73.2%. The model's confidence scores were well calibrated, so they can be trusted to trigger an extra safety check when needed. An uncertainty-based search then used these confidence scores to find risky scenarios efficiently: 187 of the 500 most uncertain cases turned out to be unsafe, about 9.4 times more effective than random selection. Finally, the 30 highest-priority cases were replayed in the CARLA driving simulator, and 27 were confirmed to reproduce unsafe outcomes. Together, these results show that pairing a fast rule-based formula with a calibrated learning-based monitor can catch dangerous situations the formula alone would miss, supporting the safety documentation required for automotive safety-of-the-intended-functionality standards.
Sekaran, Nikhil, Sehgal, Vishal
Influence of Transversely-Isotropic Lining Elastic Constants on Brake Squeal in S-Cam Drum Brakes: An Optimization Framework2026-28-0161To be published on 11/05/2026
Brake squeal in heavy-duty S-cam drum brakes remains a persistent NVH challenge, primarily driven by friction-induced modal instabilities. Existing studies on drum brake squeal mitigation largely emphasize geometric modifications, stiffness tuning, or the application of damping treatments, while the influence of directional stiffness characteristics of friction linings remains insufficiently explored. This research presents a novel, material-centric approach by modelling the brake lining as a transversely isotropic material, characterized by six independent elastic constants, and systematically quantifying their influence on squeal-prone unstable modes using Complex Eigenvalue Analysis (CEA). A validated finite-element model of a production S-cam drum brake assembly is developed and correlated through experimental modal analysis, ensuring close agreement between predicted and measured natural frequencies and mode shapes. The validated model is then integrated with a Design of Experiments (DOE) within commercially available FEA software to quantify both main effects and higher-order interactions across practical ranges of in-plane and out-of-plane elastic moduli and shear constants. To enhance the predictive accuracy of the surrogate models, a subsequent sampling–fit study is conducted, significantly improving the coefficient of determination (R2) of the response surfaces. The refined surrogate models are employed within a Global Response Surface Methodology (GRSM) framework to perform multi-parameter optimization aimed at minimizing squeal propensity under representative operating conditions. Finally, complex eigenvalue analysis quantifies the impact of the optimized transversely isotropic property combinations on modal stability, establishing a predictive, optimization-driven methodology for robust, noise-free drum brake design. The study culminates in the identification of an optimized transversely isotropic elastic-constants signature for the friction lining, providing actionable targets for friction material formulation and reinforcement alignment. Unlike prior research focused on isotropic material assumptions or component-level design changes, this first-of-its-kind methodology advances the state of the art by enabling friction material– centric NVH optimization for heavy-duty drum brake systems.
Philip, Rony, Muralidharan, Sudharsan, Mohanam, Gopalakrishnan
Enhancing FEA Preprocessing Efficiency through a Multi AI Model Strategy2026-28-0174To be published on 11/05/2026
Finite Element Analysis (FEA) plays a vital role in accelerating product development and reducing overall engineering costs. Accurate simulation of powertrain components requires extensive preprocessing of solid models. Detailed contact definitions and bolt representations are required to properly evaluate screw joints. Pipe joints present similar challenges, as they often incorporate various types of nuts and connectors. As a result, engineers spend a significant amount of time manually modelling these joints. Automating the modelling process is difficult due to the high variability in bolt sizes and pipe joint designs. This paper proposes AI-based models to automatically classify these components and extract their distinctive geometric features. Because bolts and pipe joints have wide feature variations, developing a generalized AI model requires a large dataset which is often hard to obtain. To address this limitation, this paper explores strategies for generating smaller, specialized AI models tailored to each unique joint type. This study describes the development of deep learning neural network models capable of learning distinguishing features of different joint configurations from limited training data. A multilayer graphical neural network architecture is employed, enabling efficient extraction and representation of these unique geometric characteristics. The proposed models can robustly generalize different joint types while maintaining high classification accuracy and reliable feature detection despite constrained training data availability. Automated workflows have been implemented in commercially available preprocessing tools to validate the proposed AI methodology and demonstrate its effectiveness in practical engineering applications. The integration of AI model automation in preprocessing tools significantly reduces engineering effort and repetitive manual preprocessing steps. This work highlights the potential of hybrid AI models to accelerate CAE workflows and provides scalable framework for future automation in FEA.
Jayachandran, Janarthanan, Agarwal, Deepak
Thickness Optimization of Constrained Layer Damping Shims for Reducing Friction-Induced Instabilities in Drum Brakes2026-28-0160To be published on 11/05/2026
Commercial vehicles predominantly employ drum brakes because of their low cost, robustness, and simple construction. However, with the increasing push toward quieter powertrains, particularly in electric commercial vehicles, brake squeal has become a dominant noise source. The inherent complex and chaotic nature of brake squeal makes its prediction and suppression especially challenging. Traditional squeal mitigation strategies, such as modifying component mass and stiffness or using materials with higher native damping (e.g., cast iron), are inherently constrained in their capacity to mitigate instability. In such scenarios, constrained layer damping (CLD) shims have proven to be an additional, effective and practical solution in disc brakes, which is motivating growing interest in extending similar concepts to drum brake applications. A key challenge in adopting shims for drum brakes lies in identifying and achieving the required damping level necessary to suppress squeal while adhering to stringent packaging, manufacturability, and cost constraints of foundation brakes. Establishing a clear relationship between squeal propensity, noise intensity, and target damping is therefore essential. While past CLD studies discuss layer-thickness effects in generic structures and caliper brakes, no work applies thickness optimization to brake shims in a drum brake assembly. This paper presents a systematic and target-driven framework for the design of CLD shims for drum brake applications. The approach initially defines the damping requirement for squeal suppression using system noise metrics and dynamic stability evaluations. Based on this requirement, a design optimization approach is employed to determine the appropriate thickness distribution of the individual shim layers, while accounting for practical constraints such as packaging limitations, material feasibility, and manufacturing considerations inherent to foundation brake systems. The optimized CLD shim configurations obtained through this process are subsequently integrated into the drum brake assembly and evaluated using complex eigenvalue analysis. The simulations utilized a validated FE model of the S-cam drum brake assembly, whose accuracy was established through experimental modal analysis–based correlation. The results indicate that the proposed target-based shim design methodology leads to a measurable improvement in modal damping and system stability, thereby demonstrating its effectiveness as a practical and robust approach for brake squeal mitigation in drum brake systems.
Muralidharan, Sudharsan, Philip, Rony, Mohanam, Gopalakrishnan
Advanced Semiconductor Derating Methodology for Electronic Circuits and Validation2026-28-0172To be published on 11/05/2026
In current and futuristic electronic designs, ensuring the accuracy and reliability of electrical circuits over all component parameter variation and operating condition, requires validation across multiple domains - mathematical modelling, simulation, and physical measurements. Before committing to physical board fabrication, simulation has become a critical step in the circuit design process. With increasing design complexity, engineers rely heavily upon simulation tools to predict circuit behavior, optimize performance, and reduce the number of design iterations. The effectiveness of simulation lies not only in its ability to provide accurate representations of real-world circuit behavior, but also capturing key electrical characteristics such as voltage levels, frequency response, accurate modeling of component tolerances and coefficients, etc. It should also excel in efficiency-allowing rapid iteration, testing under varied conditions, evaluation of multiple scenarios without excessive computational costs, and quick comparisons of design alternatives. This paper presents an advanced semiconductor derating methodology that addresses the limitations of conventional approaches through automation. The derating algorithm has been developed and implemented using Maple (Mathematics based Tool) to generate precise worst-case values of semiconductor component parameters adjusted for specific circuit operating conditions. This automated tool significantly enhances both the efficiency and accuracy of mathematical analysis results, providing engineers with more reliable performance predictions for electronic circuit design. This paper also presents a comparative study that analyzes and correlates the performance of an electrical circuit obtained through worst-case mathematical analysis using internal tool, simulations using industry-standard tools, and the result collected during internal testing of the product. The objective is to evaluate how closely theoretical predictions and simulations align with real-world behavior and to identify factors contributing to any discrepancies.
Mishra, Anshul, M. S., Suhas
RADAR-Based Object Merging and Retention Decision for Long Vehicles Using Fuzzy Logic2026-28-0145To be published on 11/05/2026
In autonomous driving and advanced driver-assistance systems (ADAS), radar sensors play a crucial role in detecting and tracking objects under diverse environmental conditions [1, 2, 3]. One persistent challenge is the accurate representation of long vehicles, such as trucks and buses, whose extended radar signatures often result in a wide spatial spread of detections with similar radial velocities, making association difficult due to gating limitations. The GRID component processes raw radar measurements to generate occupancy grids and extract candidate targets, including stationary environmental structures [4, 5]. In the case of stationary long vehicles, multiple stationary objects may overlap with grid-based environment targets, allowing them to be consolidated into a single tracked object. This paper presents a novel approach for radar object merging and retention decision-making using fuzzy logic to distinguish between true moving and stationary objects in the presence of GRID targets and object association mechanisms. The association modules link radar measurements or detections, when available, with existing tracks to maintain temporal consistency, enabling the formation of radar objects represented by track points. The proposed method fuses radar objects based on their radial velocity and spatial overlap, while a fuzzy logic–based decision engine evaluates object persistence. The approach improves object continuity and reduces false positives in driving functions such as Blind Spot Detection (BSD), Emergency Brake Assist (EBA), and Adaptive Cruise Control (ACC). Simulation and real-world validation results demonstrate significant improvements in object tracking accuracy, particularly for long vehicles in urban environments.
Mohanty, Sanket, Sarkar, Sanglap
Rapid Optimization of Body Structures Using an Interface Simplified Super-Element Model2026-28-0183To be published on 11/05/2026
Finite element (FE) models of Body In White (BIW) structures are inherently large and computationally intensive due to a high number of degrees of freedom. Performing optimization studies of body structures in a full BIW model requires a significant amount of time, as each optimization cycle requires multiple iterations of the complete FE model. To reduce computational effort, techniques such as the Component Mode Synthesis (CMS) based reduction methods are commonly utilized. In this work, we adopt the Craig Bampton Nodal (CBN) approach by retaining the component targeted for optimization as the residual structure, while condensing the remaining BIW into a super element. This strategy significantly decreases simulation runtime. To generate the reduced model, all the interface nodes that connect the retained residual with the super-element structure need to be defined as analysis sets (ASETs). Earlier studies primarily considered residual structures connected through simple RBE2 bolt connections, resulting in a relatively small set of ASET nodes. In the present work, we address more realistic BIW assemblies incorporating complex joining technologies such as spot welds, structural adhesives, bolts, rivets and other intricate connection schemes. This leads to a substantially larger number of ASET nodes. A high ASET count increases boundary condition complexity, memory requirements, and overall simulation runtime, thereby limiting the efficiency of the reduced model. This paper proposes a novel methodology to simplify the connection interfaces between the optimization component and the rest of the BIW while preserving essential dynamic characteristics. With this strategy the number of ASET grids is reduced by roughly 70%, thereby reducing the computation time by approximately 80% without compromising accuracy. The resulting reduced order model enables rapid execution of multiple optimization studies through an efficient use of computational resources, thereby reducing overall lead time and improving the feasibility of iterative design exploration in early vehicle development.
Rajendran, Hariprasad, Pal, Arkaprabho, Schauwecker, Florian
Integrated Virtual and Field Assessment of Stabilizer Bar for Truck Rollover Stability and Strain-Force Calibration2026-28-0168To be published on 11/05/2026
Rollover stability is a critical safety requirement for heavy-duty trucks, particularly during severe manoeuvres and tipping-prone operating conditions. Accurate estimation of stabilizer bar forces plays an important role in assessing vehicle rollover behaviour. This study presents a finite element-based virtual strain gauge calibration methodology for estimating stabilizer bar forces from measured strain signals, using a heavy-duty vehicle tipping stability application as an industrial case study. A high-fidelity vehicle dynamics model was developed to assess the influence of the stabilizer bar on vehicle roll performance. To increase confidence in the virtual assessment, strain measurements were obtained from instrumented stabilizer bars during durability track testing and correlated with simulation results. A key challenge in the measurement process was the calibration required to convert strain signals into applied forces. To address this, a virtual calibration approach was developed using finite element analysis to establish linear strain-force transfer relationships. By combining FEM-derived transfer functions with measured strain data, stabilizer bar forces were successfully reconstructed under real operating conditions without the need for conventional physical calibration. Correlation between simulation and test measurements demonstrated the capability of the proposed methodology to estimate component loads with good accuracy. The results show that virtual strain gauge calibration can significantly reduce reliance on dedicated test rigs and prototype-based calibration activities, enabling earlier load assessment and more efficient chassis development. The methodology is scalable and can be extended to other dynamically loaded suspension and chassis components for durability and vehicle dynamics applications.
Thirunavukkarasu, Siddeshwaran, Sheepri, Sunil, N., Balagangadharan
A Sequential Structural Optimization Framework for Chassis Components Using Efficient Linearized Finite Element Models2026-28-0200To be published on 11/05/2026
As the global automotive industry increasingly prioritizes electrification and long-term sustainability, the demand for aggressive lightweighting in automotive chassis engineering has intensified. This study presents a process-driven, multi-stage structural optimization workflow designed to maximize total mass reduction while maintaining rigorous structural performance. To mitigate the high computational costs and software license overhead of nonlinear Finite Element (FE) analysis, the methodology transforms complex models into efficient linear representations. This strategic shift preserves critical structural behaviors while significantly enhancing license efficiency and total computational simulation throughput. The novelty of this workflow lies in an integrated optimization loop that begins with Topology Optimization to define primary load paths, followed by Free Shape Optimization to mitigate stress concentrations. By embedding manufacturing constraints such as draft angles and minimum thickness directly into the optimization process, the methodology effectively eliminates traditional redesign cycles. Optimized designs are then seamlessly translated into CAD via advanced ANSA tools for validation of von Mises stress, plastic strain, and buckling. This synchronized transition from analysis to realization reduced the development cycle by 50%, enabling a design freeze significantly faster than conventional methods. The integrated approach achieved a 15% mass reduction while meeting all functional durability and crash requirements. Given that a 1% reduction in mass correlates directly to a 1% reduction in CO2 emissions, the resulting design substantially minimizes the vehicle's total carbon footprint. By lowering material consumption and maximizing computational efficiency, this study offers a repeatable solution for balancing simulation fidelity with the urgent demands of modern sustainable automotive design.
Venkataraman, Nageswaran, Rajendran, Hariprasad, Karimbedu, Saikumar, Yadav, Balwan
Efficient Finite Element Modeling and Sensitivity Analysis of Adhesive Joints for Early-Stage Battery Pack Design2026-28-0190To be published on 11/05/2026
The increasing adoption of structural adhesives in electric vehicle battery packs introduces challenges in predicting joint performance under complex loading and operating conditions. While advanced material models can capture detailed failure mechanisms, their application in early-stage design is often limited by extensive material characterization requirements and high computational cost and time. This study presents a computationally efficient finite element methodology for screening-level assessment of adhesive joint stress distribution and utilization using readily available material data from standard datasheets. The approach is specifically developed for early design stages, where rapid assessment, limited input data, and multiple design iterations are critical. The method is compared against adhesive manufacturer material datasheet strengths using strength-normalized loading to assess consistency of load transfer representation, hotspot locations, and relative stress-utilization trends. It is further extended to evaluate multiple joint configurations, including lap joints, T-joints, and hybrid joints, enabling a broader assessment of adhesive behavior across different loading conditions. A systematic design sensitivity analysis is performed to investigate the influence of key parameters such as adhesive bonding area, and geometric variations on joint stress utilization and hotspot sensitivity. The results highlight the capability of the approach to identify key design sensitivity parameters within the studied joint configurations and support informed decision-making during the concept development phase. The study demonstrates that the proposed methodology enables efficient screening of design concepts, can reduce reliance on high-fidelity material models during early-stage concept screening, and supports reduced evaluation turnaround time. This work establishes a practical right-level modeling strategy that complements advanced simulation techniques by enabling faster iterations, reduced computational effort, and compatibility with existing automotive CAE workflows.
Sugumar, Mohanraj, Bhat, Sadashiv
A Surrogate Modeling Approach for the Weight Optimization of a Two Wheeler Chassis with PSD Based Durability Validation2026-28-0207To be published on 11/05/2026
As the global automotive industry pivots toward sustainable mobility, reducing vehicle curb weight remains a paramount objective for enhancing energy efficiency and range. This paper presents a robust and comprehensive methodology for the light weighting of a two-wheeler chassis through the integration of Design of Experiments based optimization and Power Spectral Density durability analysis. The study begins by establishing a baseline durability profile for the vehicle using frequency-domain durability simulations under random vibrational loading, ensuring a benchmark for structural integrity. To explore the design space effectively, frame tube thicknesses are parameterized, and a Design of Experiments design matrix is constructed using Modified Extensible Lattice Sequence technique. This design matrix is solved, and a response matrix is built. These two matrices are combined, and appropriate curve-fitting techniques are applied to construct a Reduced Order Model. This mathematical surrogate model enables the rapid evaluation of the frame’s response under varying frame thickness combinations, thereby avoiding computationally expensive traditional Finite Element Analysis iterations. The optimum combination of frame tube thicknesses is identified using the Global Response Surface Method. This configuration minimizes the overall mass of the vehicle while strictly maintaining predefined durability and safety targets. The final optimized frame is evaluated by a secondary Power Spectral Density durability analysis to validate the accuracy of the surrogate model’s predictions. The integration of this metamodel-based design approach results in a significant reduction in structural weight. Ultimately, this framework accelerates the design cycle by developing a lightweight, cost-effective, and durable structure, thereby minimizing the overall time-to-market for sustainable automotive products.
Chettiar, Alfred Selvamani, V., Vinothkumar, S., Deepak, Praveen, Kannan
A Physics Artificial Intelligence Framework for Durability Analysis of Motorcycle Brake Pedal2026-28-0188To be published on 11/05/2026
The brake pedal assembly is a critical load bearing automotive component that must perform reliably under severe service conditions, especially during emergency braking events. Structural assessment is commonly carried out using computationally intensive non-linear finite element analysis, which can limit rapid design iterations during early product development. This study presents a physics informed surrogate modelling framework for predicting displacement and von Mises stress responses of a brake pedal directly from unstructured three dimensional mesh geometry. A structured dataset was created through parametric morphing and a Design of Experiments approach by varying pedal arm angle, pedal moment length, flange/web thickness, and combined geometric conditions. The simulation database was developed using conventional solvers and the generated cases were used to train Physics Artificial Intelligence based on the Transformer Neural Simulator architecture. The trained model showed close agreement with conventional solver results for both peak response values and contour distributions. For representative testing cases, predicted displacement and stress responses remained within acceptable engineering tolerance compared with the finite element baseline. After training, the PhysicsAI workflow reduced the prediction turnaround time compared with the conventional solver workflow. The results suggest that Design of Experiments driven datasets combined with geometric deep learning can provide an efficient workflow for rapid brake pedal structural assessment during concept development and iterative design studies.
N., Rengarajan, Praveen, Kannan, Philip Thomas, Febin
Option for Introducing Vehicle Crashlytics with SOVD2026-28-0215To be published on 11/05/2026
Automotive E/E architecture is getting transition from distributed ECUs to high performing Zonal architectures. Traditional diagnostic methodologies based on Unified Diagnostic Services (UDS/ISO 14229) is being followed for providing failure analysis. It is difficult when debugging complex software and to find issues where lies exactly at modular level called “stack trace”. This could provide solutions for software bugs, failures or even container-level crashes in Software-Defined Vehicle (SDV). This paper provides a remote diagnostic framework that makes use of the ISO 17978 (SOVD) standard to visualize a “Vehicle-wide Crashlytics” as analogous to modern mobile application analytics with Firebase SDK. Also, the comparison of failure management with traditional diagnostic method to handling fault management in remote framework with both ISO 17978 (SOVD) and ISO 20077/20078 (Remote Diagnostic) is made. It also speaks how methodology can be formed for software logs capturing and traces execution triggered by function software level anomalies. Also, the constrains such as V2X bandwidth and data is monitored and controlled with Diagnostic Saliency Algorithm that helps to determine only relevant telemetry is transmitted via 5G or V2N. This framework proposal is checked with a simple application failure scenario, which leads to the reduction in “Mean Time to Repair” (MTTR) for software bugs. The reduction in MTTR is achieved since actionable root cause of software issue is shared directly from vehicle’s run-time environment. Future development of vehicle functions (SaaS) is expected to be integrated with SOVD and cloud-native observability patterns. This is much needed towards the scalable and quick development and deployment of applications into Vehicle as Digital Hub.
Kalirajan, Vignesh, Annamalai, Viswachandar, Balakrishnan, Arun Shankar
Investigation on Dynamic Expansion Response and Structural Safety Assessment of Typical Polygonal Fabric Gasbags2026-99-233710/30/2026
The geometric structure of a gas bag has a profound effect on its effectiveness in transferring energy and reliability of its structure when rapidly expanding. Even though there have been considerable improvements in high-performance composite materials such as Kevlar, the transient dynamic reaction and possible rupture mechanisms associated with particular polygon shapes have been greatly unexplored. In this paper, we focus on conventional polygonal gas bags, and perform extensive inflation tests using high-accuracy transient data recorders to monitor the development of the dynamic pressure. In order to overcome the limitations of physical testing, a highly non-linear dynamic finite element model - based upon the application of the *MAT_FABRIC material model in LS-DYNA - is used. This technique allows the systematic examination of how changes in geometric parameters affect the performance of payload dispersion and membrane stress distribution. The results indicate unique ways of deploying depending on their geometry. The circular gas bags are capable of obtaining a velocity of the payload of 4.23 m/s but have high stress concentrations of up to 426 MPa due to the lack of geometric boundaries which make them prone to tearing failure. Rectangular gas bags with very large aspect ratios will experience a tubular expansion deformation that causes energy transfer efficiency to plummet; the release speed of these types of gas bags falls below 2.77 m/s. Ultimately, the optimal structural design is a regular hexagonal shape that optimally balances work performance and structural safety. It can keep good levels of energy transfer efficiency with a final release velocity of 4.12 m/s. Moreover, it successfully stops the continuous hoop tension and reduces the peak stress by around 46 percent through straight edge boundary constraints. This complete research offers an adequate scientific foundation to the anti-burst design of sophisticated polygonal gas bags.
Wang, Jinwei, Shi, Lv, Hou, Jingjing, Li, Yilong, Li, Dailin, Liu, Zhiyi
Process and Performance Optimization Design of Metal-3D Braided Composite Joint Structures2026-99-234910/30/2026
The sleeve is the key assembly part of the high-speed internal electric spindle support of the high-precision CNC internal and external grinder, and its machining accuracy directly affects the running state of the spindle. The purpose of this study is to adjust the structural process to improve its bearing capacity in all directions. Combined with the refined mechanical simulation of 3D braided composites, the influence mechanism of joint thickness and surface pretreatment on the connection performance is studied from the aspects of stress distribution characteristics and damage failure mechanism. The overall performance analysis and design of the metal-composite sleeve are carried out. Experiments show that the thickness of the joint has a great influence on its bearing performance, and the effectiveness of the proposed pretreatment process is verified, which provides a reliable technical path for the realization of high-performance metal-3D braided composite connection structure.
Wang, Zijun, Chi, Xinfu, Li, Chengen, Li, Dinghua
Research on Dynamic Response and Impact Resistance of Airport Pavement Made of Gradient Carbon Fiber Reinforced Composites2026-99-234610/30/2026
Aiming at the engineering problems of easy cracking and insufficient impact resistance of traditional airport concrete pavement under complex dynamic loads, this paper studies the application of functionally graded carbon fiber reinforced polymer ( FG-CFRP) in airport pavement. Based on the modified Halpin-Tsai model and the first-order shear deformation theory, the dynamic finite element control equation is established. Based on the COMSOL Multiphysics platform, the modal characteristics and transient impact response analysis are carried out, and the dynamic response of the aircraft under moving wheel load is simulated. The static bending test of FG- CFRP plate samples made in the laboratory was carried out, and the numerical model was calibrated. The relative error of test-simulation was controlled within 5 %, which verified the accuracy of predicting static response. The carbon fiber volume fraction gradient curve was parametrically studied to reveal the sensitivity of the gradient to impact and stress reduction. The key engineering factors such as long-term environmental durability and life cycle cost of FG-CFRP pavement are discussed, and targeted improvement and optimization strategies are proposed. The results show that the fundamental frequency of the gradient plate is about 1.93 % lower than that of the homogeneous plate, and the vibration mode characteristics are highly consistent. The peak displacement of transient impact is reduced by 9.4 %. Under the action of moving wheel load, the peak Von Mises stress decreased from 5.32 MPa to 2.15 MPa, with a decrease of 60 %. The gradient design realizes efficient load diffusion and impact hardening through the surface high modulus layer, and the gradual change of interlayer wave impedance promotes the dissipation of stress wave, which significantly improves the safety reserve and dynamic stability of pavement structure. This study verifies the application feasibility of FG-CFRP in high-performance airport pavement engineering, and provides innovative material solutions and theoretical support for the development of a new generation of long-life and high-reliability airport pavement.
Li, Zhenyue
Evaluation of Low-Altitude Aircraft Operation Status Based on Fuzzy Reasoning and D-S Evidence Theory2026-99-230210/30/2026
To provide auxiliary verification for the technical evaluation of low-altitude aircraft operation status and address the limited effectiveness of uncertainty handling, this paper proposes an evaluation method based on fuzzy reasoning and D-S evidence theory. Firstly, the relevant knowledge of D-S evidence theory and fuzzy reasoning is introduced. Secondly, the aircraft operation status levels are constructed, and an intuitionistic fuzzy reasoning system is established based on Gaussian membership functions. The initial basic probability assignments (BPA) are obtained through defuzzification using the intuitionistic fuzzy centroid method. The conflict between evidences is measured by the weighted average of Jensen-Shannon (J-S) evidence distance and conflict coefficient k, followed by multi-cycle discount correction and fusion. Finally, experimental verification proves the rationality of the proposed method, which can provide a reference for related research.
Niu, Xiaowei, Shen, Di, Yu, Fuping, Guo, Yiduo, An, Xiangyu, Xuan, Haoli
Study on Fatigue Crack Growth of Sandwich Composite Panels with Opening2026-99-232710/30/2026
Owing to their remarkable specific strength and stiffness, coupled with superior impact resistance and thermal/acoustic insulation, sandwich composite panels are extensively utilized in marine, aerospace, and transportation sectors. In contrast to conventional monolithic structures, the multi-layered architecture of sandwich panels introduces complex fatigue failure mechanisms that are not yet fully understood. This study experimentally investigates a U-shaped core sandwich panel featuring a rectangular hatch opening under uniaxial tensile fatigue loading, aiming to characterize its crack initiation and propagation behavior. Subsequently, a custom three- dimensional fatigue crack growth simulation code is developed, grounded in linear elastic fracture mechanics and the extended finite element method (XFEM). A comparison with experimental measurements reveals that the maximum relative error between the predicted and observed fatigue life is 15.7%. The findings offer a theoretical foundation and a technical reference for the fatigue design and life assessment of such sandwich structures.
Chen, Siyu, Xu, Yunze, Liu, Gang
Research on Failure Mode Verification of Civil Aircraft Based on Reliability Data2026-99-231410/30/2026
To verify the accuracy and completeness of FMEA and FTA, this paper proposes a comprehensive failure mode verification method based on reliability data. This method collects multi-source reliability data and identify actual failure modes occurring in equipment during operation firstly, thereby compiling a list of actual failure modes. Next, this list is compared with the FMEA report from the design phase to verify the failure modes themselves. A deeper qualitative verification of the causes, mechanisms, and impacts of the failures is then conducted, leading to the correction and updating of the FTA logical structure to ensure consistency between the safety analysis model and the actual failure logic.Building upon the qualitative verification, the method proceeds to quantitative verification. A comparative analysis is conducted between the actual failure rates of the failure modes and the estimated values in the FMEA. Using the validated failure rate data, the probability of the FTA top event is recalculated and compared against the safety target probability specified in airworthiness regulations to achieve quantitative verification of the failure condition probability. As a core innovation and enhancement of the method, this paper introduces sensitivity analysis. Through tools such as calculating Birnbaum importance and plotting tornado plots, it quantitatively assesses the sensitivity and impact of uncertainties in the failure rate parameters of each bottom event on the probability of failure condition, thereby identifying the key failure modes. The research findings provide theoretical support and a practical engineering foundation for evaluating the actual reliability level of the aircraft fleet, improving the accuracy of safety analysis during the design phase, and guiding preventive maintenance and design improvements.
Han, Bing, Li, Haiwei
High-Temperature Sealed Disc Spring Compression Load-Holding Performance Testing and Material Parameter Calibration2026-99-232210/30/2026
In high-temperature sealed assemblies, disc springs provide sustained preload to suppress leakage. However, at elevated temperatures, material creep and stress relaxation may reduce elastic recovery and accumulate permanent deformation, thereby degrading sealing reliability. Because the material properties of commercially procured disc springs vary with suppliers and production batches, they are difficult to apply directly in the analysis of high-temperature components. This study investigates Inconel-800 disc springs. Sustained compression tests were conducted at 600–900 °C to systematically obtain load–time responses and the temperature dependence of permanent deformation. The results show that, as temperature increases, the rate of force decay accelerates significantly and the residual load decreases markedly. At 800 °C and 900 °C, the permanent deformation approaches the compression limit, indicating that time-dependent material deformation becomes the dominant factor. Based on these results, a finite-element model consistent with the test conditions was established. Material creep parameters were calibrated from component-level compression data by minimizing the discrepancy between simulated and experimental curves. The identified parameters were validated under different temperature conditions and loading cases. The simulation results agree well overall with the experiments, and the errors remain within engineeringly acceptable limits. This study provides baseline data and an engineering method for obtaining material parameters to support material selection and structural optimization of high-temperature sealed disc springs.
Yang, Yuyao, Cai, Xianhui
Multiscale MD-FEA Investigation on Large-Deformation Mechanical Behaviors of Hydrogels Modified with Ionic Liquids2026-99-230610/30/2026
Hydrogels incorporated with ionic liquids (ILs) demonstrate broad application prospects in the fields of wearable flexible sensors and human physiological monitoring. Nevertheless, the intrinsic correlation between microscale intermolecular interactions and macroscale nonlinear mechanical responses under large tensile deformation has not yet been fully clarified. In this work, a coupled numerical framework integrating molecular dynamics (MD) and finite element method (FEM) was adopted to explore the mechanical behaviors of poly(acrylic acid)/N,N’-bis(acryloyl)cystamine/ionic liquid (PAA/NB/IL) hydrogels under large deformation. A series of molecular models with graded IL doping levels were established to characterize the structural rearrangement and energy change characteristics during uniaxial tension. Numerical results from MD simulations reveal that the addition of ILs strengthens non-covalent interactions such as van der Waals forces and electrostatic attractions, and further drives covalent bond stretching and angular deformation to bear external loads under high-strain conditions. Meanwhile, a generalized Maxwell constitutive model, whose Prony-series parameters were fitted by experimental stress-relaxation data, was introduced into FEM numerical calculations. The simulated quasi-static tensile properties are highly consistent with experimental measurements. Comprehensive analysis demonstrates that the mechanical enhancement induced by ILs stems from the synergistic effect of covalent structural deformation and the reconstruction of non-covalent interactions, which offers valuable theoretical guidance for the development of hydrogel materials applied in large-deformation engineering scenarios.
Li, Sitan
The Statistical Analysis of Commercial Flight Data for Defining the Payload-Range Performance of the Aircraft Conceptual Scheme Design2026-99-231110/30/2026
This research focuses on the application of statistical analysis of commercial flight data in aircraft conceptual scheme design. The payload-range performance of the commercial aircraft should be defined and analyzed by utilizing statistical methods during the conceptual scheme design, as it determines marketability. Statistical analysis is deemed essential due to the complexity and specific requirements of conceptual design. The research paradigm established in this study involves collecting and analyzing flight data of competitor aircrafts operating in the target market, using the OAG database as a foundation. This data is then utilized to build statistical models and conduct trade-off analysis. The results reveal that while the competitor aircraft has good route coverage, the conceptual scheme designed exhibits better payload-range performance, with a potential reduction in structural weight and better aerodynamic performance. Aircraft designers should take the imbalance in flight data between different airports, which is revealed by the application of statistical analysis, into consideration in conceptual scheme design. This study fills the gap between “market data-driven” and “performance modeling” in aircraft conceptual design, providing a reusable paradigm for subsequent research. The process of aircraft scheme design is a multi-disciplinary optimization process. When the range under certain payload of the concept scheme is already sufficient to cover the target market, how to conduct iterative design in conjunction with other indicators such as economy and commercial range is a problem worthy of in-depth study.
Gu, Xiangying, Cheng, Zhiyu
Experimental Investigation on Flame Characteristic for Civil Aircraft Propulsion System Fireproof Test2026-99-231710/30/2026
In order to meet the airworthiness regulations, the civil aircraft propulsion system needs to carry out the necessary fireproof test. The measurement of the flame temperature is very essential for the certification and validation of fire protection thermal simulation models, and also helps to establish the empirical understanding of complex flame behaviors. In order to investigate the influence of the specimen on the flame temperature distribution, the two dimensional flame temperature distribution of the free burning test and several fireproof tests, including titanium panel fireproof tests, stainless steel panel fireproof tests, composite laminated panel fireproof tests, honeycomb sandwich panel fireproof tests, were experimental investigated in this paper. The test results shows that panel specimens significantly alter the temperature distribution of the standard flame and interfere with radiation heat transfer measured by thermocouple, resulting in a significant reduction in the temperature gradient of the flame and a obvious increment in the temperature of the surrounding non-flame-impinging zone. The central flame-impinging zone exhibits minimal temperature variation compared to the baseline flame at 1093 °C, with temperature elevation restricted to approximately 40 °C (within 4% deviation). Both metal panels and composite panels demonstrate consistent trends in affecting flame temperature distribution. However, compared to metal panels, composite materials induce smaller temperature gradients and more uniform temperature fields. Multiple burning tests on same panel progressively intensify the oxidation on the specimen surface, leading to a gradual decrement in flame temperature. Especially, this temperature reduction becomes statistically significant as early as the second time burning test.
Chen, Fang, Guo, Junxian, Peng, Sen
Pressure-Type Anchors in Weathered Rock for Transportation Infrastructure2026-99-236610/30/2026
In transportation infrastructure constructed in mountainous and rocky regions, such as highway slopes, railway cuttings, bridge abutments, and tunnel portal slopes, uplift-resistant anchorage systems are widely required to ensure structural stability and service safety. To support anchorage design in such conditions, this study investigates the uplift bearing behavior, load-transfer mechanism, and key design parameters of pressure-type rock anchors in weathered rock strata through a combined field-and-numerical approach. A rock anchorage project in mountainous terrain in Shaoxing, Zhejiang Province, China, was adopted as the prototype case, and two representative anchors installed in strongly weathered and moderately weathered tuffaceous sandstone were selected for field pullout testing and three- dimensional finite element simulation. The failure characteristics, uplift load–displacement response, axial strain distribution of the grout body, and the effects of anchorage length and rock-socketed length were systematically examined. The results show that pressure- type rock anchors exhibit favorable uplift resistance in weathered rock strata. No overall pullout failure occurred during the tests, and no obvious cracking or significant ground deformation was observed. The uplift load–displacement curves exhibited a pronounced steep- transition pattern, and the axial strain of the grout body was mainly concentrated near the bearing plate and attenuated upward along the anchorage section. When the anchorage section extended into the moderately weathered rock stratum, strain attenuation became faster and interfacial load transfer became more efficient. Parametric analysis further indicates that the uplift bearing capacity increases approximately linearly with anchorage length before a critical anchorage length is reached, whereas the increase becomes much less significant beyond about 8 m. In addition, a proper increase in rock- socketed length can significantly improve the uplift bearing capacity. The findings provide useful reference for the design and optimization of uplift-resistant anchorage systems in transportation infrastructure founded in layered weathered rock.
Feng, Bing, Li, Panfeng, Zhou, Ji’an, Lv, Qing
Multi-Objective Robust Optimization Method for CFRP Laminates Based on a Dual Dimensionality Reduction Strategy2026-99-234510/30/2026
This paper presents a robust multi-objective optimization method for a symmetric carbon fiber reinforced polymer laminate under uniaxial tension. The study focuses on the combined design problem of failure strength and failure displacement under material uncertainty. A fast structural response model is built by using classical laminate theory, and the Hashin failure criterion is used to predict the first-failure state of the laminate. The model takes uncertain material properties into account, so it can describe the effect of random changes in stiffness and strength on the structural response. To reduce the computational cost, the method uses a dual reduction strategy. The first step reduces the parameter space. Latin hypercube sampling is used to generate random samples, and Spearman correlation analysis is used to identify the key uncertain parameters. The second step reduces the sample space. K-means clustering is used to group the reduced samples and generate representative scenarios. Based on these scenarios, the study defines robust failure strength and robust failure displacement by combining the mean value and the standard deviation of the responses. A multi-objective genetic algorithm is then used to search for the Pareto solution set. After that, two single-objective optimizations are carried out to obtain the best strength and the best displacement, and these two values are used to form an ideal point. The two objectives are normalized, and the Pareto point with the smallest distance to the ideal point is selected as the final design. The results show that the proposed method can capture the positive relation between failure strength and failure displacement, identify the main uncertain parameters that control the response change, and provide a balanced design with good strength and displacement performance under uncertainty.
Wang, Yaoqiang
Optimal Design and Analysis of Axial Gradient Buffer Structure Based on 2-DOF Flexible Hinge2026-99-235610/30/2026
Conventional unmanned aerial vehicle (UAV) landing gears exhibit deficiencies including severe stress concentration, insufficient cushioning deformation capacity, and inadequate fatigue resistance during the landing impact process, which makes it difficult for them to meet the technical requirements of high-overload buffering conditions. Based on the elastic mechanics theory of the cantilever beam and using a two-degree-of-freedom (2-DOF) flexible hinge as the base, an analytical equation for the rotational stiffness of a 2-DOF flexible hinge in the direction was established. By combining the response surface methodology (RSM), the influence laws of key geometric parameters on the mechanical performance of the hinge were systematically analyzed. The optimal value range of the core structural parameters of this 2-DOF flexible hinge was determined as follows: the radius of the rounded corner R = 2.5 - 3.5 mm, the minimum thickness t = 1.5 - 2 mm, and the width a = 20 - 30 mm. Building on this foundation, an axial gradient columnar buffering structure incorporated with flexible hinges is proposed for the application of UAV landing buffering. Cross-verification between theoretical analysis and finite element simulation shows that the relative errors of structural deformation in each loading direction are merely 1.76% and 1.9%, respectively, which validates the accuracy and reliability of the proposed theoretical model. Parametric studies incorporating transient dynamic analyses under impact loading conditions elucidate that the maximal deflection and peak stress characteristics of the flexible hinge are positively correlated with the drop height and the structural connection angle. The outcomes of this research contribute a foundational theoretical framework and actionable technical support for facilitating the lightweight design and performance enhancement of flexible landing gears within high-load buffering systems of UAV.
Zhang, Qixian, Zhou, Jinhua, Zhang, Dongsheng, Wang, Jiashuai, Yang, Zengliang, Zhang, Yingxue
Modeling and Supervisory Reconfiguration of a Dissimilar Redundant Flight Control System under Common-Mode Failures2026-99-235810/30/2026
Common mode failures may simultaneously impair multiple redundant channels, threatening redundancy independence and flight safety. Existing studies have mainly focused on safety architecture analysis, analytical redundancy design, and reliability allocation, while systematic research on failure propagation in closed loop flight control systems remains limited. In particular, insufficient attention has been given to channel degradation divergence and unified modeling for online reconfiguration. To address these issues, a unified framework for common mode failure propagation is established. On this basis, a dissimilar redundant control architecture is constructed by combining a state feedback channel and a proportional integral derivative channel. A supervisory reconfiguration strategy is developed by integrating multi source degradation assessment, credibility based control allocation, and hysteresis based hybrid mode switching. Simulation results from a longitudinal closed loop model show that the proposed strategy captures the divergent degradation of heterogeneous channels and triggers coordinated reconfiguration during fault propagation. As degradation increases, the control system transitions from baseline operation to weighted fusion and then to a safety preserving degraded mode, with control authority shifted toward the more credible channel. Compared with the case without reconfiguration, the proposed method yields smoother control responses and improved tracking performance.
Peng, Cheng, Guo, Jianwei, Xu, Youping
Machining Deformation Control of 5A06 Aluminum Alloy Aircraft Skin2026-99-234310/30/2026
Aircraft skin is a critical external structural component that provides aerodynamic profile, sustains and transfers aerodynamic loads, and protects internal systems from harsh environments. High machining precision is essential to ensure assembly accuracy and flight performance. However, large scale thin walled skin components are prone to significant machining deformation due to their low stiffness. Traditional deformation control methods, which relying heavily on experiments and full-process simulations, are inefficient and costly for industrial applications. To address this issue, this paper establishes a correlation model among machining parameters, residual stress, equivalent bending moment, and machining deformation. Single-factor experiments are conducted to reveal the effects of spindle speed, feed rate, and cutting depth on surface residual stress. Based on plate-shell theory, the relationship between residual stress and equivalent bending moment is established. The quantitative relationship between equivalent bending moment and machining deformation of the skin is obtained via finite element simulation modeling. Finally, a deformation control strategy for the skin during finish machining is established by optimizing the cutting depth. Experimental results show that after parameter optimization, the overall machining deformation of the 5A06 aluminum alloy skin is reduced by 81%, and the maximum deformation is controlled within 0.17 mm, which fully meets the aeronautical assembly requirements. This work provides an efficient and low-cost process optimization strategy for the machining deformation control of large weak-stiffness thin-walled components.
Chen, Shu-Guang, Wang, Jing-Kai, Wu, Chao, Wang, Tian-Rui, Yuan, Hao
Numerical Diagnosis and Evidence-Chain Analysis of the Non-Sustained Ignition Process in a Hydrogen/Air Turbulent Diffusion Flame under an LES–PaSR Framework2026-99-232410/30/2026
This paper addresses non-sustained ignition in numerical simulations of hydrogen/air turbulent diffusion flames. A reproducible LES-PaSR case study was constructed on the OpenFOAM platform, and a box-shaped ignition core was applied using setFields. The work is positioned as a numerical diagnostic and benchmark workflow, not as an experimentally validated prediction of a stable flame. The evidence chain, consisting of three diagnostic indicators - the domain-wide maximum temperature, centerline statistics along the ignition axis, and slices at critical moments - tracks the complete ignition-peak decay process. Calculations show that the domain-wide maximum temperature (Tmax) reaches a peak of 2852.5 K at t = 3.13×10^–4 s and then rapidly drops to 956.4 K at t = 0.0015 s. The duration of Tmax ≥ 2000 K is 0.818 ms, while Tmax ≥ 1000 K lasts for 1.388 ms. Comparison between the centerline and cross-sections further indicates that the high-temperature OH reaction zone is mainly limited to the ignition core and its downstream neighboring region. Under the combined effects of convective transport and turbulent mixing, this region rapidly cools and dissipates, failing to form a stable self-sustained exothermic structure. Finally, three physically plausible explanatory pathways and corresponding sensitivity-case designs are proposed, providing a practical basis for subsequent parameter scanning, mechanism replacement, and future comparison with experimental or literature-reported data. A supplementary sensitivity case with the ignition-kernel temperature increased from 2200 K to 2400 K exhibits a higher peak temperature and delayed threshold crossings, yet the reacting structure remains non-sustained within the simulated time window. Tmax ≥ 2000KTmax ≥ 1000K
Liu, Jiaqi, He, Jiandong, Yuan, Youquan, Duan, Yu
Vehicle Front-End Architecture as a Driving Factor in Occupant Injury Risk during Vehicle-Guardrail Impacts: A Hybrid III Dummy-Based Numerical Study09-14-03-001110/26/2026
The performance of Midwest Guardrail Systems (MGS) during impact with electric vehicles (EVs) has raised concerns with recent observations demonstrating a higher susceptibility to rail failures. These failures are hypothesized to be related to EV-specific design, including variations in front-end geometry and inertial properties. Current guardrail evaluations rely on two vehicle types, a small car and a pickup truck, potentially overlooking sensitivity to front-end geometry variations, potentially leading to unanticipated impact responses. Additionally, the extent to which geometry influences occupant injury risks and trends during vehicle-guardrail impact remains insufficiently investigated. The objective of this study is to quantify the influence of vehicle front-end geometry on occupant injury risk during vehicle-guardrail impacts using finite element (FE) simulations. Occupant injury risk is analyzed using Hybrid III anthropomorphic test device (ATD)-based injury criteria and flail space-based vehicle severity metrics to identify trends and relationship between geometric variations and injury probability. Front-end geometry data from the Insurance Institute of Highway Safety (IIHS) were analyzed to identify parameters and ranges associated with adverse MGS performance. Based on vehicle alignment with the W-beam, hood leading-edge height, hood leading-edge angle, and bumper height were morphed on the Toyota Yaris model. A Latin Hypercube Design of Experiments generated 13 unique configurations for detailed analysis. Full-scale Manual for Assessing Safety Hardware (MASH) Test Level 3 FE simulations were then performed between the morphed vehicle models integrated with Hybrid III 50th percentile male ATD and MGS guardrail. Occupant injury metrics were evaluated alongside vehicle-based crash severity measures. All vehicle geometries fulfilled MASH criteria with occupant impact velocity (OIV) and occupant ride down acceleration (ORA) within tolerance with no rail rupture. Simulations suggest that probability of serious full-body injury (AIS3+) was estimated at 17.11% while probability of severe neck injury was the highest at 15.64% among the component level probabilities. While front-end geometry influences occupant kinematics, resulting injury metrics and vehicle-guardrail interaction characteristics, it does not independently govern occupant injury risk. Although, the MASH tests have the same outcome as the geometries studied, the changes in EV inertial properties could interact with the geometry leading to barrier failure, which will be explored in future work.
Bubna, Maheep, Buckland, Elijah, Riexinger, Luke E., Untaroiu, Costin
Failure Tolerance of Male and Female Lower Extremities under Pure Bending and Combined Loading Modes09-14-03-001410/26/2026
Lower extremity injuries remain common in frontal motor vehicle crashes, with evidence suggesting increased vulnerability among female occupants. While prior research has established failure tolerances of midsize male lower extremities under bending and combined loading conditions, limited data exist for small female specimens. This study aimed to characterize and compare the failure tolerance of small female and midsize male postmortem human subject lower leg specimens under combined axial compression and posterior–anterior (PA) bending using consistent boundary conditions. Twenty-four lower leg specimens, including the tibia, fibula, and surrounding soft tissue, were obtained from 50th percentile male (n = 12) and 5th percentile female (n = 12) donors. Dynamic tests to failure were conducted at 1.5 m/s using a test apparatus designed to apply three-point bending in the PA direction with varying levels of superimposed axial compression (0, 2, 4, and 6 kN). When paired left and right legs from the same donor were available, they were assigned to different loading conditions to assess the influence of axial compression magnitude. Fracture timing was determined using force, strain, acoustic emission data, high-speed video, and post-test CT imaging. Results indicated that 18 specimens fractured at the tibial shaft, 5 near the potting interface, and 1 did not fracture. Under pure bending, mean failure moments were 255 ± 57 Nm for males and 156 ± 15 Nm for females. At 6 kN axial compression, failure moments decreased to 195 Nm and 72 Nm for male and female specimens, respectively. Among matched pairs, increasing axial load resulted in an average 21% reduction in bending moment at failure. These findings demonstrate reduced failure tolerance with increasing axial compression and highlight sex-based differences. The data provide a foundation for developing improved injury risk functions and scaling methodologies for diverse occupant populations as well as informing anthropometric test devices and human body models.
Carbon, Maria, Nazari, Isabella, Edinger, Timothy, Shin, Jeesoo, Hanggi, Connor, Kerrigan, Jason, Forman, Jason
Deriving Representative Head-Impact Profiles in Contact Sports: A Shape-Based Clustering Approach09-14-03-001810/26/2026
Instrumented mouthguards (iMGs) capture thousands of head acceleration events in contact sports, though processing the data from these into meaningful information remains a challenge. To bridge this gap, we developed a principled clustering pipeline to extract concise kinematics that are representative of real-world impact scenarios. We evaluated six multivariate time-series distance metrics using k-medoids clustering and a dataset of 5149 impacts across three sports: mixed martial arts, boxing, and rugby union. Model performance was evaluated using a Coverage Quality Score (CQS), prioritizing reconstruction coverage and cluster separation. Joint Shape-Based Distance (SBD) significantly outperformed all other metrics, including Dynamic Time Warping (DTW), driven primarily by superior reconstruction coverage. The methodology yielded two complementary archetypes. First, a 36-impact canonical set provides distinct medoids representing core kinematic mechanisms; however, distributional checks confirm these medoids alone do not reproduce cohort severity distributions. To resolve this, we generated a second, extended impact set sampled proportionally within clusters along specific severity axes (e.g., peak acceleration and whole-brain strain). This targeted sampling recovers full severity-distribution fidelity using fewer than 150 impacts per dataset. Ultimately, this framework successfully distills large iMG datasets into manageable cohorts providing a scalable solution for assessment, experimental targets, and finite element simulations.
Koumantou, Eleni, Chan, Emily Yik Kwan, Jones, Chris M., Austin, Kieran, Loosemore, Mike, Ghajari, Mazdak
Permutation Test as a Novel Robust Statistical Approach for Gauging a Change in the Rate of Stochastic Preignition03-19-06-002710/23/2026
Multiple statistical methods were evaluated to determine the most suitable approach to gauge whether a gasoline deposit control additive (DCA) causes a statistically significant increase in rate of stochastic preignition (SPI) in a direct injection, turbocharged, spark ignition (SI) engine. SPI is an abnormal combustion phenomenon observed at highly boosted conditions in turbocharged SI engines. The high in-cylinder pressures observed during an SPI event have the potential to cause physical damage to the engine. The SPI rate is a function of engine operating conditions as well as fuel and oil properties. DCAs are commonly used in commercial gasoline at very low concentrations (20–200 ppm m/m) to prevent the buildup of carbon deposits in the combustion chambers and fuel systems. Since the effect of DCAs on SPI rate can be subtle and the data inherently stochastic, a controlled engine test methodology with robust statistical analysis is required to conclusively determine whether a gasoline detergent increases or mitigates the rate of SPI occurrence. Therefore, the TOP TIER™ Standardized Dynamometer Test Method to Evaluate Additized Detergent Gasoline for Stochastic Pre-Ignition (hereafter referred to as the TOP TIER™ Additive SPI Test Protocol) described herein is designed to gauge whether a gasoline additive causes a statistically significant change in the SPI rate at a controlled operating condition as compared to unadditized gasoline. This study focuses on the statistical analysis portion of the test protocol. Multiple statistical approaches were evaluated using data from the TOP TIER™ Additive SPI Test Protocol to determine the most appropriate and robust approach for detecting a statistically significant difference in SPI rates. The analysis included the two-sample (unpaired) t-test, linear mixed-effects models (LMMs), the Mann–Whitney U test, and the permutation test. The results indicated a combination of permutation paired with Wilkinson’s method as the most suitable approach.
Gopujkar, Siddharth, Davis, Richard, Zhang, Kui, Reilly, Veronica, Studzinski, William
Acceleration Factor Derivation Using Test Bed and Field Failure Data of Automotive Engines2026-01-507710/19/2026
Reliability testing and validation are essential parts of any automobile development project. These tests mainly depend on two things: how much of the product’s target life is covered and whether enough samples are tested to capture variations. Today, most projects face tight timelines and cost constraints, which makes it important to design an optimized reliability test plan. Accelerated testing is widely used to reduce testing time, but finding the correct acceleration factor for a complex system like an automotive engine is difficult. Existing studies describe methods such as simple stress tests, step-stress tests, and degradation tests to shorten test duration. However, these methods need more samples at different stress levels to establish a clear relationship between stress and life and then calculate the acceleration factor. While such models work well at the component level, they become much more complicated at the system level because different components can have different acceleration behaviors. In this paper, a method is proposed to derive the acceleration factor by correlating engine test bed data with field failure and usage data. Test engineers often use standard accelerated test cycles for engines used in similar applications. As a result, organizations usually have historical test data and field data from earlier, similar products. The proposed approach models accelerated test data and field failure data separately and then links them to determine the acceleration factor. This derived factor can later be used in future engine development projects to convert test cycles into equivalent field mileage coverage. Weibull analysis and regression techniques are used to support the case studies presented in this work.
Koulage, Dasharath Baliram, Mondal, Kanchan
An Electromagnetic-Thermal-Mechanical Coupling Model of the Three-Coil Wear Debris Sensors for Wide-Temperature and Kinematic Analysis2026-99-298510/19/2026
Real-time monitoring of metal wear debris in lubricating oil is crucial for accurately assessing aerospace equipment health and preventing catastrophic mechanical failures. Although three-coil differential inductive sensors are widely used due to their high sensitivity and robust structure, their detection performance inevitably degrades under the influence of multi-physics coupling effects inherent in complex industrial environments. Existing analytical models were primarily limited to single electromagnetic field simulations, failing to capture the intricate electromagnetic-thermal-mechanical interactions, which reduced analytical accuracy and hindered the optimization of sensor designs for extreme conditions. To overcome these challenges, this study established a comprehensive parametric simulation model to conduct a rigorous electromagnetic-thermal-mechanical coupled analysis of the differential sensor. Specifically, the research analyzed the underlying mechanisms by which wide temperature variations from -55 °C to 200 °C and specific debris kinematics exert varying degrees of influence on the induced voltage. Quantitative results indicated that both the isolated thermal and mechanical fields altered the voltage by a maximum of 0.649%, demonstrating that the sensor maintains a relatively stable response across extreme thermal gradients. Furthermore, the analysis revealed that increased debris velocity decreased the induced voltage amplitude due to the intensified motion-induced eddy current effects. Notably, in complex multiple debris scenarios, the strong dominant signals generated by ferromagnetic wear debris severely masked the weaker responses from non-ferromagnetic ones, indicating a substantial missed detection risk under mixed contamination conditions. Ultimately, this research successfully filled the critical simulation gap for evaluating these sensors under multi-physics coupling, providing robust theoretical guidance for their reliable deployment in complex industrial applications.
Shi, Xinfa, Duan, Chenhao, Zhen, Huanyi, Zhao, Yihe, Li, Min, Wang, Xiaozhang, Zhang, Qing, Li, Zhikang, Zhao, Libo
Research on 3D Cooperative Localization Method Based on Single-Auxiliary Node Maneuver Reconstruction2026-99-295910/19/2026
The demand for high-precision, real-time, and continuous localization in aerospace and low-altitude unmanned systems is steadily increasing. However, under weak GNSS availability, obstruction, and degraded ranging geometry, conventional 3D localization methods often suffer from insufficient observation constraints and severe accuracy degradation. To address observability degradation and localization error divergence in single-auxiliary-node systems, this paper proposes a 3D cooperative localization method based on maneuver reconfiguration of a single auxiliary node. Within a particle filtering framework, the method fuses target motion increments with single-shot range measurements to recursively estimate the target’s 3D position and heading. A geometric degradation metric and a radial-consistency metric are further constructed from the line-of-sight distribution and the alignment between target velocity and line of sight within a sliding window, enabling online detection of degraded observability. Once degradation is detected, the auxiliary node performs either lateral maneuvering or circular maneuvering to actively reshape the target–auxiliary relative geometry and restore observation diversity; meanwhile, the two metrics can also serve as feedback variables for adaptive maneuver adjustment. Comparative analyses are conducted under three modes, namely no reconfiguration, lateral maneuvering, and circular maneuvering, from the perspectives of 3D trajectory evolution, error time series, degradation-metric time series, and statistical error performance. Parameter sensitivity analyses and an additional nonlinear velocity-varying scenario are also included. Experimental results show that the no-reconfiguration mode exhibits clear error divergence under degraded conditions, whereas both maneuver-based strategies effectively reduce localization errors. Lateral maneuvering is more suitable for rapid recovery in the early degradation stage, while circular maneuvering achieves better overall accuracy and stronger post-degradation recovery capability. These results demonstrate the effectiveness and engineering feasibility of degradation-triggered active geometric reconfiguration for cooperative localization in low-configuration unmanned systems.
Wen, Yuanbin, Guo, Ziwen, Wu, Jian, Tu, Jiajun, Ma, Junteng, Wei, Jinyue, Zhou, Jihua
Multi-Objective Optimization Design Method for MEO Constellations Oriented towards LEO Constellation Coverage2026-99-293110/19/2026
Aiming at the challenges faced by Low Earth Orbit (LEO) constellations, such as difficulties in cross-shell communication, complexity in global management and control, and highly dynamic network topologies, this paper evaluates the introduction of a Medium Earth Orbit (MEO) constellation as a management layer to optimize the coordination and servicing of the underlying LEO constellation. The core contributions of this work are twofold. First, a coverage performance analysis method based on Voronoi diagrams and Delaunay triangulation is adopted to evaluate the coverage of MEO constellations over LEO constellations. Second, a four-dimensional multi-objective fitness function is constructed, comprehensively considering launch cost, communication delay, coverage performance, and space security. On this basis, a multi-objective genetic algorithm is employed to optimize the key parameters of a Walker-type MEO constellation, including orbital altitude, orbital plane count, and satellites per plane. Simulation results show that, under the condition of a LEO constellation altitude of 600 km, highly fit MEO configuration parameters are obtained. STK simulations verify that this configuration can achieve complete and dynamic coverage of the underlying LEO constellation. This study provides a systematic theoretical framework and an efficient algorithmic tool for solving the configuration optimization problem of the MEO layer in the "LEO-MEO" architecture.
Li, Ruiyao, Cui, Hutao
Analysis of Structural Stress Distribution and High-Temperature Performance of Load-Bearing Components in External Fire Scenarios of Steel-Structured Aircraft Models2026-99-297510/19/2026
This paper focuses on three typical external fire scenarios in aircraft firefighting and rescue training systems: engine fires, external fuselage skin fires, and nose landing gear fires. Unlike internal fires, these external fires expose the load-bearing steel structure directly to flame radiation and high-temperature environments, and the cooling methods and stress conditions associated with each fire scenario differ significantly. Based on the ABAQUS finite element platform, this study established a three-dimensional finite element model comprising an engine model, a water-cooled thermal insulation structure, and a fuselage support system, fully accounting for the actual structural layout and cooling protection measures of the training system. Material parameters were derived from measured performance data for Q355NH steel. The calculation results indicate that under the three types of external fire scenarios, the steel structure model maintained sufficient structural safety margins, with a minimum safety factor of 4.49 and a maximum of 9.43. This demonstrates that the current design meets the safety requirements for prolonged external fire training, providing a reliable mechanical basis for the safe operation of the ARFF training system.
Gao, Jianwei, Xu, Xiaodong, Huang, Min, Wang, Shumin, Shi, Yajie, Zhang, Yun
Analysis of MIMO Channel Characteristics under Large Spatio-Temporal Scales Based on Distribution Formula2026-99-296210/19/2026
In recent years, the global demand for high-rate, high-reliability, and wide-coverage wireless communications has continued to grow. Traditional satellite communications are faced with bottlenecks such as spectrum scarcity, insufficient single-satellite capacity, and poor adaptability to highly dynamic channels. Relying on the advantages of wide-area coverage and multi-satellite cooperation, low Earth orbit (LEO) satellite constellations can construct distributed satellite MIMO systems and form virtual large-scale antenna arrays, significantly improving system capacity and reliability. However, such systems are characterized by large spatiotemporal scales, and their channels exhibit strong heterogeneity in dimensions such as delay, frequency, and polarization, which are difficult to accurately evaluate using existing methods. To this end, this paper proposes a large-spatiotemporal-scale channel characteristic analysis method for distributed LEO satellite MIMO systems. A satellite-terrestrial full-link channel model is established, and quantitative characterization of multi-dimensional heterogeneity is realized based on statistical boundary analysis. Combined with the H3 global grid system, large-scale simulations are carried out to statistically analyze the distribution laws of delay differences, frequency offsets, and polarization angle deviations under different user configurations. The unique value of this analysis method lies in that its output results can be used to determine the upper limit of buffer capacity required for the receiver to complete coherent combining of multi-channel signals and the frequency offset correction range required for signal acquisition, provide a quantitative basis for optimizing satellite set selection, and offer a solid theoretical model and data support for performance evaluation and parameter design of multi-satellite cooperative systems.
Lu, Yi, Hou, Yucheng, Su, Xingyu, Yu, Xinyue, Jiang, Yanzi, Zhang, Jie, He, Wenxi
Coupled Vibration Analysis and Vibration Suppression for Spacecraft Multi-Panel Structures by Stiffness-Tunable Hinges2026-99-292410/19/2026
Spacecraft hinged-panel structures feature a multi-hinged configuration, substantial dimensions, low stiffness, and low damping, making them susceptible to sustaining low-frequency vibrations under multi-source excitations. To address this challenge, a novel stiffness-tunable hinge is proposed for low frequency vibration attenuation. Firstly, the hinge structural geometry and stiffness-tunable principle are presented. Subsequently, FEM models of multi-panel structures incorporating stiffness-tunable hinges are developed. The natural frequencies and mode shapes of the system are investigated. Simulation results indicated that the multi-panel structures reveal bending vibrations, torsional vibrations, and coupled bending–torsional vibrations of the hinge-panel structure. Finally, a microgravity experimental platform was developed, this experimental platform offers several advantages, including extremely low friction, unconstrained horizontal motion, a compact structure, a small footprint, and robust reliability. The gravitational load of the hinged-panel is nearly counterbalanced, thereby enabling its unrestricted, omnidirectional translation across the horizontal plane. The experimental results rigorously validated the link between hinge stiffness-tuning and system eigen-frequency migration. Furthermore, vibration suppression at low frequencies of 0.29 Hz (for three-panel) and 0.31 Hz (for four-panel) was realized using non-contact magnetic excitation. This approach achieved amplitude reductions of approximately 9.70 dB at 0.29 Hz and 12.53 dB at 0.31 Hz. These findings demonstrate a novel strategy for suppressing low-frequency vibrations in spacecraft structures.
Bi, Zhiwei, Lin, Hui, Hu, Wei, Wang, Yufeng, Zhang, Ce, Yu, Ruijiang
Nonlinear Dynamic Modeling and Active Control of a Large-Scale Two-Dimensional Planar Phased Array Antenna2026-99-290110/19/2026
A new method for dynamic modeling and active control of the large-scale, on-orbit assembled two-dimensional planar phased array antenna is proposed in this study. As the demand for high imaging accuracy and scanning coverage in satellite antennas increases, there is a shift toward larger apertures. However, large-scale phased array antennas face challenges such as low rigidity and significant nonlinear vibration during attitude maneuvers, which can degrade pointing accuracy and shape precision. To address these issues, this study proposes a dynamic modeling framework that incorporates geometric nonlinearity, employing finite element method (FEM) and a polynomial fitting-based model order reduction technique. A rigid-flexible coupling dynamic model is then developed based on the velocity variation principle. In control design, the paper integrates an active vibration control law using cable actuators with a computation torque algorithm based on control moment gyroscopes (CMGs) to design an integrated attitude-vibration control system. This system aims to suppress nonlinear vibration while achieving high-precision attitude control. Simulation results demonstrate the effectiveness of the proposed integrated control approach in both vibration suppression and attitude maneuvering, offering valuable insights for its practical application in future space missions.
Ma, Yihao, Liu, Xiang, Jin, Chaochen, Cai, Guoping
A Fuzzy Initiative-Yielding Mechanism for Space-Air-Ground Cooperative Communication2026-99-294110/19/2026
Space-Air-Ground Integrated Network (SAGIN) emerges as an appealing framework for implementing pervasive connections among diverse layers of space, air, and ground. The scheme relies on satellite deployment for extensive space coverage, Unmanned Aerial Vehicle (UAVs) acting as agile aerial relay stations, and ground robots for performing the final task assignment. Nevertheless, integrating heterogeneous nodes with varying mobility characteristics poses significant challenges: satellite operations are restricted by orbital dynamics, ground robot movements are restricted by topography constraints, whereas UAVs demonstrate flexibility and can establish line-of-sight connections with both satellites and ground robots. Current solutions usually adopt UAVs merely as passive relay nodes, causing inefficient adaptation mechanisms and excessive communication overhead when responding to dynamic perturbations or blocked connections. To overcome the aforementioned challenge, this paper proposes a novel fuzzy initiative-yielding coordination approach assigning two opposite behaviors. Initiative is assigned to UAVs in order to facilitate coordinated operations between layers, whereas Yielding refers to the adaptive response of satellites and ground robots. Contrary to previous research where behavioral assignments follow strict threshold rules, our fuzzy inference considers various parameters such as link quality, node state, and task priority, providing flexible behavioral assignments. Our predictive approach incorporates the Exponentially Weighted Moving Average (EWMA) technique to perform proactive failure avoidance. Based on five comprehensive experiments with 30 independent runs each, our approach obtains similar task completion rates (84.4%), but exhibits significantly lower communication overhead by up to 11 to 147 times compared to baseline methods. Furthermore, the proposed behavioral assignments avoid any coordination conflict (0 vs. 30.8), reduce completion time by 10.1%, maintain robust performance under 20% rule reduction, scale to 150-node mega-constellation scenarios, and exhibit higher fault tolerance under dynamic environments (81.5% vs. 79.8% of CBBA under high stress conditions).
Bao, Jie, Zhang, Yuping, Zhang, Ronghao
Fault Diagnosis Study of MCSECNN Model Integrating MK-MMD and DANN under Variable Operating Conditions2026-99-297210/19/2026
Bearing signals measured under different loads rarely keep the same waveform characteristics. Their amplitudes, spectral components, and statistical properties may change at the same time, which makes a classifier trained on one load condition less reliable on another. This study investigates a cross-load diagnosis method that trains a multi-scale convolutional squeeze-excitation network (MCSECNN) with multi-kernel maximum mean discrepancy (MK-MMD) and a domain-adversarial neural network (DANN). The convolutional branch captures vibration patterns over several temporal ranges, and the SE unit adjusts the importance of the channels generated by the parallel kernels. MK-MMD penalizes the distance between load-dependent feature distributions, whereas DANN weakens the load identity retained in the features. Tests using CWRU bearing data show that the integrated model gives better accuracy than the basic MCSECNN and the two single-adaptation variants. Ablation results indicate that the statistical and adversarial constraints contribute to transfer performance in different ways.
Ming, Mei, Ai, Yumei, Li, Shengnan, Wan, Haozheng, Sang, Shentian, Zhang, Haifeng
Optimal Low-Thrust GEO-to-DRO Transfers via a Multi-Layer Homotopy Strategy for Cislunar Applications2026-99-293710/19/2026
Low-thrust transfers from Geostationary Earth Orbit (GEO) to Distant Retrograde Orbit (DRO) are numerically difficult to compute because the underlying two-point boundary value problem is strongly affected by nonlinear cislunar dynamics, discontinuous control behavior, and the high sensitivity of indirect methods to the initial costates. Within the framework of the Earth-Moon Circular Restricted Three-Body Problem (CR3BP), this paper formulates an indirect optimal control model for GEO-to-DRO low-thrust trajectory design and develops a multi-layer homotopy strategy to improve the solvability of the problem. In the minimum-fuel formulation, the optimal throttle generally follows a bang-bang structure, which makes the associated shooting equations highly sensitive and often causes convergence failure. To reduce this difficulty, a smoothing homotopy is first introduced into the performance index, allowing the solution to evolve continuously from a minimum-energy problem with smoother control characteristics toward the target minimum-fuel problem. On this basis, a thrust continuation procedure is adopted to transfer the converged solution from an auxiliary high-thrust case to the desired low-thrust regime. However, when the conventional discrete continuation scheme becomes ineffective in a local thrust interval, a nonlinear intra-interval thrust homotopy combined with pseudo-arclength continuation is further employed to pass through the problematic region and maintain continuous tracking of the solution branch. Numerical results show that the proposed method can successfully overcome the local breakdown encountered in classical continuation and eventually obtain the minimum-fuel optimal transfer trajectory from GEO to DRO. The study indicates that the proposed framework enhances the robustness and continuity of indirect optimization for low-thrust transfers in the CR3BP, and it provides an effective computational approach for the design of cislunar transfer trajectories.
Liang, Jintang, Zhang, Zexu, Wang, Yiyu, Mao, Yicheng, Li, Yingshuo
LLM-Based Satellite Multimodal Knowledge Graph Construction: A Context-Aware Self-Interpretable Paradigm2026-99-290410/19/2026
This paper addresses three main bottlenecks in commercial aerospace multimodal data: semantic alignment difficulties, rigid ontologies, and context loss. We propose a method to construct satellite multimodal knowledge graphs (KGs) using Large Language Model (LLM).The process begins with layout analysis. This step preprocesses interleaved text-image satellite documents to align cross-modal semantics. To resolve ambiguity in technical terms, we developed a triple-positioning entity extraction mechanism. This mechanism directly identifies the original text, summaries, and entity types. For event nodes, we use an event-centric modeling paradigm. This paradigm captures spatio-temporal elements and other meta informations to ensure the nodes are self-interpretable. Entity normalization and automatic schema recognition rely on deep vector embedding. This embedding can semantic clustering the entities for the KG, eliminates the need for prior ontologies. We tested the method on real satellite datasets. The experimental results show high logical integrity and confirm its practical value for aerospace failure analysis.
Chen, Yanyu, Yu, Yeluo, Zhang, Jiliang, Bao, Junpeng
Multilevel Bayesian Model Updating for Satellite High-Stability Brackets Considering Welding Uncertainties2026-99-292510/19/2026
The high-stability bracket is the primary component in remote sensing satellites responsible for carrying the camera and attitude control equipment; its dynamic behavior directly affects imaging quality and in-orbit service life. Welding-based connections introduce uncertainties during manufacturing and assembly, leading to significant discrepancies between finite element analysis results and experimental data. To quantitatively assess the impact of welding processes on the accuracy of the high-stability bracket’s finite element model(FEM), this study proposes a Bayesian parameter correction method based on Markov Chain Monte Carlo (MCMC) sampling and the Metropolis-Hastings (MH) algorithm. It employs a multilevel correction experimental framework—”material-level, component-level, and system-level”—to progressively refine material parameters and establish a high-fidelity FEM. First, the elastic modulus of the welded and non-welded regions were calculated using stress-strain curves obtained from static tensile tests on welded and non-welded plates; then, targeting the first two natural frequencies from dynamic impact tests on bar specimens, the material densities of the welded and non-welded regions were corrected using the Bayesian method; finally, the effectiveness of the correction method was verified through free vibration tests on the actual high-stability bracket. The results indicate that, compared to the undamaged region, the welded region exhibits a 13.87% loss in elastic modulus and a 55.32% loss in density. The relative errors in the first three natural frequencies of the corrected model were 0.59%, 0.01%, and 0.83%, respectively, verifying the high reliability of the proposed method in modal identification and providing a new technical approach for correcting spacecraft structural models.
Liu, Kun, Guo, Chongchong, Wu, Wenhua
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.
Robust Fiber Optics for Ground Combat Vehicles: Unlocking the Bandwidth for the Mission2026-01-75419/22/2026
The U.S. Army’s Modular Open Systems Approach (MOSA) is driving data-centric vehicle architectures that demand higher bandwidth, faster decision loops, and greater cross-platform interoperability. Despite these needs, stakeholders hesitate to adopt fiber optics because of perceived fragility, field-retrofit FOD risk, and soldier-handling concerns. This paper characterizes common fiber failure modes in ground-vehicle environments and demonstrates how system-level ruggedization, connector design, and qualified components mitigate those risks. Drawing on demonstrated experience with hardened optics, connector-integrated transceivers, sealed media converters, and rugged cabling, we summarize practical architecture and field maintenance procedures that enable reliable fiber deployment in vehicles. Results show that, with appropriate component selection, installation practices, and built-in diagnostics, fiber optics can provide a robust, maintainable backbone for future ground vehicle networks.
Kouns, Heath, Simms, Marc, Weiss, Evan, Breedlove, Joe
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