Browse Topic: Collaboration and partnering

Items (2,273)
Structural optimization in shipbuilding represents a significant research focus within the fields of naval architecture and marine engineering. This study investigates multi-condition topological optimization for the deck pillar region of a transport ship's sectional structure. A mechanical model incorporating six typical load conditions was developed, and the Analytical Hierarchy Process (AHP) was employed to quantify the weighting coefficients for each condition. This enabled multi-condition collaborative topological optimization of the pillar layout. The optimized configuration underwent model reconstruction and finite element verification. Results demonstrate that the proposed multi-condition collaborative topology optimization method effectively balances structural performance and weight reduction requirements while satisfying strength specifications. This method yields optimal pillar layouts meeting multi-condition constraints, providing a reference for multi-condition topology optimization studies in ship structures.
Pei, ZihaoWei, YiFeng, RugeLiu, Kun
A research team led by Professor Lin Gui at the Institute of Physics and Chemistry, Chinese Academy of Sciences, reports the first fabrication of multi-layer flexible batteries using a combination of liquid metal microfluidic perfusion and plasma-based reversible bonding techniques.
Innovators at NASA Johnson Space Center, in collaboration with innovators at American Oxygen, have developed a solid-state system and process that separates oxygen from ambient air and compresses the resulting purified oxygen — with a significant reduction in power consumption compared to prior state-of-the-art. It is based upon a proven solid oxide electrochemical oxygen separation and compression technique that derives purified oxygen from ambient air and compresses it using an electrochemical pumping method.
3D printing could change how we build parts for jet engines and power plants, but the process leaves microscopic holes that cause the materials to shatter. Published in International Journal of Extreme Manufacturing, Professor Fangyong Niu’s team in Dalian University of Technology have fixed the problem by doing something unconventional: They added a microwave.
A team of researchers at the Max Planck Institute for Intelligent Systems (MPI-IS) in Stuttgart developed a biohybrid micro swimmer covered with magnetic material, whose swimming ability is largely unaffected by the coating. The team from the Physical Intelligence Department at MPI-IS published their work in the journal Matter, which covers a wide range of materials science research.
Focusing on the requirements engineering activities, this study analyzed the problems in the implementation process of the forward design practice of commercial aircraft airframe, introduced the breakthrough methods, including the convergence and integration with the traditional design process, the supporting work organization model, process optimization, and specification, and proposed the airframe stakeholder need capture model based on the theory of systems engineering. Practice has shown that the requirements engineering implementation strategy introduced in this paper can effectively resolve conflicts and redundancies between the requirements system and the original top-level document system requirements. It ensures clear requirements sources, sufficient basis, reasonable allocation, controllable changes, adequate change assessments, clear design status, and controllable design risks. It effectively overcomes human resource bottlenecks during the early stage of requirements engineering implementation while cultivating talent reserves for systems engineering implementation, saving approximately 23.5 person-years in labor costs. It significantly optimizes non-value-added processes, reducing approximately 100 reports. It unifies the team’s understanding of requirements work, improves coordination efficiency, and significantly improves the requirements validation rate between aircraft-level and system-level requirements by an average of approximately 46%. It assists stakeholders and engineers in systematically and scientifically capturing product requirements during the design phase, with original product design specifications covering approximately 70% of subsystem specifications on average. Given its generality across the airframe forward design domain, the airframe requirement management paradigm established by this implementation strategy holds significant importance for the comprehensive and in-depth application of systems engineering methods in commercial aircraft development.
Sun, LuyanChang, Liang
A test device for detecting the durability of the surface of elderly-friendly mattresses was designed and developed, which has functions such as force value monitoring, displacement monitoring, data recording, and hardness grade determination. Through the collaborative work of the mechanical system and the control system, high-precision reciprocating rolling tests and hardness grade determination on the mattress surface are realized. The verification test results show that the relative standard deviation (RSD) value of the mattress hardness grade test results is less than 10%, indicating that the detection data obtained by using this device is stable, meets the design requirements, and has operability.
Wang, JinFeng, PanpanShen, GuofengZhang, Lei
Reliability evaluation aims to quantify the reliability level of equipment and to verify its compliance with reliability requirements. Existing reliability evaluation methods primarily rely on operational phase data, which means reliability evaluation may lag behind actual needs. In practice, both users and design teams are more concerned with how to estimate CNC machine tools’ reliability before they are put into operation. Moreover, current reliability evaluation methods usually ignore the design team’s influence on CNC machine tool reliability. To overcome these limitations, this study proposes a novel reliability evaluation method that accounts for the influence of the design team on the reliability of CNC machine tools. By analyzing the impact of the design team’s technical capabilities and reliability capabilities on CNC machine tool reliability, a set of quantifiable evaluation indicators was established. Then, the weight coefficients of all indicators were determined using the expert scoring method. Finally, all data were integrated using the vector projection method, which enabled a quantitative reliability evaluation of CNC machine tools from different design teams within the same category. Additionally, the proposed method was applied to conduct practical case studies on multiple CNC external cylindrical grinding machine tools designed by different design teams, thereby validating the feasibility of the proposed method. The reliability evaluation results not only determine the reliability level of each CNC machine tool but also identify the weak points in the technical capabilities and reliability competencies of each design team. This study concludes by discussing the significance of this approach for enhancing the reliability capabilities of design teams and its practical implications for end users.
Sun, DongyangZheng, WeixuChu, HongyanXu, JingjingCheng, Qiang
Collaborative manufacturing networks enhance production efficiency but are increasingly vulnerable to cascading failures due to their complex interdependencies, particularly in critical processes like gear manufacturing. This study addresses this challenge by proposing a dynamic modelling framework based on Cellular Automata. Utilizing manufacturing resource and task scheduling data, a material flow-driven Directed Acyclic Graph (DAG) is constructed to capture the network’s hierarchical topology. Key innovations include state transition rules with memory effects, where dynamic failure probability integrates neighbouring node states and historical failure records, governing normal node failure, recovery, and re-failure (with an attenuation factor reflecting enhanced resilience). The case study focusing on the gear manufacturing industry, through simulations on a 100-node gear production network, reveals spatiotemporal failure propagation patterns. By implementing resource redundancy configuration and material flow optimization, iterations generally converge around 35 steps, demonstrating significant self-recovery potential and strong network robustness in collaborative manufacturing networks. This approach provides a scientifically grounded tool for identifying cascading risks in collaborative manufacturing networks.
Bai, HaoKou, ZhidaLiang, JingyaZhang, Cheng
This paper takes a 3D Printer proposed by the project team in the early stage as the research object, constructs a digital twin entity including 3D models and data models in order to develop a digital twin interactive software. By activating the real-time correlation between 3D models and data models, valuable data exchange can be achieved between the digital twin entity and the physical entity, and valuable data can be used to drive both to refresh their operating status.
Li, QiWu, WenKaiLang, ZhiQiJiao, HongChengJing, TaoZhao, HanTaoDong, ShenShi, Lei
North American CAV Performance Data StandardWP-00157/22/2026
As the deployment of connected and automated vehicles (CAVs) expands, the need for a consistent, cross-industry approach to performance relevant CAV data exchange is becoming more pressing. Vehicle developers, infrastructure owners and operators (IOOs), and technology providers generate and consume data that support safety, mobility, and operational efficiency, yet much of the data remains fragmented, inconsistently formatted, and difficult to reuse across systems. To address these gaps, the Society of Automotive Engineers (SAE) and the Canadian Standards Association (CSA) convened a multi-stakeholder workshop on November 3, 2025, with participants representing original equipment manufacturers (OEMs), automated driving system (ADS) developers, state and local agencies, standards bodies, and technology partners. The workshop focused on identifying challenges, clarifying needs, and outlining a path toward a North American CAV Performance Data Standard. Key themes from the workshop included: -The need for a shared data language to support safe and interoperable CAV operations. -The lack of consistent formatting, labeling and visibility regarding who produces and consumes data. -A “start small, iterate and scale” approach beginning with well-defined use cases. -The need for technical harmonization and governance structures that build trust and support sustained coordination. This white paper summarizes the key findings and outlines a practical approach to developing a Version 0.1 base-layer data standard that can support measurable progress in 2026 and beyond.
Nesheli, Mahmood
This paper puts forward a Privacy-Preserving UAV-Based Traffic Data Acquisition Platform to address 1) privacy leakage, 2) limited scenario coverage, and 3) low traffic data utilization efficiency in urban traffic monitoring environments. Our system integrates three innovations: 1) Dynamic Privacy Masking (DPM) and Dual-Track acquisition (DTC), which hides sensitive information (e.g., faces, license plates or LPL) in real-time while preserving critical traffic data (e.g., vehicle density, speed), 2) traffic data Localization (DL) and Privacy-Enhanced Federated Learning (FEFL), enabling cross-regional collaboration without raw traffic data sharing by perturbing neural network updates with differential privacy (DP), and 3) Ground-Air Collaboration (GAC) and VPF (VPF), combining UAVs with ground sensors and digital twins (DTs) to cover blind spots (e.g., tunnels, extreme weather). Experimented on UA-DETRAC and CitySim traffic data-sets, the platform achieves 92% privacy compliance (GDPR/PIPL), 87.5% mAP accuracy, and 85% road network coverage, outperforming other methods (e.g., FedUAV, static blurring). It supports applications such as traffic flow optimization, accident prevention, and regulatory alignment.
Zhang, ShilinYan, Ming
SAE TOMORROW TODAY - What Baja SAE Teaches That College Can?t135746/26/2026
What does it really take to engineer under pressure? From mud-soaked vehicles and broken suspensions to team dynamics and split-second decisions, Baja SAE has become a proving ground for the next generation of engineering leaders. By challenging engineering students to design, build, and race single-seat off-road vehicles capable of surviving extreme terrain, Baja SAE requires every team to use the same 14 hp Kohler engine -- creating an even playing field and putting the focus on innovation, durability, and teamwork. Listen in as Honda's Adam Hussemann and TTX Company's Jason Rounds pull back the curtain on the intense, unpredictable world of Baja SAE competitions and how they prepare students for careers in manufacturing, mobility, and beyond. After hearing this conversation, you'll understand why more and more companies value Baja experience just as much as a perfect GPA. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.
Patterson, Lori
Augmented Reality (AR) and multimodal human–machine interfaces (MMI)— combining visual overlays, voice, gesture, eye- tracking, and biometric sensing—are maturing into flight-relevant technologies capable of transforming astronaut training and in-orbit operations. These interfaces can reduce task time, lower procedural errors, and mitigate cognitive workload, thereby strengthening crew autonomy and mission safety. Global operational experiences from International Space Station (ISS) augmented- reality trials and related international programs are synthesized to inform the proposed system architecture and validation framework: (i) an overview of India’s current AR/MMI-related ecosystem relevant to human spaceflight, including astronaut training pipelines and research collaborations; (ii) a mission-grade AR/MMI system architecture and multimodal fusion/decision logic suitable for human-rated operations; (iii) algorithms and programming examples for AR-driven finite-state-machine (FSM) procedures and workload-sensitive adaptation; and (iv) simulation-backed datasets across representative procedures indicating approximately 20 to 30 percent task-time reduction and approximately 40 to 50 percent error- rate reduction under controlled conditions (based on ten procedures and twenty-four simulated sessions for workload analysis). The findings reinforce that AR/MMI deployment can improve training throughput, reduce crew fatigue, and increase safety margins when designed with evidence gating, conservative confidence thresholds, and robust fallback modes. Recommendations include establishing a Human Space Flight Centre (HSFC) AR/MMI laboratory, conducting structured A/B validation trials, and committing resources for progressive demonstrations aligned with future in-orbit operations.
Yadav, Anoop Singh
In the field of Aerospace, which has a long Life-Cycle process [20-30Years], Component Obsolescence has become a major problem as it prevents Maintenance & sustenance of a product with committed life-cycle period. Obsolescence Management plays a vital role by deriving strategic plans on proactive obsolescence where the system needs to be supported for several decades. This abstract analyzes the obsolescence challenges in the Aviation industry especially in Avionics System impacted by component obsolescence and present the possible proactive obsolescence management in terms of Engineering, Technology, and business/cost elements. The Obsolescence problem cannot be avoided but the impact of obsolescence and mitigate the risk can be minimized by planning and managing response. The obsolescence risk assessment for the Bill Of Materials (BOM) is a paramount activity to manage obsolescence proactively and cost-effectively. Digital Transformation of analyzing the component obsolescence status and integrated with statistical model to predict the End of Life (EOL) of sub-system/System. The EOL predictions would aid Obsolescence management plan, with mitigation strategies including Form-Fit-Function (FFF) replacements, component life extension through refurbishment, Lead-Free Control plan, component counterfeit and collaborative frameworks for modular, open-standard designs. This approach aimed at reducing unplanned costs by up to 40% on DMSMS (Diminishing Manufacturing Sources and Material Shortages) Management Plan, aligning with IEC 62402 (International standard for obsolescence management) and ARINC 662-1 (Guidelines for obsolescence management in commercial aircraft).
Dharmananyala, RohithMunirathnam, KrishnaMarokeyfrancis, JoisyjoseSadashivaiah, NageshKondamari, Harshitha
Achieving zero-waste manufacturing in aerospace requires a shift from end-of-pipe waste mitigation toward circular design principles embedded early in product development. This paper presents a practical framework for integrating circularity into aerospace systems through five design pillars: design for modularity and disassembly, material substitution to enhance recyclability, waste segregation and characterization, component-level circularity readiness scoring, and collaborative supplier engagement. To operationalize this approach, a Circularity Readiness Assessment Tool (CRAT) is developed to evaluate design alternatives against criteria such as disassembly ease, material recyclability, manufacturing waste potential, end-of-life recovery pathways, and supplier take-back mechanisms. The framework supports multi-criteria decision-making by complementing traditional aerospace design drivers including weight, performance, cost, and safety. The methodology is demonstrated through a case study of an aircraft seating system. Scenario-based analysis indicates that targeted circular design interventions can reduce material waste and lifecycle carbon emissions while maintaining functional and regulatory requirements. Emphasizing practical engineering workflows rather than exhaustive lifecycle modeling, this work provides a scalable foundation for embedding circular design into aerospace product development and advancing zero-waste manufacturing objectives.
S, Chaitra
Aircraft Maintenance, Repair, and Overhaul (MRO) operations are highly complex, involving coordination among multiple stakeholders including airlines, MRO providers, OEMs, and regulatory authorities. A significant challenge in this space is managing unplanned events such as Aircraft on Ground (AOG) conditions, where delays can lead to major financial losses to airlines and safety risks. Engineers must quickly diagnose the damage, evaluate compliance against regulatory limits, coordinate with OEMs, and make critical decisions—all while navigating a fragmented ecosystem of disconnected systems, diverse document types, and time-sensitive processes. This paper presents a real-world, intelligent MRO solution that addresses these challenges through the use of Agentic AI and context engineering. The system is designed to automate and augment key MRO workflows such as damage detection, repair pathway selection, compliance verification, and supplier coordination. At its core, the solution is powered by a set of autonomous agents—each responsible for specific tasks like interpreting repair manuals, evaluating damage severity, or communicating with OEM portals. A key innovation of this system is its use of context engineering, which enables agents to share a unified, real-time view of the aircraft condition, document references, decisions made, and deadlines involved. This shared memory—dynamically updated using modern data stores and retrieval systems—ensures that agents and human experts operate with full situational awareness. The solution facilitates measurable improvements in reducing aircraft downtime, speeding up OEM coordination, and ensuring real-time continuous regulatory compliance. Engineers can take faster, more informed decisions with confidence, while human-in-the-loop oversight was preserved for critical steps such as compliance sign-off and final approvals. Overall, this intelligent MRO system transforms static, document-heavy processes into a dynamic, context-aware workflow. It brings together agent collaboration, regulatory alignment, and real-time information flow to solve one of the most pressing operational problems in aviation today. By improving inspection-to-repair cycles, enhancing SLA adherence, and enabling traceable, data-driven decision-making, this work lays the foundation for the next generation of digital MRO ecosystems that are efficient, safe, and scalable.
Abburu, SunithaG.V.V., Ravi KumarPoovalingam, SundaresanVaderahobli, Devaraja Holla
Smart implants that not only stabilize a fracture but also monitor the healing process from day one — and deliver targeted support when required — are currently being developed at Saarland University by a team of engineers, medical researchers, and computer scientists. The engineering team led by Paul Motzki is contributing shape-memory micro-actuators with integrated sensing capabilities, while Bergita Ganse and her research group provide the medical expertise in fracture healing.
With $800 of off-the-shelf equipment and months’ worth of patience, a team of U.S. computer scientists set out to find out how well geostationary satellite communications are encrypted. And what they found was shocking.
Healable spacecraft structures could soon be possible thanks to cutting-edge composite technology. Swiss companies CompPair and CSEM, and Belgian company Com&Sens have partnered with the European Space Agency (ESA) to modify their self-healing carbon fiber product for use in space transportation.
Researchers at Cornell University, working with collaborators, have created an extremely small neural implant that can sit on a grain of salt. Despite its size, the device can wirelessly transmit brain activity data from a living animal for more than a year.
Keith Yuen, a supervisory engineer at Naval Air Warfare Center Weapons Division (NAWCWD) spent years building a jammer designed to defeat America’s own radars. The harder his team made it for friendly pilots to see through the jamming, the better they were doing their job.
Researchers from CompPair and the European Space Agency have developed a new composite material for spacecraft with an embedded healing agent. European Space Agency, Paris, France Healable spacecraft structures could soon be possible thanks to cutting-edge composite technology. Swiss companies CompPair and CSEM, and Belgian company Com&Sens have partnered with the European Space Agency (ESA) to modify their self-healing carbon fiber product for use in space transportation. Project Cassandra - an abbreviation for Composite Autonomous Sensing and Repair - includes sensors and a heating element within a composite carbon-fiber material, allowing spacecraft to autonomously repair initial stages of damage.
Researchers discover texts, phone calls, military communication, internal corporate networks all easily eavesdropped on using off-the-shelf equipment. University of California San Diego, La Jolla, CA With $800 of off-the-shelf equipment and months' worth of patience, a team of U.S. computer scientists set out to find out how well geostationary satellite communications are encrypted. And what they found was shocking. Close to half of the communications beamed from satellites to the ground that the researchers were able to listen in on were not encrypted. This included sensitive data including cellular text messages, voice calls, as well as sensitive military information, data from internal corporate and bank networks, and the in-flight online activity of airline passengers.
Materials innovations are shaping the next generation of medical devices. In this Q&A, Jeremy Schaffer, director of research and development at Fort Wayne Metals, discusses how advances in titanium, nickel-titanium, surface engineering, and smart materials are helping device developers improve performance, miniaturization, durability, and patient outcomes. He also addresses sustainability, scale-up challenges, and the collaborations needed to move promising materials from research into real-world medical use.
To address the challenge of balancing voltage support and current limitation in grid-forming converters (GFCs)—a challenge induced by the uncontrollability of active power during transient faults in microgrids and weak grids—a low voltage ride through (LVRT) strategy utilizing adaptive virtual impedance with a variable resistance-to-inductance ratio is proposed. This strategy is designed to maximize the satisfaction of reactive power support and current limiting characteristics. By adaptively generating virtual impedance based on changing line parameters, the method enables adaptation to large disturbance conditions involving variations in line impedance and Short Circuit Ratio (SCR). First, a transient model of the virtual impedance for GFCs is established to clarify the transient instability mechanism. During the transient period, the power loop is controlled to prevent power angle divergence. Second, the influence mechanism of virtual impedance on reactive current and output current during transients is analyzed. By integrating inrush current limitation and LVRT requirements, an adaptive virtual impedance control strategy is formulated to achieve collaborative optimization of transient LVRT and current limiting. Finally, the effectiveness of the proposed control strategy is verified through MATLAB/Simulink simulation experiments.
Pang, BoYang, XiangzhenLiu, Fang
A boat can sink in under a minute, stranding passengers miles from land and leaving them virtually invisible amid the vast expanse of water. Survival can depend on emergency supplies such as a personal locator beacon (PLB) and emergency position-indicating radio beacon (EPIRB) to alert search and rescue teams. When a competitive fishing trip went wrong for Easton Barrett and his friends 40 miles off the Gulf Coast of Mississippi in 2024, a PLB helped the U.S. Coast Guard rescue the people clinging to coolers and treading water after being head-butted by sharks.
A new approach that enables the synthesis of fully coupled system dynamics is described in this paper. The approach facilitates collaboration between solver developers by explicitly avoiding inter-code coupling and instead uses a generic interface that enables inputs to be set and flags outputs available to other solvers. The assembly of a fully coupled linearized system matrix is obtained entirely from the existence of coupling maps, and does not rely on user intervention. Tiltrotor whirl flutter in cruise conditions is systematically investigated by careful examination of results obtained through various combinations of domain synthesis and obtained from the Hermes coupling framework. Investigated solver domains include nonlinear rotor dynamics, nonlinear aerodynamics, and linear structural dynamics. Utilized software modules include RCAS; Project Chrono, a purpose-built lifting line aerodynamics solver; and FuselageSolver for linear structural dynamics. Aeroelastic predictions are synthesized and verified for a pitch/plunge airfoil and a low-speed wing. Rotor aeroelastic effects are verified by coupling rotor dynamics with rotor aerodynamics. Structure-to-structure results are synthesized by coupling both RCAS and Project Chrono rotor dynamics models to a pylon structure modeled in FuselageSolver. Finally, full whirl flutter predictions are synthesized by coupling Project Chrono, lifting line aerodynamics, and FuselageSolver, as well as an RCAS model without the wing/pylon structure to FuselageSolver. Results indicate that the new synthesis approach is viable and accurate.
Reveles, NicolasVan Damme, ChristopherRobinson, JosephTuman, MatthewHansen, Josh
This paper presents a spatio-temporal graph neural network (STGNN) centric approach to enable heterogeneous agents to collaborate and cooperate for different types of missions. The STGNN-centric approach and corresponding autonomy are encapsulated in the Advanced Graph-enabled Network Technology for Collaborative Autonomous Agents (AGENTCA) technology. Various decentralized and distributed control architectures are reported in the literature, but in some instances these approaches do not leverage the inherent graph network which can increase scalability to larger teams and algorithmic efficiency. Specifically, in this paper advances in artificial intelligence are leveraged to parameterize and encode optimal, or nearly optimal, swarm control techniques. For this work, the team focused on developing a diffusion-based STGNN swarm controller using imitation learning. An expert, centralized swarm control law was used to guide the STGNN during the learning process. The STGNN controller enables the swarm to follow a leader while avoiding static and dynamic obstacles and maintaining a desired separation distance from neighbors and obstacles. The approach is demonstrated in simulation with hundreds of agents and in flight tests with up to thirteen test vehicles.
Cooper, JaredLu, Chang-TienChen, SijiCarson, AndrewPeters, AndrewOlowin, AaronEnnasr, OsamaLichter, Matthew
The University of Maryland undergraduate team presents Draco in response to the 42nd Student design Competition RFP "Pioneering Hydrogen-Electric VTOL". Draco uses a simple, effective configuration: a single main rotor helicopter with compounded wings. Through calculations and trade studies, the team was able to design a rotorcraft capable of performing the prescribed mission with maximized loiter endurance, while meeting all design constraints and requirements.
Renz, SamCotoia, Colby
In response to the 42nd (2025) Annual VFS Student Design Competition, the Graduate Student Design Team from the University of Maryland introduces Wyvern, a novel hydrogen-powered electric compound rotor-craft engineered for maximum loiter and operational safety. Named after a mythical dragon that defies convention by not breathing fire, Wyvern only breathes water vapor by forgoing hydrocarbon combustion in favor of the quiet and clean power of hydrogen. This design reflects not only an aeronautical solution to an engineering challenge but a greater aspiration to reshaping how practical and clean vertical flight can be achieved.
Basak, KumardipOgle, William
Software is driving major changes in automotive design. The rise of the software-defined vehicle, combined with increasing automation, is dramatically increasing software complexity. Automotive teams must deliver larger volumes of safety-critical code on tighter schedules while maintaining strict compliance with functional safety standards. In this environment, effective testing and verification are more important than ever. Development teams are increasingly adopting shift-left testing strategies, where defects are identified early at the unit level before software progresses down the development pipeline. Detecting issues earlier reduces risk, lowers remediation costs, and improves development velocity.
Camacho, Ricardo
Recently, a cross-border collaborative team consisting of Sunwoda Mobility Energy Technology Co., Ltd (a globally leading battery manufacturer), Chery Automobile Co., Ltd (a world-renowned vehicle manufacturer), the State University of New York at Binghamton (including Professor M. Stanley Whittingham, a Nobel laureate), Semitronix Corporation (a globally renowned EDA company), the University of Delaware, and Advance Power jointly officially published their review article titled “Revolutionizing Batteries Based on Digital Twin through AI-Simulation Synergy for Design, Manufacturing, Operation, and Recycle” in the international academic journal National Science Open.
A research team from Huawei’s advanced wireless labs in Canada and China has published a blueprint for a 6G core network that can generate, update, and execute its own control procedures without human intervention. Described in Engineering, the “Agentic-AI Core” (A-Core) treats every service — whether a simple connection request or a complex artificial intelligence (AI)-driven application — as a “mission” that is planned, instantiated and run by a team of specialized AI agents.
A team led by Professor Yan Lu, Helmholtz-Zentrum Berlin, and Professor Arne Thomas, Technical University of Berlin, has developed a material that enhances the capacity and stability of lithium-sulfur batteries. The material is based on polymers that form a framework with open pores (known as radical-cationic covalent organic frameworks or COFs). Catalytically accelerated reactions take place in these pores, firmly trapping polysulfides, which would shorten the battery life.
A team of researchers from the U.S. Department of Energy Ames National Laboratory developed a magnetocaloric heat pump that matches current vapor-compression heat pumps for weight, cost, and performance. Current heating and cooling devices are based on vapor-compression technology, which is over 100 years old. They rely on refrigerants that contribute to global carbon emissions, and when they leak the chemicals are harmful to people and the environment.
The shared autonomy framework has become an option with great potential in the field of autonomous vehicles. Human and machine control decisions typically demonstrate strengths in different scenarios. As a result, the robustness of systems can be enhanced by the collaboration between humans and autonomy. A shared autonomy architecture that takes into account both human and environmental factors was proposed in this work. The authority distribution between the human operator and the autonomy algorithm was determined by the Shared Autonomy Arbiter (SAB). Designed with a two-tier structure, the SAB incorporated a policy-level decision module, as well as a numerical-level arbitration tuning module. A fuzzy inference system (FIS) was incorporated to enhance the noise tolerance of the policy selection module. Furthermore, the human factor was taken into account by applying a projection to the users’ control input. The human operator’s control decision was projected by the Adaptive Personalized Control System (APeCS) to accommodate the skill levels and habits of various users. By incorporating a broad set of factors, this framework is suitable for diverse applications that require robustness in complex environments. Two case studies were included in this work to demonstrate its effectiveness. The first presented a concept design illustrating the application of the proposed architecture on autonomous vehicles operating in varied environments. The second showed that the proposed architecture can serve as a robust testbed by taking advantage of the authority modulating mechanism. By connecting a system under assessment and an established autonomy algorithm to the SAB, the new system can be tested robustly and safely through the flexible authority distribution.
Sang, I-ChenNorris, WilliamPatterson, AlbertSreenivas, Ramavarapu S.Soylemezoglu PhD, AhmetNottage, Dustin S.
This paper presents the collaborative efforts of the USCAR GPF OBD Working Group to evaluate and recommend On-Board Diagnostic (OBD) monitoring requirements for Gasoline Particulate Filters (GPFs). The group, comprising representatives from major OEMs, aims to establish a unified understanding of GPF monitoring capabilities and propose regulatory recommendations to CARB. The paper outlines the physics of soot generation and oxidation, regulatory interpretations, and diagnostic strategies, culminating in a proposed framework for GPF OBD compliance. The material in this paper was previously presented at the 2024 SAE OBD Symposium [1].
Van Nieuwstadt, MichielRamappan, VijayJohnson, LonnyWendling, Timothy
The tire model is a crucial component in the design of the K-characteristic of FSAE racing car suspensions, and directly influences the achievement of maximum cornering lateral force. Not only do the slip angle, vertical load, tire pressure, and camber angle affect the mechanical characteristics of the tire, but temperature is also an important influencing factor when FSAE vehicle tires operate at high speeds. However, the modeling process of traditional tire models based on temperature characteristics is often very complex. The FSAE tire test code (FSAE TTC) already has a large amount of official sample data, which provides a basis for data-driven neural network models. This study implemented a hybrid modeling methodology, constructing two cascaded feedforward neural networks that combine the physical interpretability of the Magic Formula tire model with the nonlinear approximation capabilities of neural networks. The first network model uses slip angle, vertical load, tire pressure, and camber angle as input features, while the second uses tire temperature, ambient temperature, and ground temperature. The first network model simulates the magic formula model of the tire, and the second fine-tunes the lateral force, aligning moment, and overturning moment based on temperature characteristics. It prevents secondary input features (such as temperature) from being completely dominated by primary input features, facilitating the explanation of the influence of the two feature groups on tire characteristics. The accuracy and robustness of the model are suitable for the engineering requirements of FSAE. During the Formula Student China competition, based on on-track measured data, the tire model was co-simulated with VI-CarRealTime to quickly calculate the tire pressure required to achieve maximum lateral force. This effectively saved practice time before the race and helped the team achieve a third-place finish.
Liu, XiyuanWang, ShenyaoLi, MingyuanHuang, Jiayu
The multi-body dynamics (MBD) model and the MATLAB Simulink model can be integrated to create a control-integration model. Using a high-fidelity MBD model to represent the vehicle as the plant, this integrated model can be used to analyze vehicle system physics and develop control strategies. For hybrid vehicles, this process is more complex because the powertrain and other vehicle systems are often built as separate MBD models. This paper describes a method for integrating a powertrain model developed in AMESIM, a vehicle model developed in SIMPACK, and a control model developed in MATLAB Simulink. The resulting integrated model was then used to perform frequency sweep analysis to identify driveline system properties. In particular, the driveline frequency and the amplitude of the transfer function between motor speed and motor torque are critical parameters. By applying active damping control to the driveline system, the peak amplitude and driveline vibrations can be reduced. The hybrid vehicle studied includes a transmission system with ten different gears. When the vehicle operates at different gear level, the system behaves differently. The analysis results can assist the driveline control team in developing appropriate strategies to improve overall vehicle performance.
Xing, XingMathew, Vino
A suspension system was designed, fabricated, and tested following a systems design approach by an SAE Off-road Team from a North Midwest university. Compared to previous suspensions, the new suspension system is more reparable and contains a minimal number of custom parts, while still maintaining sufficient strength to withstand dynamic loads experienced when operating the vehicle. Modifications were also made to fit the newly designed vehicle body frame. As an integral part of the team’s 2025 Baja vehicle, the redesigned suspension system contributed to the vehicle’s improved performance during the 2025 SAE (Society of Automotive Engineers) Baja Competition. This paper presents a detailed account of the design, development, and fabrication process of the suspension system. The final design was tested and evaluated via both computer simulations and physical tests, whose efficiency and reliability were finally demonstrated by the team’s improved ranking in the 2025 Baja SAE Competition.
Liu, YuchengAnderson, MatthewLarson, CodyRodgers, JoshuaSeberger, AaronLetcher, Todd
Trust calibration is vital for safe human–automation interaction but remains largely qualitative. This study develops multiple quantitative frameworks modeling trust as a function of automation reliability. Four progressive models of binary, linear, triangular, and logistic formalize the calibrated trust zone, defining where human reliance aligns with system performance. The framework corrects major misconceptions: that trust is purely qualitative, that low trust–low reliability states are acceptable, and that overtrust and distrust pose equal risk. It establishes a minimum reliability threshold for meaningful trust and identifies distrust as the safer default in high-risk contexts. A case study on an empirical observation of 32 AI applications plotted in the trust–reliability space confirms the analysis, revealing a consistent distrust tendency where reliability exceeds user confidence and other observations. By quantifying trust through reliability, the study reframes it as a controllable safety variable, enabling predictive calibration and adaptive, trust-aware safety architectures for reliable human–AI collaboration.
Wen, HeMounir, Adil
As automotive aerodynamic testing facilities evolve to capture more real-world behavior, updating the correlation between old and new technologies is essential. Recently, the three-member consortium of the United States Council for Automotive Research (USCAR) - General Motors, Ford Motor Company, and FCA US LLC - transitioned from full-size static ground plane facilities to 5-belt moving ground plane wind tunnel facilities. The primary objective of this study was to update the correlation data sets to maintain consistent and robust data sharing among companies, which is the cornerstone of USCAR efforts. To achieve this, a set of updated correlation data sets were calculated to replace the original correlation study results from 2008. Additionally, the methodology for applying correlation equations was revised from using averaged wind tunnel data to employing direct wind tunnel-to-wind tunnel correlation equations. In a two-phase correlation effort conducted in 2022 and 2025, the three companies exchanged and evaluated six vehicles of varying size and proportions across the three rolling road wind tunnels. To ensure the updated correlation data sets capture the bounds of current and future vehicle aerodynamic performance, the tested bandwidth of coefficient of drag area (CDA) data ranged from 0.37 m2 to 1.45 m2 (CD from 0.17 to 0.48). Despite the unique challenges of each wind tunnel project, the outcome of the updated correlation efforts demonstrated excellent correlation (R2 > 99.8%) across direct tunnel-to-tunnel comparisons, mirroring the success of the original 2008 correlation efforts. These findings validate the accuracy and reliability of aerodynamic data collection in each of the three rolling road facilities, thereby supporting consistent and robust data sharing among USCAR partners.
Nastov, AlexanderLounsberry, ToddMadin, TrevorLangmeyer, GregoryFadler, GregorySkinner, ShaunHorton, Damien
Reliable component libraries are the foundation of the engineering process and the starting point for all intelligence within CAD tools. In practice, however, libraries created and maintained by librarians often contain incomplete, inconsistent, or outdated data. This paper introduces the component data consistency and relationship inference AI system, developed within Amoeba software, which addresses these challenges by improving component library quality. The system uses AI to infer component attributes such as component type, gender, color, material, etc. Moreover, it can identify relationships such as the family a connector is associated with based on its attributes and geometry. The system improves data consistency in areas such as resolving mismatched wire size constraints imposed by the connector and cavity components. It also utilizes computer vision to identify common connector footprints, cavity sizes, and 2D symbol geometries. Deployed within Amoeba software, the system has shown an ability to create parts ~30 times faster than manual methods with 98.81% accuracy. The novelty of this system is two-fold. First, it represents a unique integration of AI-based attribute inference and relationship reasoning for improving component library data quality. Second, the system enables a new paradigm of on-demand component creation within Amoeba software that allows engineering teams to obtain tailored components immediately rather than waiting for delivery from librarians. By enabling agile component library management and maintaining data integrity, the system brings benefits in the environment of Industry 4.0 and the increasing digitization of engineering processes.
Phan, DungHorvat, Bryan
The automotive industry is subject to major transformation initiated by societal and economical pull (reducing emissions, zero fatalities, European competitiveness) and accelerated by technology push (electrification, Cooperative, Connected and Automated Mobility (CCAM), and Cooperative Intelligent Transport Systems (C-ITS)). Following this trend, the Software-Defined Vehicle (SDV) targets the integration of software (SW) development methodologies for vehicle development as well as the value delivery shift toward customers along the entire lifecycle. It promises to create benefits for the car manufacturers in terms of faster time to market, easier update – as well as for the car users (private persons, fleet operators) in terms of personalized user experience, upgradability. At the same time, SDV requires a much more integrated and continuous development framework to enable different experts to efficiently develop and validate concurrently the different parts of the vehicles, to gather information about real operation, and to support update in the field. This paper introduces the collaborative development framework introduced in the European research program Collaborative Development Framework for electric-based Software-Defined Vehicles (CODE4EV).
Armengaud, EricPermann, RobertJoergler, SabrinaBarcelona, Miguel AngelGarcía, LauraRodriguez, José ManuelIvanov, ValentinLi, ZhenqianNguyen Quoc, TrieuRodrigues, SandyKowalczyk, BogdanAvdić Čaušević, Amra
The evolution toward software-defined vehicles (SDVs) is causing disruption to the traditional automotive supply chain and breaking down the common hierarchical OEM, tier 1 supplier, and tier 2 supplier relationships. With demands for faster software release cycles, more advanced software projects involving multi-party development, and considerations for end-to-end embedded and cloud integrations, new cybersecurity challenges are introduced that no single organization can address alone. Thus, this disruption creates new trust dependencies and requires new models for collaboration, transparency, and joint responsibility in cybersecurity. This paper presents a collaborative cybersecurity model, emphasizing shared responsibility during multi-party development between OEMs, tier 1 and 2 suppliers, engineering services organizations, and technology and services providers. As such, we explore collaborative approaches for each stage in the development lifecycle including design, development, testing and validation, and post-release activities. This includes joint development frameworks, standardized communication and reporting approaches, and cooperative continuous cybersecurity activities. These collaborative approaches enable the involved parties to maintain trust, mitigate cross-organization risks, and support rapid innovation while assuring cybersecurity. The current traditional siloed approaches or purely internal monitoring practices cannot adequately address new multi-party risks. Thus, as the automotive supply chain is disrupted, cybersecurity must also be considered in a collaborative manner in order to secure vehicles throughout the development lifecycle across a distributed and rapidly changing supply chain. Therefore, our paper focuses on a collaborative model that provides a practical, pre-competitive framework that allows to tackle cybersecurity cooperatively while enabling agile software delivery.
Oka, Dennis KengoVinzenz, Nico
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