Browse Topic: Body structures
Recent studies indicate that the door system plays a significant role in the interior noise levels of newly developed vehicles. This research investigates the noise transmission paths through the door system and identifies effective strategies for improvement through a combination of door buck testing and simulation. Specifically, in this study, the finite element method (FEM) was employed for door buck simulation, and the model was validated against vibration test results. Subsequently, acoustic analysis tools were utilized to correlate with noise testing, thereby establishing a process to ensure simulation accuracy. The sound insulation performance for the main areas of the door was experimentally evaluated, and a simulation model with good correlation to these test results was developed. By utilizing both experimental and simulation results, the principal transmission paths were identified, and appropriate improvement strategies for these paths were investigated. The validated improvement strategies are intended to be applied in the development of next-generation vehicles.
For analysing flow and acoustic induced structural vibration, a fully run time coupled framework combining a hybrid CFD-CAA approach with a modal response simulation was validated and presented at the ISVNH 2022 (SAE Technical Paper 2022-01-0938). In this paper i We apply this CFD–CAA–modal coupling method to a series-representative bonnet geometry and demonstrate its capability to capture flow and aeroacoustically driven vibration with two-way coupling. ii We analyse the modal properties of the bonnet and show that confined air volumes beneath the bonnet can introduce significant fluid loading effects, which are already embedded in experimentally validated FE modal models and must therefore be treated carefully in two-way coupled simulations. iii We validate the fully coupled aeroelastic simulation against wind-tunnel measurements with undisturbed inflow, show close agreement with the measured vibration response and analyse that the dominant excitation is in this case from below the bonnet due to acoustic pressure fluctuations.
Acoustic user interfaces and audio experiences are among the leading comfort factors in new vehicle interior designs. OEMs are more and more focusing on loudspeaker design and positioning, to provide the most immersive experience to the customers. The industrial target is to be able to predict the performance of an audio system in early design phases. This paper presents an integrated vibro-acoustic methodology enabling early-stage prediction of loudspeaker performance in real vehicle conditions. The approach combines electromechanical characterization, a hybrid loudspeaker calibrated model valid across the audible range and coupled FEM/BEM/SEA simulations to capture the loudspeaker response in the vehicle’s cabin considering door-installation effects and cabin acoustics. The method is validated experimentally on a rear-door loudspeaker installed in a production vehicle, showing strong correlation with measured SPL. A final application case demonstrates its capability to assess the impact of alternative speaker mounting positions during the design phase.
Gyroscopic effects split circumferential traveling-wave resonances of rotating structures into forward and backward branches. This work first analyzes the splitting in the co-rotating (Lagrangian) frame to provide physical intuition for the evolution of the two branches with spin speed. A transformation to the inertial (Eulerian) frame is then derived, showing that the observed frequencies are shifted by a kinematic Doppler-like term that acts with opposite sign on the forward and backward waves, leading to different Campbell-diagram slopes depending on the observation frame. The resulting framework is validated experimentally on a freely rotating, unloaded tire using two complementary sensing modalities: wireless on-tire accelerometers (co-rotating view) and a scanning laser Doppler vibrometer (inertial view). A frequency-domain SVD-based identification (FDD/ODS-SVD) is used to extract poles and deformation patterns over a range of spin speeds, enabling Campbell diagrams in both frames. The application of the proposed transformation maps the co-rotating branches onto the inertial observations, yielding consistent forward/backward splitting between the two measurement systems.
Autonomous platforms such as self-driving vehicles, advanced driver-assistance systems (ADAS), and intelligent aerial drones demand real-time video perception systems capable of delivering actionable visual information at ultra-low latency. High-resolution vision pipelines are often hindered by delays introduced at multiple stages—sensor acquisition, video encoding, data transmission, decoding, and display—undermining the responsiveness required for safety-critical decision making. This study introduces a holistic system-level optimization framework that systematically reduces end-to-end video latency while maintaining image fidelity and perception accuracy. The proposed approach integrates hardware-accelerated encoding, zero-copy direct memory access (DMA), lightweight UDP-based RTP transport, and GPU-accelerated decoding into a unified pipeline. By minimizing redundant memory copies and software bottlenecks, the system achieves seamless data flow across hardware and software boundaries. Evaluations demonstrate a latency reduction from a baseline of 45.3 milliseconds to an optimized 23.5 milliseconds, representing a 48.1% improvement without sacrificing spatial resolution or detection robustness. Under optimized configurations, the framework sustains frame rates above 60 FPS at both Full HD and 4K resolutions, with frame drop rates held to approximately 3%. Perceptual evaluation further confirms that object detection accuracy consistently exceeds 91% within the <35 ms latency range, while collision-prediction delays are reduced to below 12.4 ms, ensuring timely responses in dynamic scenarios. These improvements collectively validate the critical importance of hardware-software co-design for embedded vision systems. The results highlight that ultra-low-latency perception is achievable on edge platforms when pipelines are designed with cross-layer optimization, bridging sensor interfaces, video codecs, network transport, and GPU computation. The proposed architecture provides a scalable foundation for future embedded vision deployments in autonomous driving, robotics, and unmanned aerial systems, where low latency is a non-negotiable requirement for safety, reliability, and operational efficiency.
In the stringent market of BEV, the development of integrated Drive Modules (iDM) fitting environmental and customer needs is mandatory. It is important to extract the best from the less. To achieve those goals, a deep insight into complex multiphysics phenomena occurring in an iDM has been achieved by accurate and validated models. This engineering methodology is applied through the development of BorgWarner products, comprising non-exhaustively iDM 180-HF, Externally Excited Synchronous Machine and Multi-Level Inverter. The paper will review the methodology development for deeper understanding involving in-house technical excellence and complemented by strategic partnerships with academic institutions and start-ups. It will present the approach of integrating advanced multiphysics models with high-quality experimental validations, specifically on loss evaluation on electrical machines and inverters. Complex models involving multiphysics such as thermal/fluid coupling or electric-magnetic-mechanical behaviors are usually difficult to optimize separately since their objectives are often contradictory. Thus, BorgWarner PDS Engineering uses tools involving close coupling to optimize iDM products. The lecture will focus on innovation and optimization which are supported by several key pillars in the scope of a Next Generation iDM development. These are based on the following strategic levers such as process and design development, material development and control strategy among others. This ensures tailoring all components at the best of their capabilities to reduce their weight and maximize their use. Finally, the results achieved by the high-fidelity model-based optimization on the selected example will be presented (e.g., impact of the cooling improvement on overall iDM performances), demonstrating the benefit of capturing the system from granular view to a helicopter view in the design phase of next generation eDrives.
Industries are following a tedious product development cycle for developing their product. In product development major steps includes design ideas, Drawings, CAD, CAE, Testing and design improvement cycle. This is a monotonous process and takes time which impacts on its time to deliver product and cost on development. Now a days industries are fast growing and targeting to reduce development cycle time and cost. AI&ML is impacting almost all areas in the industry and significantly reducing efforts time and cost. To make use of AI&ML in CAE, Altair Physics AI is an effective tool. To ensure the design of product traditional way is to develop a CAD of the product, develop, perform CAE and analyze performance. If we consider CAE procedure it is time consuming process which includes FEA model build, applying boundary conditions, running simulation and analyzing results which could take minutes to hours. By using ML with Physics AI we can make predictions on new design of the product in seconds and significantly save time and cost. To demonstrate the CAE acceleration process with physic AI we have solved two case studies. The first case study is head impact on hood where ML tool will predict deformation contour of the hood, acceleration and displacement curve of the impactor. The second case study is Tube crush analysis where prediction of tube deformation pattern, force and energy curve for different tube length and impact velocity is carried out. For both Case studies we have used TCS inhouse data to train test and prediction of the ML model. For Head impact case study, it gives lower training loss with more than 90 percent prediction accuracy. Similarly for tube crush study it gives good accuracy and predicts comparable behavior patten with CAE results. Physic AI ML tool accelerates the design and development cycle and can be utilized in different product development. Implementation of ML accelerates the CAE process in design and development of products. It saves a lot of time in multiple design iteration study. Similar method can be implemented for different CAE cases.
In the automotive industry, the perceived quality of a vehicle is heavily influenced by the ease and effort required to close its doors (which is governed by total door closing energy), particularly when all windows and other doors are closed. A major contributor to increased door closing energy is the air bind energy, a phenomenon caused by the rapid compression of trapped air within a sealed vehicle cabin during door closure. Studies have shown that this transient event leads to a significant rise in cabin pressure. This study presents a Computational Fluid Dynamics (CFD) method to evaluate the impact of air bind energy on door closing during the early stages of vehicle design. By simulating the cabin pressure dynamics during door closure, the research identifies key parameters influencing the air bind energy, such as door closing velocity, pressure relief valve and airflow escape paths. Other mechanical factors like hinge friction, check arm, and door seal etc. are excluded from the CFD model due to their complexity in simulation. The proposed CFD method is validated using three different vehicle models with varying door closing speeds. Cabin pressure vs time results are compared against physical tests conducted with the EZ-Slam measurement device. The strong correlation between CFD results and physical test data confirms the robustness of this method. Research findings highlight that optimizing the placement & size of pressure relief valve can significantly reduce the air bind energy, thereby lowering the total door closing energy. This is achieved by reducing cabin peak pressure and maximizing the rate at which peak pressure reaches atmospheric conditions. These insights are valuable for automotive engineers, to improve customer satisfaction through reduced door closing effort.
Audi has streamlined the A6 lineup. The automaker announced this past summer that the fancier-looking A7 (which was essentially an A6 glow up) was being pulled from the North American market. Now it's reduced the engine offerings from three to one. After a recent drive of the 2026 model, what's left under the hood was the best choice. But you might want to wait a few months if you're interested in the vehicle.
This paper carried out the fire failure analysis of valve-regulated lead-acid battery in communication equipment room. Through disassembly and observation of the battery and iron frame of battery cabinet in the area of fire origin, we obtained the key residual traces and used the physical and chemical analysis methods such as macroscopic/microscopic morphology, EDS, X-ray and metallographic, it was finally judged that the leakage of the battery electrolyte lead to the connection of the battery electrode plate and the iron frame and subsequently the electric heating fault caused the fire accident. Furthermore, we put forward some suggestions according to the existing problems, which may contribute to the prevention of similar failures.
Dooring accidents occur when a vehicle door is opened into the path of an approaching cyclist, motorcyclist, or other road user, often causing serious collisions and injuries. These incidents are a major road safety concern, particularly in densely populated urban areas where heavy traffic, narrow roads, and inattentive behavior increase the likelihood of such events. To address this challenge, this project presents an intelligent computer vision based warning system designed to detect approaching vehicles and alert occupants before they open a door. The system can operate using either the existing rear parking camera in a vehicle or a USB webcam in vehicles without such a feature. The captured live video stream is processed by a Raspberry Pi 4 microprocessor, chosen for its compact size, low power consumption, and ability to support machine learning frameworks. The video feed is analyzed in real time using MobileNetSSD, a lightweight deep learning object detection model optimized through TensorFlow Lite to ensure smooth and efficient processing even on resource- constrained hardware. Detected objects are classified, and the relative distance of approaching vehicles is estimated based on bounding box dimensions and simple geometric calculations. If a vehicle is detected within a predefined safety distance, the system immediately displays a clear visual warning on an in-vehicle screen, giving occupants enough time to delay opening the door and avoid a potential collision. The system was successfully implemented and tested on both a laptop and Raspberry Pi, demonstrating high accuracy, low latency, and minimal hardware requirements, making it cost effective and scalable. Looking forward, the design allows for future upgrades such as automatic door locking when a hazard is detected, audio and haptic alerts for greater situational awareness, integration with other vehicle sensors for improved detection accuracy, and seamless incorporation into commercial advanced driver assistance systems, providing a practical, affordable, and effective solution to enhance road safety and protect vulnerable road users.
Commercial success of the autonomous truck may be closer than we think. The last half decade has brought the best of times and worst of times for the commercial autonomous truck sector. While some perceived pillars of this technology have fallen, others have continued to carry the weight of bringing driverless trucks closer to commercialization. Consolidation was inevitable given the volume of speculative investment that brought a tidal wave of capital to various startups. Even so, some industry experts and Wall Street investors wondered if the autonomous truck sector might collapse entirely.
A mobile wireless charger is a device that charge a smartphone or other compatible gadgets without the need for physical cables. Principle of wireless mobile charger system based on inductive coupling phenomena. The main objective of this paper aims to address the challenge of packaging wireless mobile charger in peculiar door trim profile keeping overall functionality and aesthetic appearance of door trim intact. This paper deals with integration of a wireless charging system within the door trim of a vehicle to provide convenience and advanced functionality. The objective is to pack a wireless charger in door trim meeting the ergonomic target and equilibrium state stability while maintaining sleek and minimalist design of the door trim. The study focuses on innovative packaging solutions related to space optimization in door despite multiple challenges involved. Major challenge lies in packing the unit amidst complex mechanisms such as window regulators, speakers, structural reinforcements while managing the thermal heat generation with proper dissipation techniques The main objective of this paper is to address the following: An innovative approach to the design of Wireless charger for Door trim Meeting stable equilibrium state. Focusing on enhancing aesthetics. Low weight impact, robust design, and assembly, Managing Wireless charger quality quality as per regular standard.
This research analyzes the significance of air extractor on car door closing effort, especially within the context of highly sealed cabins. The goal is to measure their effectiveness in lowering pressure-induced resistance, study how the cut-out cross section and location affect performance, and its contribution to vehicle premium feel. Current vehicle design trends prioritize airtight cabin sealing for improving aerodynamic efficiency, NVH performance. This causes a problem in door closing operation. Air trapped while closing door creates transient pressure pulses. This pressure surge creates immediate discomfort to user i.e., Popping in Ears and requires high door closing force, and long-term durability problems in hinges and seals. In properly sealed cabins, air pressure resistance can contribute to 25% to 40% of total door closing force. Air extractors, usually installed in the rear quarter panels or behind rear bumpers, serve as pressure relief valves, allowing for a smoother airflow out of the cabin during such incidents. This passive system lowers door-closing effort, improves occupant experience, and safeguard structural components. A combination of CFD simulations, and real-world validations was employed to assess various air extractor configurations. Extractor size, location, flap design, and sealing levels of the vehicle were varied. Cabin pressure behavior and door closing force were evaluated under controlled and dynamic conditions. Comparative studies were also conducted across vehicle segments, including electric vehicles with high sealing requirements. Through these factors, this paper gives a holistic view to improve overall user experience as well as help to align with industry standards. The results have been backed with case studies as well as with simulation analysis to properly optimize the extractor design for new vehicles.
Side crashes are generally hazardous because there is no room for large deformation to protect an occupant from the crash forces. A crucial point in side impacts is the rapid intrusion of the side structure into the passenger compartment which need sufficient space between occupants and door trim to enable a proper unfolding of the side airbag. This problem can be alleviated by using the rising air pressure inside the door as an additional input for crash sensing. With improvements in the crash sensor technology, pressure sensors that detect pressure changes in door cavities have been developed recently for vehicle crash safety applications. The crash pulses recorded by the acceleration based crash sensors usually exhibit high frequency and noisy responses. The data obtained from the pressure sensors exhibit lower frequency and less noisy responses. Due to its ability to discriminate crash severities and allow the restraint devices to deploy earlier, the pressure sensor technology has gained its popularity for side crash applications. CAE based calibration approach reduces cost of multiple physical tests required for side airbag algorithm development to deploy the airbags. With a goal to achieve CAE based calibration such that side airbag deployment algorithms can be enhanced with the help of pressure sensors, Corpuscular Particle Method (CPM) was adopted to predict the pressure responses of side crash pressure sensors. The major challenge was to capture the change in pressure accurately in side door cavity during an event of side crashes in digital environment. In addition, the challenge was to develop robust CAE methodology that can predict sensible pressure responses during event of high speed as well as low speed side crashes. This paper describes the innovative CPM airbag based methodology developed to predict the pressure response and its correlation with side impact physical tests.
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