Browse Topic: Body structures
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.
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.
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.
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.
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.
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.
This paper proposes HaloBus, an innovative, edge-computing solution designed to mitigate this risk by detecting student boarding and exiting in real time using lightweight AI based methods. A persistent challenge in elementary school transportation is the issue of missing students after they exit their buses, which disproportionately impacts low-income households. Current safety systems place the burden of implementation on individual households, often requiring independent methods. Common methods include applications on a personal device or a small tracker. However, not everyone can afford these options, and ensuring child safety is a primary concern for parents and caregivers. That is why HaloBus was invented. The system employs YOLOv5us—an Ultralytics-enhanced, anchor-free, split-head architecture that offers a superior accuracy speed trade-off. By providing real-time, on-device alerts, HaloBus enables immediate intervention to prevent a student from being left behind, thereby shifting the focus from reactive post-incident response to proactive safety. Trained on over 70,000 labeled and unlabeled images, the model can accurately detect multiple students simultaneously, significantly reducing false positives. In real-world deployment, the model sustained 30 frames per second on the Raspberry Pi and achieved detection confidence levels exceeding 75% even when subjects wore sunglasses or hoodies. With opt-in participation for each family, HaloBus effectively balances detection efficiency and privacy protection. Overall, HaloBus offers a low-cost, scalable, and ethically conscious approach to enhancing school-bus safety by delivering reliable, on-device boarding and exit detection for multiple students in varied real-world conditions.
The timing of video recordings, along with the spatial positioning of objects, is a fundamental parameter for calculating the speed time history. If the task involves determining the average speed of an object moving at approximately constant speed, it may be acceptable to average the speed over several to a dozen frames, using the fps (frames per second) parameter as the basic time unit.. However, if the objective is to compute speed from individual frames, the reliability of the timing becomes crucial. Without access to DVR hardware documentation, proprietary algorithms, or software – and considering the frequent hardware modifications and software updates - the most effective way to solve the problem is through a reverse-engineering approach. This study discusses several aspects of timing analysis, including: (1) making a test recording of a calibrated LED lightboard; (2) analyzing the relationship between the lightboard time and the presentation time stamp (pts) extracted from the file metadata; (3) investigating frame skipping and frame timing errors due to frame rate changes; (4) modeling the composite motion of the rolling shutter and the lightboard LEDs; (5) identifying the DVR’s actual frame capture rate; and (6) compensating the timing of the evidentiary recording. Establishing the timing scheme of the test recording enables reliable speed analysis based on two or three adjacent frames of the evidentiary recording, as well as the determination of the velocity time history over a short segment of the recording.
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.
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