Browse Topic: Bodies and Structures
The scope of this SAE Recommended Practice is to promote compatibility between child restraint systems and vehicle seats and seat belts. Design guidelines are provided to vehicle manufacturers for certain characteristics of seats and seat belts and to child restraint system (CRS) manufacturers for corresponding CRS features so that each can be made more compatible with the other. The CRS accommodation fixture (see Figure 1) is used to represent a CRS to the designers of both the vehicle interior and the CRS for evaluation of each product for compatibility with the other. The features of the accommodation fixture are described as each is used.
This SAE Recommended Practice is intended to cover plastic safety glazing for use in motor vehicles and motor vehicle equipment. Nominal specifications for thickness, flatness, curvature, size, and fabrication details are presented principally for the guidance of body engineers and designers. For additional information on plastic safety glazing materials for use in motor vehicles and motor vehicle equipment, please refer to SAE J673.
This SAE Aerospace Recommended Practice (ARP) provides the user with standardized guidelines for the measurement of effective intensity of short pulse width strobe anticollision lights for aircraft in the laboratory, in maintenance facilities, and in the field. A common source of traceability for calibration of the measurement systems, compensation for known causes of variation in light output such as the use of colored lenses, and recommendations which minimize sources of errors and uncertainties are included in this document. Estimates of uncertainty and error sources for each class of measurement are discussed.
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
Weather-strip sealing systems are critical to automotive closure performance, influencing water- and dust-tightness, aerodynamic noise control, and overall NVH quality. Conventional validation often relies on flat or straight JIG-based tests that inadequately represent the curved, angled, and non-uniform geometries of real closures such as doors, tailgates, hoods, roofs, and fixed or movable glass. This disparity limits the predictive accuracy of sealing performance in actual vehicles. This study proposes a vehicle-integrated validation framework that mirrors true geometric and contact conditions. The methodology combines finite element analysis (FEA) of both flat JIG and full-vehicle CAD geometries with experimental JIG tests, establishing a baseline for pressure distribution, compression load, and sealing contact behavior. A comparative analysis highlights significant deviations between flat-section predictions and vehicle-specific closure profiles. Results demonstrate that the
The front wing of a Formula 1 car is one of the most important aerodynamic components in design development. Particularly, as it is the first to interact with the upcoming airflow, the aerodynamic flow structures generated will have a strong interaction with the remainder of the car’s components. In 2026, the Fédération Internationale de l’Automobile will introduce new regulations that incorporate new aerodynamic philosophies for the front wing, including active aerodynamics. This paper presents a design methodology study for the development of a Formula 1 2026 front wing, compliant with Issue 9 of the technical regulations. A computational-based, structured optimisation series was conducted to enhance the aerodynamic performance of a front wing concept with a focus on improving downforce, maximising efficiency, and enhancing trailing flow for the remainder of the car. The final front wing concept at 40%, running at 30 m/s, generated 189 N of downforce and 19 N of drag. Active
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
MSIL (Maruti Suzuki India Limited), India’s leading automotive manufacturer, offers a diverse range of SUVs (Sports Utility Vehicles) in its portfolio. Traditionally, SUVs are associated with an assertive stance and a commanding road presence; however, this bold design language often compromises aerodynamic drag performance. Over the past decade, demand for this segment has surged, while CAFE (Corporate Average Fuel Economy) regulations have become increasingly stringent. To address this growing market need, MSIL conceptualized a new SUV - Victoris - targeted to deliver best-in-class aerodynamic efficiency in MSIL SUV portfolio. This paper details the aerodynamic development process using CFD (Computational Fluid Dynamics) and full-scale WTT (Wind Tunnel Testing). Initially, the aggressive styling of Victoris negatively impacted drag performance. Strategic exterior surface refinements and integration of aero components enabled recovery of aerodynamic efficiency. Key interventions
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
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
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