Browse Topic: Test procedures
This SAE Recommended Practice provides minimum performance requirements and uniform procedures for fatigue testing of wheels intended for normal highway use and temporary use on passenger cars, light trucks, and multipurpose vehicles. For heavy truck wheels and wheels intended to be used as duals, refer to SAE J267. For wheels used on trailers drawn by passenger cars, light trucks, or multipurpose vehicles, refer to SAE J1204. These minimum performance requirements apply only to wheels made of materials included in Tables 1 to 4. The minimum cycles noted in Tables 1 through 4 are to be used on individual test and a sample of tests conducted, with Weibull Statistics using two parameter, median ranks, 50% confidence level, and 90% reliability, typically noted as B10C50.
This SAE lab test procedure should be used when performing the following specialized weathering tests for wheels; Florida Exposure, QUV, Xenon and Carbon Weatherometer. In addition to these procedures, some additional post-weathering tests may be specified. Please refer to customer specifications for these requirements.
This SAE Aerospace Recommended Practice (ARP) is written for individuals associated with the ground-level testing of large and small gas turbine engines and particularly for those who might be interested in constructing new or adding to existing engine test cell facilities.
This document establishes a standardized test method designed to provide stakeholders—including runway deicing/anti-icing product manufacturers, users, regulators, and airport authorities—with a means of evaluating the relative ice penetration capacity of runway deicing and anti-icing products over time. The method measures ice penetration as a function of time, thereby enabling comparative assessments under controlled conditions. While commonly applied to runway treatments, these products may also be used on taxiways and other paved surfaces. The test is not intended to provide a direct measurement of the theoretical or extended ice penetration time of liquid or solid deicing/anti-icing products. Instead, it offers a practical and reproducible basis for performance evaluation, supporting operational decision-making and regulatory compliance.
Automotive OEMs can derive significant cost savings by reducing the quantity of physical crash tests and thereby accelerate product development, when they follow the Euro NCAP Virtual Testing procedure. It helps in optimizing the overall vehicle development process via more efficient simulations, as well as facilitates in early adoption of new safety regulations. In this pursuit, companies must comply with strict Euro NCAP requirements, which includes transparency and traceability of virtual tests. A major challenge therein is model validation – which requires highly precise detailing and extensive use of data for accurately replicating real physics of the problem. Deploying these workflows into an existing simulation process can be a complicated and time-consuming task, particularly when integrating various simulation and testing methods. A powerful simulation process and data management system (SPDM) can thereby assist companies to automate their entire simulation process, ensures
Nowadays, digital instrument clusters and modern infotainment systems are crucial parts of cars that improve the user experience and offer vital information. It is essential to guarantee the quality and dependability of these systems, particularly in light of safety regulations such as ISO 26262. Nevertheless, current testing approaches frequently depend on manual labor, which is laborious, prone to mistakes, and challenging to scale, particularly in agile development settings. This study presents a two-phase framework that uses machine learning (ML), computer vision (CV), and image processing techniques to automate the testing of infotainment and digital cluster systems. The NVIDIA Jetson Orin Nano Developer Kit and high-resolution cameras are used in Phase 1's open loop testing setup to record visual data from infotainment and instrument cluster displays. Without requiring input from the system being tested, this phase concentrates on both static and dynamic user interface analysis
This study investigates the phenomenon of receptacle icing during Compressed Natural Gas (CNG) refueling at filling stations, attributing the issue to excessive moisture content in the gas. The research examines the underlying causes, including the Joule-Thomson effect, filter geometries, and their collective impact on flow interruptions. A comprehensive test methodology is proposed to simulate real-world conditions, evaluating various filter types, seal materials and moisture levels to understand their influence on icing and flow cessation. The findings aim to offer ideas for reducing icing problems. This will improve the reliability and safety of CNG refueling systems.
The HVAC (Heating, Ventilation, and Air conditioning) system is designed to fulfil the thermal comfort requirement inside a vehicle cabin. Human thermal comfort primarily depends upon an occupant’s physiological and environmental condition. Vehicle AC performance is evaluated by mapping air velocity and local air temperature at various places inside the cabin. There is a need to have simulation methodology for cabin heating applications for cold climate to assess ventilation system effectiveness considering thermal comfort. Thermal comfort modelling involves human manikin modeling, cabin thermal model considering material details and environmental conditions using transient CAE simulation. Present study employed with LBM (Lattice-Boltzmann Method) based PowerFLOW solver coupled with finite element based PowerTHERM solver to simulate the cabin heat up. Human thermal comfort needs physiological modelling; thus, the in-built Berkeley human comfort library is used in simulation. Human
The automotive industry is rapidly evolving with technologies such as vehicle electrification, autonomous driving, Advanced Driver Assistance Systems (ADAS), and active suspension systems. Testing and validating these technologies under India’s diverse and complex road conditions is a major challenge. Physical testing alone is often impractical due to variability in road surfaces, traffic patterns, and environmental conditions, as well as safety constraints. Virtual testing using high-fidelity digital twins of road corridors offers an effective solution for replicating real-world conditions in a controlled environment. This paper highlights the representation of Indian road corridors as digital twins in ASAM OpenDRIVE and OpenCRG formats, emphasizing the critical elements required for realistic simulation of vehicle, tire, and ADAS performance. The digital twin incorporates detailed 3D road profiles (X-Y-Z coordinates), capturing the geometry and surface variations of Indian roads. The
As the brain and the core of the electric powertrain, the traction inverter is an essential part of electric vehicles (EVs). It controls the power conversion from DC to AC between the electric motor and the high-voltage battery to enable effective propulsion and regenerative braking. Strong and scalable inverter testing solutions are becoming more essential as EV adoption rises, particularly in developing nations like India. In India, traditional testing techniques that use actual batteries and e-motors present several difficulties, such as significant safety hazards, inadequate infrastructure, expensive battery prices, and a shortage of prototype-grade parts. This paper presents a comprehensive approach for traction inverter validation using the AVL Inverter TS™ system incorporating an advanced Power Hardware-in-the-Loop (PHiL) test system based on e-motor emulation technology. It enables safe, efficient, and reliable testing eradicating the need for actual batteries or mechanical
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