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This specification covers a corrosion-resistant steel in the form of investment castings.
AMS F Corrosion and Heat Resistant Alloys Committee
This specification covers an aircraft-quality, low-alloy steel in the form of bars, forgings, mechanical tubing, and forging stock.
AMS E Carbon and Low Alloy Steels Committee
This specification covers an aluminum alloy in the form of sheet and plate 0.008 to 4.000 inches (0.20 to 101.6 mm), inclusive, in thickness (see 8.5).
AMS D Nonferrous Alloys Committee
This specification covers a corrosion- and heat-resistant steel in the form of bars, wire, forgings, mechanical tubing, flash-welded rings, and stock for forging or flash-welded rings.
AMS F Corrosion and Heat Resistant Alloys Committee
This test procedure provides a standard method for evaluating the side stand retraction performance of a side stand/motorcycle combination.
Motorcycle Technical Steering Committee
The processes addressed in this AIR apply to the acquisition and validation of dynamic total-pressure and distortion data from CFD models simulating turbulent flows in inlets. The results of these processes can be used in the formation of an inlet-flow-distortion methodology that addresses turbine-engine operability assessments.
S-16 Turbine Engine Inlet Flow Distortion Committee
This SAE Recommended Practice establishes harmonized test methods for measuring volatile organic compound (VOC) emissions from polyurethane foam materials used in automotive interior applications. This recommended practice complements SAE J2989 by providing standardized emission collection methods, analytical procedures, and reporting formats to ensure consistent and comparable results across different testing laboratories and organizations. The methods discussed in this recommended practice include micro-scale chambers, small-scale chambers, bag methods, thermal extraction techniques, and bottle methods for aldehyde determination. This standard addresses the unique challenges presented by polyurethane foam materials, including their high surface area, absorption capacity, and sensitivity to environmental conditions. The selection of the appropriate test method shall be primarily determined by customer requirements or OEM specifications, as these requirements often dictate the specific test protocol needed for material approval or compliance. When customer requirements are not specified, the selected method should be based on available laboratory capabilities, sample size constraints, testing timeline requirements, and the intended use of the results. This recommended practice provides guidance on the appropriate conditions and limitations for each method to ensure consistent and comparable results across different testing approaches. The standardization of these testing methodologies is essential for reducing the variability currently observed across the automotive industry. By providing clear guidance on specimen preparation, test conditions, analytical procedures, and reporting formats, this standard aims to facilitate meaningful comparison of results, reduce testing costs, and improve product development and quality control efforts. This recommended practice applies to both molded polyurethane foam components such as seating cushions and backrests, and slab stock foam materials used in automotive interior applications. The guidance in this document is intended to reduce ambiguity, improve reproducibility, and provide comparable results across different testing facilities.
Volatile Organic Compounds
This SAE Recommended Practice is intended to establish a procedure to certify the low mu/winter driving skill levels of professional drivers. This certification can be used by the individual driver to qualify their skills when seeking employment or other professional activity. These certification levels may also be used by test facilities or other organizations when seeking test or professional drivers of various skills. This document provides directions for obtaining certification through Probitas Authentication®1 and the low mu/winter driving skill examination requirements. This document is a supplement to SAE J3300, providing information specific to the low mu/winter driving skill certification and clarifying the application of the rules set forth in SAE J3300 to the low mu/winter driving certification. While the references, definitions, rules, and guidelines presented in SAE J3300, Sections 1 through 5 apply to the low mu/winter driving certification, they are not repeated in this document.
Driving Skills Standards Committee
This SAE Aerospace Standard (AS) establishes the requirements for externally swaged tube-fitting assemblies used in aircraft fluid systems in the following pressure classes: B (1500 psi or 10500 kPa), and D (3000 psi or 21000 kPa), and in temperature types I (-65 to 160 °F or -55 to 70 °C), and II (-65 to 275 °F or -55 to 135 °C) of AS2001. This specification covers a common Cres, titanium, and aluminum fittings that may be used for a range of operating pressures up to 3000 psi with different tubing materials and tubing wall thicknesses, and is assembled with the same tooling in accordance with AS5902. Table 11 shows applicable aerospace fitting part number standard and tubing materials and operating pressures.
G-3, Aerospace Couplings, Fittings, Hose, Tubing Assemblies
This specification covers a corrosion- and heat-resistant cobalt-chromium-molybdenum alloy in the form of bars 2.500 to 4.000 inches (63.50 to 101.60 mm) inclusive, diameter or least distance between parallel sides.
AMS F Corrosion and Heat Resistant Alloys Committee
This test method provides a guidance for determining the total free play between the ball and outer ring of a spherical bearing when measured in both the radial and axial directions. Bearings covered by this test method include all plain spherical-type bearings, both self-lubricated (lined) and metal-to-metal.
ACBG Plain Bearing Committee
This document describes [motor] vehicle driving automation systems that perform part or all of the dynamic driving task (DDT) on a sustained basis. It provides a taxonomy with detailed definitions for levels of driving automation, ranging from no driving automation (Level 0) to automated driving under all conditions in which humans can drive, with human driving not needed (Level 5), in the context of [motor] vehicles (hereafter also referred to as “vehicle” or “vehicles”) and their operation on roadways: Level 0: No driving automation Level 1: Driver support for steering OR speed, with continual driver supervision necessary and driver intervention when needed Level 2: Driver support for steering AND speed, with continual driver supervision necessary and driver intervention when needed Level 3: Automated driving under defined conditions, with human driving needed following an alert or evident vehicle malfunction Level 4: Automated driving under defined conditions, with human driving not needed to mitigate risk Level 5: Automated driving under all conditions in which humans can drive, with human driving not needed. The simple level descriptors have been changed to improve understanding of the differences among levels, but these are NOT the definitions of the levels of driving automation. See the definitions of each automation level in Sections 4 and 5 for explanation of these changes. These level definitions, along with additional supporting terms and definitions provided herein, can be used to describe the full range of driving automation features equipped on [motor] vehicles in a functionally consistent and coherent manner. “On-road” refers to publicly accessible roadways (including parking areas and private campuses that permit public access) that collectively serve all road users, including cyclists, pedestrians, and users of vehicles with and without driving automation features. The levels apply to the driving automation feature(s) that are engaged in any given instance of on-road operation of an equipped vehicle. As such, although a given vehicle may be equipped with a driving automation system that is capable of delivering multiple driving automation features that perform at different levels, the level of driving automation exhibited in any given instance is determined by the feature(s) that are engaged. This document also refers to three primary actors in driving: the (human) user, the driving automation system, and other vehicle systems and components. These other vehicle systems and components (or the vehicle in general terms) do not include the driving automation system in this model, even though as a practical matter a driving automation system may actually share hardware and software components with other vehicle systems, such as a processing module(s) or operating code. The levels of driving automation are defined by reference to the specific role played by each of the three primary actors in performance of the DDT and/or DDT fallback. “Role” in this context refers to the expected role of a given primary actor, based on the design of the driving automation system in question and not necessarily to the actual performance of a given primary actor. For example, a driver who fails to monitor the roadway during engagement of a Level 1 adaptive cruise control (ACC) system still has the role of driver, even while they are neglecting it. Active safety systems, such as electronic stability control (ESC) and automatic emergency braking (AEB), and certain types of driver assistance systems, such as lane keeping assistance (LKA), are excluded from the scope of this driving automation taxonomy because they do not perform part or all of the DDT on a sustained basis, but rather provide momentary intervention during potentially hazardous situations. Due to the momentary nature of the actions of active safety systems, their intervention does not change or eliminate the role of the driver in performing part or all of the DDT, and thus are not considered to be driving automation, even though they perform automated functions. In addition, systems that inform, alert, or warn the driver about hazards in the driving environment are also outside the scope of this driving automation taxonomy, as they neither automate part or all of the DDT, nor change the driver’s role in performance of the DDT (see 8.13). It should be noted, however, that crash avoidance features, including intervention-type active safety systems, may be included in vehicles equipped with driving automation systems at any level. For automated driving system (ADS) features (i.e., Levels 3 to 5) that perform the entire DDT, crash mitigation and avoidance capability is part of ADS functionality (see also 8.13). Note that this document provides a taxonomy and definitions and is not a safety standard. The document is not intended to provide guidance for safe vehicle operation by the driving automation system.
On-Road Automated Driving (ORAD) Committee
A numerical study on the influence of annular gap variation in correctly expanded sonic coaxial jets, focusing on its effect on mixing characteristics and jet symmetry, is presented in this paper. The computational simulations were conducted using a three-dimensional steady-state compressible Reynolds-Averaged Navier–Stokes (RANS) framework with the Spalart–Allmaras (SA) turbulence model. Both symmetric (uniform gap) and asymmetric (nonuniform gap) configurations were simulated. Eccentricity was introduced by offsetting the secondary nozzle by 2 mm downward from the center of the primary nozzle. In symmetric configurations with uniform annular gaps, the jet exhibited balanced shear-layer development, uniform entrainment, and symmetric Mach decay characteristics. However, the asymmetric annular gap configuration exhibited approximately 25–30% earlier potential core breakdown, 30–35% greater radial jet spreading, and nearly 6–10% faster centerline velocity decay compared with the symmetric configuration. The streamline analysis revealed enhanced entrainment, localized recirculation regions, asymmetric vortex generation, and accelerated momentum diffusion caused by unequal shear-layer interaction. These results demonstrate that annular gap asymmetry can serve as an effective passive flow control strategy for enhancing jet mixing and directional momentum redistribution. Such configurations may be useful in practical applications including exhaust gas dilution, fuel–air mixing enhancement in combustors, thrust vectoring, and jet-noise suppression systems.
Chandra Bose, GurusamySudalaimuthu, Ganesan
Rising vehicle complexity and electrification increase the thermal loads on automotive components, making reliable temperature models essential for ensuring thermal operational safety over the vehicle lifetime. Existing approaches (experimental wind tunnel testing, numerical simulation, and purely data-driven methods) lack scalability to many operating conditions, do not provide physically interpretable parameters, or yield inconsistent results when applied across multiple experiments. This paper addresses the gap of fitting a single, physics-constrained temperature model simultaneously across multiple experimental measurements, enabling consistent parameter estimation and prediction of unseen operating conditions. A lumped parameter thermal network (LPTN) is parameterized using a global minimization approach that classifies each model coefficient as global, discrete-global, or local, depending on whether it is shared across all measurements, across a subset with the same design configuration, or varies individually. The method is evaluated on an electronic control unit (ECU) installed in the BMW 7 Series, using nine wind tunnel measurements covering three different cooling strategies (ventilation, heat pipe, metal insert). A single global model fitted to six measurements achieves a root-mean-square error (RMSE) of 1.09 K, while three unseen measurements are predicted with an RMSE of 1.19 K. Compared to conventional single-measurement fitting, global estimation reduces convergence time to 21.2%, while yielding physically interpretable and consistent parameters across experiments. These results demonstrate that global LPTN parameter estimation provides a fast, robust, and physically interpretable framework for automotive thermal operational safety, capable of reliable extrapolation to unseen conditions with sparse experimental data.
Kehe, MaximilianEnke, WolframRottengruber, Hermann
The objective of this study was to evaluate the in-use emissions and energy consumption of similar model internal combustion engine (ICE) and battery electric vehicles (BEVs) in Canada. For the ICE vehicles (ICEVs), carbon dioxide (CO2) emissions were measured at the tailpipe. For the BEVs, the carbon intensity of different energy sources was used along with vehicle energy consumption to estimate the in-use CO2 equivalent (CO2e) emissions. Three ICEVs, the Ford Transit, Ford F-150, and Nissan Versa, and three BEVs, the Ford E-Transit, Ford F-150 Lightning, and Nissan LEAF, were tested over standard test cycles on a chassis dynamometer. The Nissan Versa, Nissan LEAF, Ford F-150, and Ford F-150 Lightning were tested at two temperatures, 25°C and −7°C, to investigate the effect of colder temperatures on emissions and energy consumption. The Ford Transit 150 and E-Transit were tested at two test weights, 2722 kg (6000 lb) and 3629 kg (8000 lb), to study the effects of cargo loading on emissions and energy consumption. In most conditions, the BEV use-phase CO2e emissions were found to be lower than those of the ICEVs. Results showed a significant increase in both emissions in ICEVs (up to 20%) and energy consumption in BEVs (up to 78.5%) at −7°C when compared to 25°C. Results also showed the significant effect of the carbon intensity of electricity on the CO2e emissions of BEVs, where more carbon-intensive electricity grids resulted in higher BEV CO2e emissions, even surpassing ICEV CO2 emissions in certain cold-temperature conditions.
Araji, FadiHumphries, KieranHornung, JeremyShantz, Emory
This study compares two international loudness standards—International Organization for Standardization (ISO) 532-1:2017 (Zwicker) and ISO 532-3:2023 (Moore–Glasberg–Schlittenlacher)—for assessing vehicle wind noise using wind tunnel measurement data. The two methods demonstrate good agreement in ranking wind noise quietness at typical highway speeds (120–140 km/h). However, discrepancies arise under specific conditions due to ISO 532-3’s binaural processing, which accounts for interaural inhibition effects. These differences are particularly pronounced in vehicles with asymmetric wind noise levels between the driver and passenger sides. Power-law regression analysis reveals that binaural loudness at the driver side (ISO 532-3) increases at a slower rate with wind speed compared to the monaural measurements [driver outboard ear (DOE)] of ISO 532-1. Under yaw angle conditions, the perceptual contribution of the driver inboard ear (DIE) introduces subtle metric-dependent variations. Contribution analyses further highlight method-specific sensitivities to noise source location and direction. Overall, this study provides a valuable reference for implementing loudness metrics in automotive wind noise engineering.
Hou, Hangsheng
With continuous advancements in load-side resources such as distributed photovoltaic systems, electric vehicles, and virtual power plants, the low-carbon and sustainable development attributes of power systems have been significantly enhanced. Meanwhile, the coupling intensity between sustainable power systems and meteorological conditions has been further consolidated. Considerable impacts are exerted by weather variations, particularly extreme weather events, on the dispatching and operation of sustainable power systems. Accurate load forecasting is critical for enabling sustainable power systems operators to optimize power generation strategy, ensuring supply stability and resilience against extreme weather-induced disruptions. However, the intrinsic non-stationarity and volatility of extreme weather events present significant challenges to conventional forecasting approaches. Herein, we introduce a hybrid algorithm integrating Newton–Raphson-based optimizer (NRBO) with extreme gradient boosting (XGBoost) to enhance short-term load predictions under such conditions. The model uses optimally selected meteorological and load features as inputs, while NRBO systematically tunes XGBoost’s hyper-parameters to maximize performance. Evaluated on an Irish dataset, the proposed framework is quantitatively compared against five baseline models, including traditional decision trees and neural networks. The case studies show that the mean absolute percentage error (MAPE) of the proposed model is 2.57%, which is the lowest among these decision tree and neural network algorithms.
Wang, YiZhou, JianWu, GangMa, TiannanMa, RuiguangHe, ChuanZhu, Huixian
G-3, Aerospace Couplings, Fittings, Hose, Tubing Assemblies
G-3, Aerospace Couplings, Fittings, Hose, Tubing Assemblies
G-3, Aerospace Couplings, Fittings, Hose, Tubing Assemblies
Rollovers are among the most severe road crashes, often leading to high fatalities and significant property damage, as reported by government and insurance agencies. This study investigates the impact of curve geometry and loading conditions on the rollover stability of a two-axle truck using validated vehicle dynamics simulations. The research highlights the importance of providing adequate curve radii and shows that larger radii are required to ensure design consistency. The study reveals that a 1 cm increase in center-of-gravity height results in a 0.82% decrease in the margin of safety against rollover, and that loading the truck to 93.75% of its full capacity over an equivalent platform length is the most critical loading condition in terms of rollover stability. To enhance safety, predictive models for lateral acceleration are developed along with geometric design consistency evaluation criteria based on vehicle rollover stability. Design guidelines for consistent curve design are also proposed. These models and criteria guide strategic improvements in road geometry, including optimized placement of rollover caution signage and targeted infrastructure refinements. The study underscores the need for enhanced curve design standards to improve truck stability and driver comfort while providing essential tools for advancing highway safety and mitigating rollover risks for heavy vehicles.
Remya, Y. K.Jacob, AnithaSubaida, E. A.
J1979 DBCJ1979DBC_2026099/16/2026
The SAE J1979 DBC file contains decoding rules for converting raw J1979 data to 'physical values' (Mph, %, etc.). This file lets you easily decode data from heavy duty vehicles (trucks, buses, tractors, etc.). This DBC file download includes: The SAE J1979 DBC file with Includes 2,400+ Parameter Group Numbers (PGNs) and 16,000+ Suspect Parameter Numbers (SPNs), derived from J1979-2 released in September 2026. One legal license (1 user, 1 PC) matching the DA license DECODE J1979: Convert J1979 data in wide range of software/API tools REVIEW FIRST: Use our CAN ID converter to check if your PGNs are covered CROWD INPUT: Benefit from free corrections based on large user base SAVE HOURS: Avoid manually constructing the DBC file from scratch Improved Accuracy & Reliability A fully standardized DBC file ensures precise signal decoding, eliminating errors and ensuring reliable data interpretation. Interoperability Seamlessly compatible with many different software stacks, enabling frictionless adoption and significantly expanding market reach. Partnership with Vector Informatik GmbH Works seamlessly with Vector’s free software (CANdb++), used by over 90% of the industry, with free download link provided on SAEI’s J1979DBC file landing page. What is a DBC file? A DBC file is a standardized method for storing the "rules" on how to interpret raw CAN bus data. It contains details on what 'signals' (e.g. RPM, Vehicle Speed, …) are contained within which 'messages' (i.e. CAN IDs). In the J1979 standard, messages are referred to as Parameter Group Numbers (PGN) and signals as Suspect Parameter Numbers (SPN). Further, a DBC file includes names, descriptions, positions, and lengths of the signals - as well as how to offset & scale them.