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Browse AllThis SAE Aerospace Recommended Practice (ARP) defines recommended analysis and test procedures for qualification of pneumatically, electrically, manually, and hydraulically actuated air valves. They may be further defined as valves that function in response to externally applied forces or in response to variations in upstream and/or downstream duct air conditions in order to maintain a calibrated duct air condition (e.g., air flow, air pressure, air temperature, air pressure ratio, or air shutoff). Qualification testing performed on the airplane to verify compatibility of the valve function and stability as part of a complete system is outside the scope of this document. Refer to ARP1270 for design and certification requirements for cabin pressurization control system components. As this document is only a guide, it does not supersede or relieve any requirements contained in detailed Customer specifications.
This SAE Aerospace Recommended Practice (ARP) discusses design philosophy, system and equipment requirements, environmental conditions, and design considerations for rotorcraft environmental control systems (ECS). The rotorcraft ECS comprises that arrangement of equipment, controls, and indicators which supply and distribute dehumidified conditioned air for ventilation, cooling and heating of the occupied compartments, and cooling of the avionics. The principal features of the system are: a A controlled fresh air supply b A means for cooling (air or vapor cycle units and heat exchangers) c A means for removing excess moisture from the air supply d A means for heating e A temperature control system f A conditioned air distribution system The ARP is applicable to both civil and military rotorcraft where an ECS is specified; however, certain requirements peculiar to military applications—such as nuclear, biological, and chemical (NBC) protection—are not covered. The integration of NBC
This specification covers a corrosion-resistant steel in the form of investment castings homogenized and solution and precipitation heat treated to 180 ksi (1241 MPa) tensile strength.
The integration of Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) has transformed various industries, offering substantial benefits. The application of these technologies in engine reliability testing has immense potential as they offer real-time monitoring and analysis of engine performance parameters. Engine reliability testing is vital for ensuring the safety, efficiency, and longevity of engines. Traditional methods are time consuming, expensive, and rely heavily on manual inspection and data analysis. This paper shows how IoT and ML technologies can enhance the efficiency of engine reliability testing. The paper includes the following case studies:
The rapid evolution of intelligent transportation systems has made drivers’ attentiveness and adherence to safety protocols more critical than ever. Traditional monitoring solutions often lack the adaptability to detect subtle behavioral changes in real time. This paper presents an advanced AI-powered Driver Monitoring System designed to continuously assess driver behavior, fatigue, distractions, and emotional state across various driving conditions. By providing real-time alerts and insights to vehicle owners, fleet operators, and safety personnel, the system significantly enhances road safety. The system integrates lightweight AI/ML algorithms, image processing techniques, perception models, and rule-based engines to deliver a comprehensive monitoring solution for multiple transportation modes, including automotive, rail, aerospace, and off-highway vehicles. Optimized for edge devices, the models ensure real-time processing with minimal computational overhead. Alerts are communicated
Reducing drag forces and minimizing the rear wake region are the main goals of evaluating exterior aerodynamic performance in automobiles. Various literature and experiments shows that the overall fuel computations of the road vehicle improves significantly with the reduction in aerodynamic drag force. In the road vehicle major components of the drag is due the imbalance in pressure between front and rear of the vehicle. At high vehicle speed, aerodynamic drag is responsible for approximately 30 to 40% of the energy consumption of the vehicle. In the recent year, cost of high-performance computing (HPC) has reduced significantly, which helped computational fluid dynamics (CFD) is an affordable tool to the automotive industry for evaluating aerodynamic performance of the vehicle during developing phase. The vehicles aerodynamic performance is greatly impacted by the dynamic environmental conditions it encounters in the real world. Such environmental conditions are difficult to replicate
Pedestrian safety is a critical concern in India, where rapid urbanization, increased vehicular traffic, and inadequate infrastructure pose significant risks to pedestrians. This study aims to analyze pedestrian accidents across various regions in India, drawing insights from comprehensive accident data. By examining accident patterns, risk factors, and contributing variables, we seek to inform policy recommendations and enhance pedestrian safety measures.