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This 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.
AC-9 Aircraft Environmental Systems Committee
The intent of this report is to encourage that the thermal management system architecture be designed from a global platform perspective. Separate procurements for air vehicle, propulsion system, and avionics have contributed to the development of aircraft that are sub-optimized from a thermal management viewpoint. In order to maximize the capabilities of the aircraft for mission performance and desired growth capability, overall system efficiency and effectiveness should be considered. This document provides general information about aircraft Thermal Management System Engineering (TMSE). The document also discusses approaches to processes and methodologies for validation and verification of thermal management system engineering. Thermal integration between the air vehicle, propulsion system, and avionics can be particularly important from a thermal management standpoint. Due to these factors, this report is written to encourage the development of a more comprehensive system
AC-9 Aircraft Environmental Systems Committee
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
AC-9 Aircraft Environmental Systems Committee
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.
AMS F Corrosion and Heat Resistant Alloys Committee
Software-Defined Vehicles (SDV) are fostered through initiatives like SOAFEE and Eclipse SDV promoting the use of cloud-native approaches, distributed workloads and service-oriented architectures (SOA). This means that in these systems each vehicle is connected to the cloud and functions are executed both inside the vehicle and in the cloud. So far, there are no established solutions for monitoring and diagnosing SDVs. In designing these solutions, the cost-sensitive nature of every component inside a vehicle must be considered since it makes it unlikely that significant resources will be provided just for diagnostics. Therefore, conventional data centre monitoring approaches that usually rely on transferring large amounts of data to dedicated servers are not directly applicable in this scenario. To illustrate the challenges in providing new solutions for diagnosing and monitoring SDVs, a SOA that has been defined and studied in research projects is introduced. In this architecture
Böhlen, BorisFischer, Diana
Mechatronic systems, which are integral to various automotive applications, enhance both functional criticality and user experience. As the complexity and number of features in automotive systems increase, the volume of test cases for system-level features and their interactions grows exponentially. This necessitates rigorous regression testing with each software update to ensure system reliability and performance. The systems engineering V-model is a crucial framework for the design and development of complex systems, emphasizing the importance of testing at every level, including system, subsystem, and software. Effective validation at the system level involves numerous subsystems and their software interacting, making the testing process resource-intensive and time-consuming. During system-level testing, issues often arise that require fixes within various subsystems. After addressing these issues, retesting is necessary to ensure that the changes do not negatively impact overall
Sureka, SumitRawat, GautamGhosh, SoumikVidhu, Nandagopal
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:
Yadav, Sanjay KumarKumar, PrabhakarR, DineshJoon, SushantRai, AyushTripathi, Vinay Mani
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
Chikhale, ShraddhaSing, SandipHivarkar, UmeshMardhekar, Amogh
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
Chalipat, SujitBiswas, KundanTare, Kedar
As the air pollution level rises around the globe, the need for alternative sources of energy increases, and this need applies to automotive industry also. Commercial vehicles are one of the major sources of air pollution around the world as they have impactful applicability in our day to day life. With growing advancement in mobility solutions, commercial vehicles are undergoing transformation to improve efficiency, safety and performance. One of the emerging technologies is of torque vectoring which is a concept used to provide better traction and stability to the vehicle in different driving conditions and used in the vehicle having multi motor configuration. Advance torque vectoring concept coupled with electric motor can react to dynamic driving conditions by providing instant torque. The concept of torque vectoring can be useful for heavy commercial vehicles used in off-road applications such as mining because torque vectoring helps in better weight management, cornering
Agarwal, PranjalChaudhari, GiteshGangad, VikasPenta, Amar
In recent years many automotive cybersecurity relevant regulations have been released and some have already started to come into effect. Moreover, some other regulations will come into effect in the next few years. These regulations provide requirements and guidance to automotive organizations with different degree of specifics. In this paper, we review a number of different cybersecurity relevant regulations such as UNR 155, UNR 156, AIS 189, AIS 190, GB 44495, GB 44496, EU Cyber Resilience Act, and BIS Final Rule. We break down and categorize these regulations based on their scope and highlight key areas relevant to different teams within the organizations. These key areas include Cybersecurity Management System (CSMS), Software Update Management System (SUMS), secure software development and software supply chain security, continuous cybersecurity activities (monitoring, incident response), and vulnerability disclosure and management. We then map responsibilities from the
Oka, Dennis KengoVadamalu, Raja Sangili
Threat Analysis and Risk Assessment (TARA) is a continuous activity, acting as a foundation of cybersecurity analysis for electrical and electronics automotive products. Existing TARA methodologies in the automotive domain exhibits challenges due to redundant and manual processes, particularly in handling recurring common assets across Electronic Control Units (ECUs) and functional domains. Two primary approaches observed for performing TARA are Manual-Asset-Centric TARA and Catalogue-Driven TARA. Manual-Asset Centric TARA is constructed from scratch by manually identifying the assets, calculating risks by likelihood, and impact determination. Catalogue-Driven TARA utilizes the precompiled likelihood and impact against identified assets. Both approaches lack standardized and modular mechanisms for abstraction and reuse. This results in poor scalability, increased efforts, and difficulty in maintaining consistency across vehicle platforms. The proposed method in this research overcomes
Goyal, YogendraSinha, SwatiSutar, SwapnilJaisingh, Sanjay
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.
Howlader, AshimMehta, Pooja
Artificial Intelligence (AI) is radically transforming the automotive industry, particularly in the domain of passenger vehicles where personalization, safety, diagnostics, and efficiency. This paper presents an exploration of AI/ML applications through quadrant of the key pillars: Customer Experience (CX), Vehicle Diagnostics, Lifecycle Management, and Connected Technologies. Through detailed use cases, including AI-powered active suspension systems, intelligent fault code prioritization, and eco-routing strategies, we demonstrate how AI models such as machine learning, deep learning, and computer vision are reshaping both the user experience and engineering workflow of modern electric vehicles (EVs). This paper combines simulations, pseudo-algorithms and data-centric examples of the combined depth of functionality and deployment readiness of these technologies. In addition to technical effectiveness, the paper also discusses the challenges at field level in adopting AI at scale i.e
Hazra, SandipTangadpalliwar, SonaliKhan, Arkadip
The vertical dynamic stiffness and damping of a tyre are critical to ride comfort and overall dynamics, particularly for low-frequency excitations in urban and highway driving. As the tyres are the primary interface between the vehicle and the road, absorbing surface irregularities before the suspension engagement, precise tyre parametrization is essential for accurate ride models. This study investigates an experimental methodology characterizing the vertical dynamic behavior of pneumatic tyres using a Flat Trac test machine. Contrary to the conventional approaches that depend on intricate shaker rigs or frequency dependence function models, the proposed technique uses a realistic force displacement loop-based methodology which is appropriate for ride models. Dynamic stiffness is computed from slope of a linear regression fitted to force and displacements during vertical sinusoidal excitation. Damping is derived from hysteresis energy loss per cycle. The tests were conducted under
Duryodhana, DasariSethumadhavan, ArjunTomer, AvinashGhosh, PrasenjitMukhopadhyay, Rabindra
The Objective is to develop a testing load case which can assess vehicle electric parking brake (EPB) performance and durability at vehicle level in different project development phases. In current scenario the EPB become one of a primary feature available in many passenger vehicles helps customers to apply this secondary braking system to hold the vehicle when parked. So, it is particularly important to evaluate this feature close to RWUP for the vehicle service life and studying the result before vehicle launch. The test method should be capable of capturing failures related to physical concerns, electrical characteristics, actuation time, gradient vehicle hold, effectiveness during vehicle running and durability. The most important challenge in this test method development is it should simulate the actual sequence followed by user in field on vehicle. A completely automated test set up integrating PLC and COBOT with closed loop feedback developed and discussed in this paper. During
Dhanapal, M RVijayakumar, NarayananMahesh, BB, VenkatasubramanianArthanathan, Sankaranarayanan
Bogie suspension systems are becoming increasingly popular in tipper vehicles to enhance their performance and durability, especially in demanding environments like construction and mining areas [1]. Bolsters contribute significantly to the overall performance and durability of the bogie suspension systems of tipper vehicles by evenly distributing the loads across the whole suspension system. They act as shock absorbers and negate the impact caused by the rough terrains and heavy loads, thereby reducing stress on individual components and maintaining the structural integrity of the vehicle. Bolsters also help in improving the ride comfort and to maintain the position of the suspension system [2]. This study focuses on the comprehensive testing and evaluation of bolsters to understand their modes and displacement data derived from field data. The primary objective is to analyse the performance and behaviour of bolsters under various operational conditions. Critical manners of
V Dhage, YogeshKolage, Vikas
Turbochargers play a crucial role in modern engines by increasing power output and fuel efficiency through intake air compression, thereby improving volumetric efficiency by allowing more air mass into the combustion chamber. However, this process also raises the intake air temperature, which can reduce charge density, lead to detonation, and create emissions challenges—such as smoke limits in diesel engines and knock in gasoline spark-ignited (GSL) engines. To mitigate this, intercoolers are used to cool the compressed air. Due to packaging constraints, intercoolers are typically long and boxy, limiting their effectiveness, especially at low vehicle speeds where ram air flow is minimal. This study investigates the use of auxiliary fans to enhance intercooler performance. Two methodologies were adopted: 1D simulation using GT-Suite and experimental testing on a vehicle under different fan configurations—no fan, single fan, and dual fans (positioned near the intercooler inlet and outlet
Patra, SomnathHibare, NikhilGanesan, ThanigaivelGharte, Jignesh Rajendra
Crash test plays a very crucial role in determining the passenger safety along with driver safety in most modern vehicles. This has become a prominent factor for many buyers to choose a safe car. During crash test, many components tend to fail. Amongst them, the major safety critical component which hampers the drivability of a vehicle is Wheel and Tyre Assembly. With the introduction of low aspect tyres, the failure rate of these assemblies has increased. A very high importance is given to ensure these parts withstand the subject load as it is directly related to function of vehicle. Many methods are available to test the Wheel and Tyre assembly to ensure they pass the crash criteria. We have developed a novel test method which can simulate the crash pattern in the rig/bench level. The method employs a mechanical actuator which can be operated at designated load application to ensure the assembly undergoes the anticipated failure. The process is repeated with different types of
Medaboyina, HarshaVardhanSingh, Ram KrishnanSundaram, RaghupathiJithendhar, Ashokan
The automotive industry is rapidly transitioning towards Industry 4.0, transforming vehicle manufacturing. To achieve a lower carbon footprint, it is crucial to minimize raw material wastage and energy consumption. Reducing component wastage, lead time, and automating gear manufacturing are key areas. Gear micro-geometry inspection is vital, as variations affect service life and NVH (Noise, Vibration, Harshness). Despite standards for permissible errors, manual evaluation of gear microgeometry inspection is often needed. This subjective evaluation approach will have a possibility that a gear with undesired variations gets assembled into the product. These issues can be detected during NVH testing, leading to replacement of part and re-assembly thus increasing lead time. This generates a need for an automated system which could reduce the human intervention and perform gear inspection. The research aims to develop a deep learning-based model to eliminate the ambiguity of manual
Ramakrishnan, Gowtham RajBaheti, PalashPR, VaidyanathanDurgude, RanjitBathla, ArchanaR, GreeshmitaV, Rangarajan
Road accidents involving cut-in and sudden brake events on highways present major challenges to driver safety, often outpacing the response time of traditional Advanced Driver Assistance Systems (ADAS). The objective of this study is to predict potential collisions caused by cut-ins before ADAS intervention becomes necessary, allowing for earlier driver alerts and enhanced vehicle response. The proposed method employs machine learning and deep learning approaches, specifically Long Short-Term Memory (LSTM) networks, to forecast collision risks 0.5 to 3 seconds in advance. Synthetic data generation techniques are used to create rare but critical cut-in and braking scenarios, complementing real-world data from test vehicles and accident records. Key predictive features monitored include relative velocity, lateral velocity, and lane overlap, which provide dynamic indicators of imminent risk. Results show that the system achieves an average early warning time of 1.35 seconds in 40.206% of
Srivastava, RohanNayak, Apoorva S.Suvvari, Sai DileepSatwik, RahulBhattacharya, Abhinov
With the growing adoption of Advanced Driver Assistance Systems (ADAS) in the Indian automotive landscape, the need for effective Driver Monitoring Systems (DMS) has become increasingly critical. This paper presents the design, development, and validation of a Driver Distraction and Attention Warning System (DDAWS) tailored to Indian driving conditions. The proposed system integrates two key modules: Driver Attention Monitoring and Drowsiness Detection, using a high-resolution driver-facing camera to analyse head pose, facial landmarks, and behavioural cues. The drowsiness module incorporates metrics such as PERCLOS and Eye Aspect Ratio (EAR), evaluated against the Karolinska Sleepiness Scale (KSS). Recognizing the limitations of self-assessed scales like KSS in dynamic driving environments, the study compares algorithmgenerated KSS values with self-reported scores to assess model accuracy. Additionally, the framework aligns with automotive safety standards such as AIS184,EU 2021/1341
Verma, HarshalKale, Jyoti GaneshKarle, Ujjwala