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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
In today's dynamic driving environments, reliable rear wiping functionality is essential for maintaining safe rearward visibility. This study sharing the next-generation rear wiper motor assembly that seamlessly integrates the washer nozzle, delivering improved performance alongside key benefits such as better Buzz, Squeak, and Rattle (BSR) characteristics, reduced system complexity, cost savings, and enhanced perceived quality. This integrated design simplifies the hose routing which improves the compactness and the efficiency of the design. This also enhances the spray coverage and minimizes the dry wiping unlike the traditional systems that position the washer nozzle separately. A non-return valve (NRV) is incorporated to eliminate spray delays ass it maintains consistent water flow giving cleaning effectiveness. Since this makes the nonfunctional parts completely leak proof due to the advanced sealing, it increases the durability and reliability in long run. As this proposal offers
Dhage, PrashantK, NagarajanG, Sabari Rajan
State Transport Units (STUs) are increasingly using electric buses (EVs) as a result of India's quick shift to sustainable mobility. Although there are many operational and environmental benefits to this development, like lower fuel prices, fewer greenhouse gas emissions, and quieter urban transportation, there are also serious cybersecurity dangers. The attack surface for potential cyber threats is expanded by the integration of connected technologies, such as cloud-based fleet management, real-time monitoring, and vehicle telematics. Although these systems make fleet operations smarter and more efficient, they are intrinsically susceptible to remote manipulation, data breaches, and unwanted access. This study looks on cybersecurity flaws unique to connected passenger electric vehicles (EVs) that run on India's public transit system. Electric vehicle supply equipment (EVSE), telematics control units (TCUs), over-the-air (OTA) update systems, and in-car networks (such as the Controller
Mokhare, Devendra Ashok
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
The modern vehicle is no longer a mechanical appliance—it has transformed into a software-defined cyber-physical system, integrating OTA updates, cloud-connected diagnostics, V2X services, and telematics-driven personalization. While this evolution promises unprecedented value in consumer experience and fleet operations, it also surfaces a dramatically expanded and evolving attack perimeter, especially across safety-critical ECUs and communication buses. Cyber vulnerabilities have shifted from isolated IT threats to real-time, embedded exploits. Controller area network (CAN), the backbone of vehicle bus systems, remains intrinsically insecure due to its lack of authentication and encryption, making it highly susceptible to message injection and denial-of-service by low-cost tools. Similarly, OEM implementations of BLE-based passive entry systems have proven vulnerable to replay and spoofing attacks with minimal hardware. In the Indian context, the transition to connected mobility is
Shah, RavindraAwasthi, Vibhu VaibhavKarle, Ujjwala
The legislation of CEV Stage V emission norms has necessitated advanced Diesel Particulate Filter calibration strategies to ensure optimal performance across diverse construction equipment applications in the Indian market. Considering the various duty cycles of cranes, backhoe loaders, forklifts, compactors, graders, and other equipment, different load conditions and operational environments require a comprehensive strategy to enhance DPF efficiency, minimize regeneration frequency, and maintain compliance with emission standards. The DPF, as an after-treatment system in the exhaust layout, is essential for meeting emission standards, as it effectively traps particulate matter. Regeneration occurs periodically to burn the soot particles trapped inside the DPF through ECU management. Therefore, understanding soot loading and in-brick DPF temperature behavior across various applications is key. This paper explores the challenges in DPF calibration for CEV Stage V and provides a
Mohanty, SubhamChaudhari, KuldeepakPatil, LalitMahajan, AtishMadhukar, Prahlad
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
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
This paper explores the implementation of ISO 21434 Automotive Cybersecurity Assurance Levels (CAL), focusing on enhancing component level cybersecurity for a vehicle. CAL values, which range from 1 to 4, provide a metric for ensuring that assets are protected against relevant threats at various phases of the product life cycle. By identifying parameters in the attack feasibility rating and their severity early in the product life cycle, specifically during the concept phase of ISO 21434, organizations can determine the CAL values. The CAL value serves as a benchmark to determine the level of severity required during the design, development and verification phases of the product life cycle. This paper outlines a method to establish CAL values as per ISO 21434 guidelines. The proposed methodology includes a detailed analysis of threat modeling, which is crucial for identifying and mitigating potential cybersecurity risks. By conducting threat modeling, organizations can systematically
Ghosh, SubhamKhader Batcha, Jashic
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
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
To develop a Test Method & Procedure for validating the Tractor clutch system performance & Wear simulation endurance test. Tractor clutch wear simulation test conducted along with transmission by operating clutch in different modes as per RWUP operation. In this test we can validate clutch field failures in short time with improved test accuracy at lab. In one of M&M technology project, Transmission Wet clutch system for higher HP tractors where we don’t have any dedicated test rig/methodology for validating Clutch wear & related failure simulation at lab
D, YashwanthRaja, RUdayakumar, SM, JeevaharanVijayakumar, Narayanan
Predictive maintenance is critical to improving reliability, safety and operational efficiency of connected vehicles. However, classic supervised learning methods for fault prediction rely heavily on large-scale labeled data of failures, which are difficult to obtain and maintain a manually built dataset of failure events in real automotives settings. In this paper, we present a novel self-supervised anomaly detection model that makes predictions on the faults without the need for labeled failures by using only the operational data when the systems or robots are healthy. The method relies on self-supervised pretext tasks, like masked signal reconstruction and future telemetry prediction, to extract nominal multi-sensor dynamics (i.e., temperature, pressure, current, vibration) while jointly minimizing the deviation between encoded/decoded signals and normal patterns in the latent space. A unsupervised anomaly detection model is then used to detect when the learned patterns are violated
Kumar, PankajDeole, KaushikHivarkar, Umesh
Bogie frame is a main skeleton and structural member in railway system which is carrying all the loads such as Suspensions, Axles, wheels, car body, Motor, Gear box etc. The frame is subjected an exceptional and service stresses in Vertical, Longitudinal, Lateral and twist directions throughout the service life which should be withstand for a life span of 30 years without failure. The purpose of this project is to determine the Structural integrity of the Metro rail bogie frame in consideration with EN13749 standard. This paper is the outcome of bench testing of metro rail bogie frame with the application of multiaxial loading in static and dynamic campaign through which stress data is collected with strain gauge sensors and correlated with the FEA results at initial design phase. This helps to verify and evaluate the design and validate the quality of metro rail frame as per the requirement specified in EN13749:2021 European standard in early design stages.
Tormal, Uday BapuraoSinnarkar, NitinShinde, Vikram
The rising software complexity in Automotive industry demands reusable, hardware-agnostic development frameworks. AUTOSAR (Automotive Open System Architecture) provides a standardized, scalable ECU software architecture but cost-effective tooling and modern workflows are critical for broad adoption and competitiveness. One such area is for AUTOSAR configuration and authoring of Autosar architecture. Current solutions include commercial offerings built by vendors on top of ARTOP (ArTOP is an eclipse-based ecosystem maintained by AUTOSAR consortium) and open-source python implementations. Commercial tools are prohibitive in cost, have complicated development workflows, are difficult to automate and lack quick integration with other tools. Python-based solutions are often community driven with small developer teams and face challenges. These tools are not mature enough, have staggered development, security concerns, liability issues, lack of approvals and other similar issues. These
Daware, KartikGarg, MuditPasupuleti, Raju
Automotive systems are increasingly adopting data-driven and intelligent functionality in the areas of predictive maintenance, virtual sensors and diagnostics. This has led to a need for the AI models to be directly run on vehicle ECUs. However, most of these ECUs – especially those in cost-sensitive or legacy platforms lack the computational capacity and parallel processing support required for standard AI implementations. Given the stringent real-time and reliability requirements in automotive environments, deploying such models presents a unique challenge. This paper proposes a practical methodology to optimize both the training and deployment phases of AI models for low-computation ECUs that operate without parallelism. Designing lightweight model architectures, using pruning and quantization techniques to minimize resource utilization, and putting in place a strategy appropriate for single-threaded execution are the three main objectives of the developed approach. The goal is to
Sharma, SahilMathew, Melvin John
Artificial Intelligence (AI) in the automotive industry is growing and transforming into different segments of the industry. Still there is a significant gap persisting in the standardization of design principles and the incorporation of manufacturing constraints in the AI CAD system. However current development in AI CAD systems isolated and non-parametric way, in contrast the conventional way of CAD methodology is knowledge based and systematic parametric steps which are agile to the iterative improvement. Hence it will be challenging in integration and adoption of these AI CAD systems in the well-established product development cycle. The research focuses on identifying the scope of AI integration which includes generative design, automated error detection, and design pattern-dependent learning systems, but also stresses the importance of standardized policies to address fundamental questions of system coherence, uniformity, and broad applicability. This research paper studies the
Shaikh, TahaHarel, SamarthKumar, AkarshVenkitachalam, MuthukumarShah, BhumikaChakraborty, Pinka
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
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