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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
As the automotive industry moves from conventional function oriented embedded ECU-based systems to Code-driven system, the core electrical and electronic (E&E) architecture is also being redesigned to support more software-driven functionality. Modern and centralized architectures promise scalability and software-driven flexibility, but they also introduce significant challenges in power distribution—an area that remains underexplored despite its critical role in overall vehicle safety and performance. Our paper aims at the adoption of the traditional power distribution approach for Next Gen vehicle architecture. It requires a fresh look at how power is distributed. In a novel E&E architecture, a single power harness supplies battery voltage to each zone. If there's a failure or voltage drop, it can affect multiple functions within that zone at once, and management of voltage regulation, thermal dissipation, and EMI/EMC compliance becomes crucial. Adding to the complexity, safety
Borole, AkashWarke, UmakantChakra, PipunJaisankar, Gokulnath
Software-Defined Vehicles (SDVs) are changing the automotive landscape by separating hardware from software and enabling features like over-the-air updates, advanced control strategies, and real-time decision-making. To support this transformation, EV powertrain systems require high-performance computing (HPC) platforms capable of real-time control, data processing, and cross-domain communication. This paper introduces a fully SDV-compatible EV powertrain architecture designed with NXP S32G3 domain controller. This processor supports multiple core having lockstep. It is designed for zonal control and automotive functional safety. The proposed designed uses the automotive Ethernet as an alternate option for CAN based communication to fulfill the bandwidth and timing requirement of today’s SDV applications. Hence it allows gigabit data transfer, Time Sensitive Networking (TSN) and also provides low latency across SDV control domain. Through secure real time interface with the vehicle’s
Pawar, GaneshInamdar, Sumer DeepakKumar, MayankDeosarkar, PankajTayade, NikhilKanse, DattatrayChopade, Vipul
The Vehicle software is moving towards software-centric architectures and hence software-defined vehicles. With this transition, there is a need to handle various challenges posed during development and validation. Some of the challenges include unavailability of hardware limiting the evaluation of various hardware options, board bring-up and hence leading to delays in software development targeted for the hardware, eventually leading to delayed validation cycles. To overcome the above challenges, we present in this whitepaper a virtual ECU (vECU) framework integrated with a CI/CD pipeline. A Virtual ECU (Electronic Control Unit) is a software-based emulation of a physical ECU. The adoption of virtual ECUs empowers development teams to commence software development prior to the availability of physical hardware. Multiple tools are available to demonstrate virtual ECUs, for example, QEMU, Synopsys, QNX Cabin, etc. vECU setup, when paired with a CI/CD pipeline, allows continuous
Singh, JyotsanaShaikh, ArshiyaMane, RahulBurangi, Piyush
Edge Artificial Intelligence (AI) is poised to usher in a new era of innovations in automotive and mobility. In concert with the transition towards software-defined vehicle (SDV) architectures, the application of in-vehicle edge AI has the potential to extend well beyond ADAS and AV. Applications such as adaptive energy management, real-time powertrain calibration, predictive diagnostics, and tailored user experiences. By moving AI model execution right into edge, i.e. the vehicle, automakers can significantly reduce data transmission and processing costs, ensure privacy of user data, and ensure timely decision-making, even when connectivity is limited. However, achieving such use of edge AI will require essential cloud and in-vehicle infrastructure, such as automotive-specific MLOps toolchains, along with the proper SDV infrastructure. Elements such as flexible compute environments, deterministic and high-speed networks, seamless access to vehicle-wide data and control functions. This
Khatri, SanjaySah, Mohamadali
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
The automotive industry is undergoing a transformational shift with the addition of Virtual ECU in the development of software and validation. The Level 3 Virtual ECU concept will lead to the transformation in the SDLC process, as early detection of defects will have a significant impact on cost and effort reduction. This paper explains the application of a Level 3 virtual ECU which can enable to perform testing in initial period considering the Shift Left Strategy, which will significantly reduce development time. This paper demonstrates various development and validation strategies of virtual ECU and how it can impact project timeline.
Bhopi, AmeySengar, Bhan
Software Defined Vehicles (SDV), Software Defined Networks (SDN), Software Defined (Power) Grids (SDG) are just a few examples of how the Software Defined Transformation is unfolding across many industries today (collectively being referred to as Software Defined X – SDX). This paper defines a maturity model for Software Defined Transformation and evaluates different industries including Automotive on their evolution so far. This cross-industry view of SDX helps in analyzing where SDV’s could be headed. A 2020 paper [1] lays out the complexity of the automotive software, with companies pursuing several directions in this transformation. The automotive industry has not yet reached a consensus on the direction it is taking on SDV. While companies like Tesla are already making software centric cars, traditional OEMs like General Motors, Toyota, Ford etc. are making huge investments and redefining their business models, tech stacks and operations to leverage the power of software. There is
Mathur, Akshay RajMisra, AmitMakam, Sandeep
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
The automotive industry produces a vast amount of multilingual textual data ranging from technical manuals to diagnostic reports that demand efficient summarization and reliable semantic reasoning. At present, the traditional large language models (LLMs) operating at the token level struggle not only with cross-lingual understanding and domain-specific reasoning but also are prone to hallucinations, leading to inaccurate insights and responses [2, 5]. This paper introduces a Unified Concept Model (UCM) architecture for the automotive domain that processes language at the concept level using multilingual, modality-agnostic embeddings, enabling coherent cross-lingual summarization and reasoning. The UCM encodes entire sentences as semantic vectors by leveraging the SONAR embedding space, a multilingual, modality-agnostic sentence representation that supports over 200 languages. This approach to encoding facilitates a deeper understanding across language boundaries and complex technical
Singh, SamagraRavi, UtkarshVikram, PrateekShenoy, LakshmiAwasthi PhD, Anshuman
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
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
Bilateral Cruise Control (BCC) is a new concept that has been shown to reduce traffic congestion and enhance fuel/energy efficiency compared to Adaptive Cruise Control (ACC). BCC considers both lead and trailing vehicles to determine the ego vehicle’s acceleration, effectively damping any disturbance down the vehicle string and reducing possibilities for congestion. Despite the advantages demonstrated with BCC, one major limitation is its non-intuitive behavior, which stems from the fact that the BCC reacts not just to the lead vehicle but also to the trailing vehicle’s movement. This paper identifies key issues with BCC control and proposes solutions that retain the benefits of BCC while maintaining intuitive behavior. Specifically, a novel switching strategy is proposed to switch between ACC and BCC control modes by critically analyzing the driving conditions. The proposed system ensures acceptable driving behavior with predictable braking and acceleration, resulting in an intuitive
A, AryaA, AishwaryaD, Vishal MitaranM, Senthil VelKumar, Vimal
This study investigates the concentrations of PM2.5 and PM10 inside an automobile under real-world driving conditions, one of the most polluted cities globally. India faces severe air pollution challenges in many cities, including Delhi, which are consistently ranking among the most polluted cities in the world. Major contributors to this pollution include vehicular emissions, industrial activities, construction dust, and biomass burning. Exposure to PM2.5 and PM10 has been linked to numerous adverse health effects, including respiratory and cardiovascular diseases, aggravated asthma, decreased lung function, and premature mortality. PM2.5 particles, being smaller, can penetrate deeper into the lungs and even enter the bloodstream, causing more severe health issues. In big cities like New Delhi, long driving times exacerbate exposure to these pollutants, as commuters spend extended periods in traffic. Measurements were taken both inside and outside the vehicle to assess the real-world
Gupta, RajatPimpalkar, AnkitPatel, AbhishekKumar, ShubhamJoshi, RishiKumar, Mukesh
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
The inertial profiler methodology is traditionally employed in RLDA (Road Load Data Acquisition) to measure road profiles and classify test routes into ISO road classes. However, this approach demands significant time and effort during instrumentation. Also, during data acquisition, laser height sensor data is affected especially during adverse conditions such as rainy seasons or on surfaces with improper reflectivity. Additionally, substantial resources are required for data processing to convert raw measurements into road classifications. To address these challenges, an initial attempt was made to establish a relationship between axle acceleration responses and road profiles, enabling axle acceleration measurements during RLDA to predict ISO road classes. However, this approach relied on a simple linear model that considered only axle acceleration responses, rendering the predictions susceptible to inaccuracies due to varying parameters such as vehicle speed. To overcome these
P, Praveen KumarP, DayalanSriramulu, Yoganandam
In recent years, virtual models have been extremely helpful in predicting potential injury risk to occupants in vehicle crashes. Virtual models offer detailed occupant anthropometry and closest possible bio-fidelity over existing test devices. This study focuses on the assessment of chest deflections in frontal thorax impacts using virtual human body models of a few anthropometries and transforming the assessment of injuries for a broader range of anthropometries (sections of the population). The study utilizes machine learning to enable injury assessment across a wide range of body types. A standard test scenario (Kroell load case) with a frontal blunt thoracic impact is considered for this study. Results from physical tests and simulations from various finite element human body models (HBMs) from literature are used to train supervised machine learning models. The combination of virtual simulation and machine learning reduces the reliance on physical prototypes and expands the reach
Sridhar, RaamArya, BibhuDivakar, PrajwalR, Udhaya KumarBhutki, PrasadKumar, DevendraKurkuri, MahendraMohan, Pradeep
David Martin, CBMM Asia Bernardo Barile, CBMM Europe BV Caio Pisano, CBMM Europe BV Automotive high strength steels have specific microstructure-dependent forming characteristics. Global formability is generally associated with high uniform strain values which imply good drawability and stretch forming properties driven by pronounced work hardening. Local formability on the other hand is often measured by various fracture strain values—generally higher in single phase steels. In this respect, the so-called ‘local/global formability map’ concept has been established not only to provide a comprehensive methodology to characterize existing automotive steels but also to enable improvement strategies toward more balanced forming characteristics. Niobium (Nb) microalloying is a powerful tool to achieve both property improvement in general and property balance in particular. More than two decades of research has demonstrated that Nb-induced microstructural optimization is applicable to HSLA
Barile, Bernardo
Aluminum alloy wheels have become the preferred choice over steel wheels due to their lightweight nature, enhanced aesthetics, and contribution to improved fuel efficiency. Traditionally, these wheels are manufactured using methods such as Gravity Die Casting (GDC) [1] or Low Pressure Die Casting (LPDC) [2]. As vehicle dynamics engineers continue to increase tire sizes to optimize handling performance, the corresponding increase in wheel rim size and weight poses a challenge for maintaining low unsprung mass, which is critical for ride quality. To address this, weight reduction has become a priority. Flow forming [3,4], an advanced wheel rim production technique, which offers a solution for reducing rim weight. This process employs high-pressure rollers to shape a metal disc into a wheel, specifically deforming the rim section while leaving the spoke and hub regions unaffected. By decreasing rim thickness, flow forming not only enhances strength and durability but also reduces overall
Singh, Ram KrishnanMedaboyina, HarshaVardhanG K, BalajiGopalan, VijaysankarSundaram, RaghupathiPaua, Ketan