Browse Topic: Avionics
This article provides a comprehensive review of existing literature on AI-based functions and verification methods within vehicular systems. Initially, the introduction of these AI-based functions in these systems is outlined. Subsequently, the focus shifts to synthetic environments and their pivotal role in the verification process of AI-based vehicle functions. The algorithms used within the AI-based functions focus primarily on the paradigm of deep learning. We investigate the constituent components of these synthetic environments and the intricate relationships with vehicle systems in the verification and validation domain of the system. In the following, alternative approaches are discussed, serving as complementary methods for verification without direct involvement in synthetic environment development. These approaches include data-oriented methodologies employing statistical techniques and AI-centric strategies focusing solely on the core deep learning algorithm.
This SAE Aerospace Recommended Practice (ARP) provides an algorithm aimed to analyze flight control surface actuator movements with the objective to generate duty cycle data applicable to hydraulic actuator dynamic seals.
Sensata Technologies' booth at this year's IAA Transportation tradeshow included two of the company's Precor radar sensors. The PreView STA79 is a heavy-duty vehicle side-monitoring system launched in May 2024 and designed to comply with Europe-wide blind spot monitoring legislation introduced in June 2024. The PreView Sentry 79 is a front- and rear-monitoring system. Both systems operate on the 79-GHz band as the nomenclature suggests. PreView STA79 can cover up to three vehicle zones: a configurable center zone, which can monitor the length of the vehicle, and two further zones that can be independently set to align with individual customer needs. The system offers a 180-degree field of view to eliminate blind spots along the vehicle sides and a built-in measurement unit that will increase the alert level when turning toward an object even when the turn indicator is not used. The system also features trailer mitigation to reduce false positive alerts on the trailer when turning. The
There are certain situations when landing an Advanced Air Mobility (AAM) aircraft is required to be performed without assistance from GPS data. For example, AAM aircraft flying in an urban environment with tall buildings and narrow canyons may affect the ability of the AAM aircraft to effectively use GPS to access a landing area. Incorporating a vision-based navigation method, NASA Ames has developed a novel Alternative Position, Navigation, and Timing (APNT) solution for AAM aircraft in environments where GPS is not available.
This SAE Aerospace Information Report (AIR) contains information on the thermal design requirements of airborne avionic systems used in military airborne applications. Methods are explored which are commonly used to provide thermal control of avionic systems. Both air and liquid cooled systems are discussed.
Aerospace manufacturers are leveraging multicore processors and modularity to design smarter cockpit displays and avionic computers that are smaller and capable of supporting more applications from a single line replaceable unit (LRU). Some are also starting to embed more of the processing required to enable cockpit display applications within the display itself, rather than having it enabled by an associated LRU. The development of new electric vertical takeoff and landing (eVTOL) aircraft and avionics companies changing their approach to the development of safety critical computers and aircraft networking technologies are some of the aerospace industry factors driving this design trend. In the U.S., the Department of Defense (DoD) embracing the Modular Open Systems Approach (MOSA) across the purchase of all new aircraft technologies is influencing design changes in cockpit displays and aircraft computers as well.
The intent of this AIR is twofold: (1) to present descriptive summary of aircraft nosewheel steering and centering systems, and (2) to provide a discussion of problems encountered and “lessons learned” by various airplane manufacturers and users. This document covers both military aircraft (land-based and ship-based) and commercial aircraft. It is intended that the document be continually updated as new aircraft and/or new “lessons learned” become available.
Over the last decade, a government-industry effort to advance the use of modern computer processors and networks in spacecraft avionics systems has quietly been making its own gains in industry adoption and a re-thinking of the way next generation spacecraft electronic systems could be designed in a less costly and more interoperable way. Spacecraft avionics systems are all of the electronic instruments, components, computers and subsystems that control primary spaceflight flight and data communications functionality.
Recent advancements of electric vertical take-off and landing (eVTOL) aircraft have generated significant interest within and beyond the traditional aviation industry, and many novel applications have been identified and are in development. One promising application for these innovative systems is in firefighting, with eVTOL aircraft complementing current firefighting capabilities to help save lives and reduce fire-induced damages. With increased global occurrences and scales of wildfires—not to mention the issues firefighters face during urban and rural firefighting operations daily—eVTOL technology could offer timely, on-demand, and potentially cost-effective aerial mobility capabilities to counter these challenges. Early detection and suppression of wildfires could prevent many fires from becoming large-scale disasters. eVTOL aircraft may not have the capacity of larger aerial assets for firefighting, but targeted suppression, potentially in swarm operations, could be valuable. Most
This SAE Aerospace Standard (AS) defines minimum performance standards (MPS) for fuel flowmeters, fuel flow indicators, and fuel flow transmitters. The fuel flow indicators and transmitters are intended for use in 14 CFR Part 23, 25, 27, and 29 aircraft equipped with reciprocating and turbine engines. Multiple function displays are not within the scope of this SAE Aerospace Standard (refer to AS6296).
This SAE Aerospace Information Report (AIR) provides the hydraulic and flight-control system designer with the various design options and techniques that are currently available to enhance the survivability of military aircraft. The AIR addresses the following major topics: a Design concepts and architecture (see 3.2, 3.5, and 3.6) b Design implementation (see 3.3, 3.6, and 3.7) c Means to control external leakage (see 3.4) d Component design (see 3.8)
This Technical Governance is part of the SAE UCS Architecture Library and is primarily concerned with the UCS Architecture Model (AS6518) starting at Revision A and its user extensions. Users of the Model may extend it in accordance with AS6513 to meet the needs of their UCS Products. UCS Products include software components, software configurations and systems that provide or consume UCS services. For further information, refer to AS6513 Revision A or later. Technical Governance is part of the UCS Architecture Framework. This framework governs the UCS views expressed as Packages and Diagrams in the UCS Architecture Model.
This aerospace recommended practice provides a framework and suggested procedures or values for requirements for the design, performance, and test of hydraulically powered servoactuators for use in aircraft flight control systems. The original version of this document was intended for military usage: consequently, the requirements still often reflect such use. However, the basic requirements of this ARP may and should be applicable to commercial usage as well, provided that appropriate considerations are given for the applicable FAR/JAR 25 regulations, hydraulic fluids, and environmental conditions.
Over the past few decades, aircraft automation has progressively increased. Advances in digital computing during the 1980s eliminated the need for onboard flight engineers. Avionics systems, exemplified by FADEC for engine control and Fly-By-Wire, handle lower-level functions, reducing human error. This shift allows pilots to focus on higher-level tasks like navigation and decision-making, enhancing overall safety. Full automation and autonomous flight operations are a logical continuation of this trend. Thanks to aerospace pioneers, most functions for full autonomy are achievable with legacy technologies. Machine learning (ML), especially neural networks (NNs), will enable what Daedalean terms Situational Intelligence: the ability to understand and make sense of the current environment and situation but also anticipate and react to a future situation, including a future problem. By automating tasks traditionally limited to human pilots - like detecting airborne traffic and identifying
With the advancement of automotive industries, the need for wireless connectivity between vehicle and smartphone is increasing. To meet the demand for wireless connectivity, Bluetooth plays a vital role. Testing Bluetooth systems is challenging and complex when development cycles of the system involve multiple partners. The system under test must fulfil consumers expectation of Bluetooth functionality paired with their personal devices. Despite many advances and existence of a few reliable systems, hardware limitation, and lack of standardization in Bluetooth test system are some of the prolonged issues. Throughout the course, various capabilities and existing traditional Bluetooth testing system practice were researched, which majorly at a system level (Black box). The gap of such testing is the escape of defect which involves the interoperability of multiple profiles like AVRCP, HFP, and A2DP. This paper focuses on a reliable testing approach which is based on packet level testing
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