Browse Topic: Aircraft instruments
Air data measurement and calibration are fundamental components in the pursuit of accurate and reliable aerodynamic assessments. The systematic collection of essential data regarding air properties are important for evaluating aircraft performance under various conditions and configurations. The scope is to achieve a comprehensive understanding of airflow characteristics, which is fundamental for design improvements and operational strategies, contributing to safer and more efficient flight operations in a several range of scenarios. This type of data measurement is even more challenging for the AW609 Tiltrotor which combines vertical take-off technology capabilities with the fixed-wing flight efficiency. The activity starts from known pitot-static system calibration methodologies for conventional applications and shows what were the difficulties encountered in a non-conventional Tiltrotor approach. The paper goes through the presentation of the original Pitot-Static and Air Data
Maintenance of spatial orientation (SO) is achieved primarily through visual information where the horizon and celestial reference cues or flight instruments are used by pilots to infer aircraft orientation. However, cross checking the instruments in degraded visual environments can be complicated by factors such as workload, distraction, and situations where the vestibular and proprioceptive systems may provide false and competing orientation information. We describe experiments measuring pilot performance using a flight simulator under challenging conditions where the sensory information was controlled. Reducing available visual instruments increased the task difficulty. A wearable vibrotactile array could provide concurrent, additional orientation information. Increasing the flying task segment difficulty increased the perceived workload and also corresponded to an increase in accidents. Adding tactile orientation information reduced the accident rate.
The hippocampus plays a crucial role in brain function and is one of the important areas of concern in closed head injury. Hippocampal injury is related to a variety of factors including the strength of mechanical load, animal age, and helmet material. To investigate the order of these factors on hippocampal injury, a three-factor, three-level experimental protocol was established using the L(3) orthogonal table. A closed head injury experiment regarding impact strength (0.3MPa, 0.5MPa, 0.7MPa), rat age (eight- week-old, ten-week-old, twelve-week-old), and helmet material (steel, plastic, rubber) were achieved by striking the rat's head with a pneumatic-driven impactor. The number of hippocampal CA3 cells was used as an evaluation indicator. The contribution of factors to the indicators and the confidence level were obtained by analysis of variance. The results showed that impact strength was the main factor affecting hippocampal injury (contribution of 89.2%, confidence level 0.01
In the aerospace industry, competition is high and the need to ensure safety and security while managing costs is paramount. Furthermore, stakeholders—who gain the most by working together—do not necessarily trust each other. Now, mix that with changing enterprise technologies, management of historical records, and customized legacy systems. This issue touches all aspects of the aerospace industry, from frequent flyer miles to aircraft maintenance and drives tremendous inefficiency and cost.Technology that augments, rather than replaces, is needed to transform these complex systems into efficient, digital processes. Blockchain technology offers collaborative opportunities for solving some of the data problems that have long challenged the industry.This SAE EDGE™ Research Report by Rhonda D. Walthall examines how blockchain technology could impact the aerospace industry and addresses some of the unsettled concerns surrounding its implementation.{"uri":[{"xlink:href":"https://www.sae.org
Spatial Disorientation (SD) mishaps account for the greatest loss of lives in both military and civilian aviation worldwide. When no mechanical cause of a mishap is identified, mishap investigators can use flight data recorder information to populate perceptual models with aircraft flight parameters in order to confirm or deny that pilot SD was the probable cause of the mishap. Current perceptual model weaknesses include the inability to analyze hover and hover-transition mishaps and not accounting for sensory inputs from the auditory and somatosensory systems. The authors have conducted in-flight helicopter perceptual threshold studies to extend the model envelop to include hover as well as a series of tactile cueing in-flight studies in fixed-wing aircraft to permit the inclusion of somatosensory information into the model. This expanded model, by including all sensory modalities, now provides a probable solution to prevention of SD mishaps by continuously maintaining spatial
FAA rotorcraft airworthiness regulations require calibration of pitot-static systems in all flight regimes. Of all methods commonly used, none has been applied in a manner showing full compliance, specifically in the takeoff phase and in determining CG (Center of Gravity) effects. A review of accepted Position Error Correction methods identifies the GPS-based true airspeed method, with an adapted execution and analysis technique, as the most practical in terms of equipment and efficiency to provide a complete airspeed system calibration. The level flight limitations of the GPS method are solved by a combination of flight profiles, continuous data recording and reduction technique. The GPS horseshoe method and the ORBIS constant turn radius method are expanded by varying the airspeed, altitude, and heading as required to provide an equation set solved for the wind components and true airspeed. The new variable parameter methods minimize wind variability effects and flight test time.
Rotorcrafts are generally subject to a higher fatal accident rate than other segments of aviation, including commercial and general aviation. The safety improvement for rotorcrafts would directly improve the efficiency of air traffic control, since rotorcrafts operate primarily within low-level airspace; an area that is becoming increasingly complex with new entrants, such as unmanned aircraft systems and urban air mobility. The recent impact of artificial intelligence and deep learning algorithms on various aspects of our lives has led to the investigation of the application of these algorithms in the aviation domain; as it may offer a prime opportunity to enhance safety within the aviation community. In this research, we explore the efficacy, reliability, and, more importantly, the explainability of modern deep learning algorithms. We use machine learning models to predict the attitude (pitch and yaw) of rotorcrafts using video data recorded with ordinary cameras. The cameras were
Over the last 100 years, the automobile has become integrated in a fundamental way into the broader economy. A broad and deep ecosystem has emerged, and critical components of this ecosystem include insurance, after-market services, automobile retail sales, automobile lending, energy suppliers (e.g., gas stations), medical services, advertising, lawyers, banking, public planners, and law enforcement. These components - which together represent almost $2 trillion of the U.S. economy - are in equilibrium based on the current capabilities of automotive technology. However, the advent of autonomous vehicles (AVs) and technologies like electrification have the potential to significantly disrupt the automotive ecosystem. The critical cog governing the rate and pace of this shift is the management of the test and verification of AVs. In this SAE EDGE™ report, six senior industry leaders in the impacted ecosystems essay articles which describe sectors of the current automotive ecosystem and
As the premier agency for promoting and insuring aviation safety, the Federal Aviation Administration (FAA) continues to promote and highlight the importance of participating in aviation Flight Data Monitoring (FDM) programs to improve flight safety and operational efficiency. Indeed, recorder safety is one of the agency's top 10 most wanted list of safety improvements in 2017-2018. The FAA, National Transportation Safety Board (NTSB), and the United States Helicopter Safety Team (USHST) are strong proponents of recorder use. These organizations and other industry partners are working together to implement a helicopter safety enhancement that promotes the use of flight data recorders as a mechanism to reduce the helicopter fatal accident rate. However, despite these best efforts to reduce the fatal accident rate with this lifesaving technology, barriers to implementation exist. These include initial costs of flight data recorders which can range from 9,000 - 50,000, on average. These
The CH146 Griffon helicopter is a Federal Aviation Administration (FAA) and Transport Canada certified commercial helicopter used by Canadian Armed Forces in military role, which is distinguished from the original design intent of the helicopter for commercial use. A regime based Structural Usage Monitoring (SUM) program is developed by Bell to meet the Canadian Government requirements documented in the Technical Airworthiness Manual (TAM). As part of the CH146 SUM, helicopter usage data recorded by the Flight Data Recorder (FDR) system is collected and flight conditions (regimes) are defined by using dedicated software tools. Helicopter usage is then compared with the baseline and by using the actual helicopter data on selected critical components and fatigue damage accumulation is performed. Finally, the calculated damage is evaluated to define recommended structural maintenance actions. Development of the CH146 SUM is completed and more than 50,000 flight hours accumulated FDR data
The effectiveness of using neural networks to predict rotor loads on the AW609 tilt-rotor is proven in this work. The main objective is to find a viable architecture for a neural network simple enough to be implemented in real time, with the aim to have a reliable prediction of rotor loads during telemetry monitoring sessions of flight test operations. The real time comparison of the loads predicted by the neural network with those measured by the aircraft instrumentation can provide immediate hints of incipient anomalies. A simple Feed Forward neural network has been tested, analyzing briefly the pros and cons of such a choice versus other possible architectures. The proposed neural network will estimate the bending loads (beam and chord) and the pitch link axial load, given the parameters that describe the aircraft trim point and how it is maneuvering. Instead of trying to estimate directly the time history of the loads, with all its associated dynamics, an approach based on a
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