Browse Topic: Electronic equipment
As per Committee/Henry E. Harschburger recommendations
Low-level flight, defined by high-speed operations near terrain, represents a significant challenge in military rotorcraft missions while providing strategic advantages, such as radar evasion and heightened surprise. Recent conflicts highlight the urgent need for advanced low-level flight capabilities in the design of new rotorcraft. The close proximity to ground obstacles, combined with the complexities of piloting, necessitates precise control and robust handling qualities to prevent accidents. However, existing handling quality standards, such as MIL-DTL-32742, reveal limitations in assessing low-level maneuvers. Given the diverse array of new rotorcraft designs, driven by initiatives like the U.S. Army's Future Vertical Lift and NATO's Next Generation Rotorcraft Capabilities, a customized handling qualities evaluation for each design is impractical. In response, a performance-driven strategy has been implemented, scaling Mission Task Elements to align with aircraft performance
A cooperative flight test campaign between the US Army and NASA was performed. This test sought to characterize the acoustic emissions of a fully instrumented MD530F helicopter using a snapshot array and a phased array of microphones. The snapshot array of microphones aimed to provide even coverage across the surface of a hemisphere, providing an acoustic emission hemisphere in a single 'snapshot' of time. The phased array of microphones was designed to provide enough resolution to determine noise sources from each individual blade as well as perform source separation from main rotor and tail rotor emissions. Test conditions for the characterization effort were chosen using a traditional one-factor-at-a-time approach as well as three design of experiment approaches. Characterization conditions included constant speed level flight, descent, and ascent conditions. Transient maneuver conditions were also captured over the snapshot array. The vehicle instrumentation included measurements
Along with unique and challenging development concerns, target hardware deployment concerns exist for artificial intelligence (AI) and machine learning (ML) applications. Those deployment concerns should be addressed in the planning phase and consist of the issues surrounding the target hardware selection and the certifiability/qualifiable of the target hardware for the AI/ML model deployment. These concerns center around certification issues identified for multi-core processors (MCP), where those MCP issues are amplified for graphics processor units (GPUs) when they are used for general computing. While the use of complex graphics processors for general computing is being reconciled for flight critical applications, the reduction of these concerns is possible through design specific target hardware choices, e.g., selection of Field Programmable Gate Array (FPGA) devices or other certifiable approaches. This paper explores these concerns and proposes design specific target hardware
This study introduces three new proposed Mission Task Elements (MTEs) - "Big Air", "Giant Slalom", and "Super Combined" - aimed at evaluating handling qualities during low-level and high-speed flight profiles. These MTEs are designed to reflect operational task elements critical in military engagements, particularly where rotorcraft capabilities in evading radar detection and maneuvering at high speeds are paramount. Utilizing piloted simulations with four generic rotorcraft configurations under various flight control laws, the MTEs' effectiveness in exposing aircraft characteristics and handling deficiencies is systematically assessed. The evaluation, conducted with a diverse group of pilots, underscores the MTEs' relevance to real-world scenarios and their robustness in handling qualities assessment across different rotorcraft designs. The study reveals that while some configurations exhibit consistent Level 1 Handling Qualities Ratings (HQRs), others show varied performance
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.
In this work, a unified framework integrating global and local SHM methods for structural health monitoring (SHM) of rotorcraft structures is proposed. This framework integrates both "local" ultrasonic-guided wave-based and "global" vibration-based SHM schemes for tackling damage detection, identification, and quantification under uncertainty. The local SHM is completed by training a variation of variational auto-encoder (MMD-VAE) along with feed-forward neural networks (FFNN). The compressed latent space vector obtained during the training process is applied to achieve both signal reconstruction and state prediction. In terms of the global model, functionally pooled auto-regressive models with exogenous excitation (VFP-ARX) models are applied including to capture low-frequency vibrations. The complete experimental evaluation and assessment of the proposed framework are presented for an Airbus H125 helicopter blade under both low-frequency vibrations and ultrasonic guided waves for SHM
The paper deals with the status of development and qualification/certification of electromechanical actuation for Helicopters and VTOL applications with the focus on aspects relevant to the Fault-Tolerance. In particular a linear Electromechanical Actuator (EMA) architecture is presented, derived from a fault tolerant ballscrew-based differential (speed-summing arrangement) actuation system patented by UMBRAGROUP S.p.A. The focus is on safety-critical and high reliability/availability requirements for electromechanical actuation certification. The main characteristic is the use of two independent mechanical actuation channels in the same envelope driven by independent Motor Control Electronics (MCEs). At the state of the art, the presented fault-tolerant architecture is under development in flight-critical swashplate application for eVTOL platform and under feasibility study in flight-critical swashplate application for CS27 platform.
Advancing technology has driven continuous improvements across most aspects of human endeavors. In the time since the first modern helicopter flew in 1939, the world has seen inventions like the microwave, personal computers, cell phones, and the internet. If helicopters predate these society-changing innovations, then it stands to reason that the manner in which helicopters operate has drastically shifted as well. Specifically, this paper reviews historical concepts of operations (CONOPS) in rotorcraft aerial firefighting and analyzes where technology advancements have made an impact on firefighting operations and the performance of helicopters in suppressing fires. These shifts were evaluated using analytical assessments and highlighting snapshots in time of how capability impacted the aerial firefighting mission effectiveness. As companies innovate and technology advances, further benefits to rotorcraft CONOPS in aerial firefighting will be realized.
Launch, recovery, and deck handling operational performance on smaller ship platforms like Corvettes, Frigates and Destroyers are qualified as the most challenging tasks in the UAS ship-deployment of a VTOL Uncrewed Air System (UAS). One of the main hurdles is the random nature of seaway-created deck motions coupled with ship structure disturbed air wake patterns. The MoD has supported a range of work aimed at bringing Quiescent Period Prediction (QPP) technology to fruition. QPP firstly requires Wave Profiling RADAR to measure the sea wave system out to approximately 2km in the region around a vessel. Secondly these measurements are employed in a wave propagation model to predict the actual wave forces acting on a vessel. Using the wave predictions as inputs to a vessel model makes possible to predict the actual (deterministic as opposed to statistical) motions of a vessel. Wave systems naturally alternate groups of large waves with smaller waves, this property, combined with the
This paper describes a mathematical framework for determining the optimal sensor set location for adequately capturing the sound generated by rotors. The approach leverages the gappy-POD method proposed by Everson and Sirovich [J. Opt. Soc. Am., Vol. 12, 1995, pp. 1657-1664], which first identifies the various mode constituents that make up the first few rotor blade-pass frequency harmonics of the sound-field. The algorithm is developed using a covariance matrix for the POD problem comprising auto- and cross-spectral densities of spatially and temporally resolved sound waves captured by an array of microphones oriented parallel to the axis of a laboratory-scale hovering rotor. Three different forms of the technique are developed and compared. These comprise a homogeneous form and two heterogeneous forms; the heterogeneous forms are referred to as XX-topos and XX-chronos and depends on which term in the error minimization equation is assigned the gappy sensor set. A greedy algorithm is
Wear debris monitoring and analysis is a common practice for the condition assessment of engine and transmission health. Oil debris monitoring (ODM) and electronic chip detectors (ECD) are two common methods deployed for continuous monitoring of oil wetted component health in-flight. This study evaluates the diagnostic performance of the two sensing technologies within controlled rolling element bearing (REB) fault experiments. Progressive visual inspection of the REB spall progression through failure provided a ground truth against which both systems could be compared. Quantifiable metrics of reliability, diagnostic accuracy, provided maintenance interval were defined to create a framework for condition-based maintenance (CBM) program decision making. In summary, it was found that the ODM sensor system provided earlier fault notice, but more so, vastly outperformed the ECD in reliability and avoidance of false positives.
Sealed electronic components are the basic components of aerospace equipment, but the issue of internal loose particles greatly increases the risk of aerospace equipment. Traditional material recognition technology has a low recognition rate and is difficult to be applied in practice. To address this issue, this article proposes transforming the problem of acquiring material information into the multi-category recognition problem. First, constructing an experimental platform for material recognition. Features for material identification are selected and extracted from the signals, forming a feature vector, and ultimately establishing material datasets. Then, the problem of material data imbalance is addressed through a newly designed direct artificial sample generation method. Finally, various identification algorithms are compared, and the optimal material identification model is integrated into the system for practical testing. The results show that the proposed material
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