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首页> 外文会议>AHS International technical specialist meeting on aerodynamics, acoustics, and test and evaluation >MODELING OF UH-60A HUB ACCELERATIONS WITH NEURAL NETWORKS
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MODELING OF UH-60A HUB ACCELERATIONS WITH NEURAL NETWORKS

机译：基于神经网络的UH-60A集线器建模

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摘要

Neural network relationships between the full-scale, flightrntest hub accelerations and the corresponding three N/revrnpilot floor vibration components (vertical, lateral, andrnlongitudinal) are studied. The present quantitative effortrnon the UH-60A Black Hawk hub accelerations considersrnthe lateral and longitudinal vibrations. An earlier studyrnhad considered the vertical vibration. The NASA/ArmyrnUH-60A Airloads Program flight test database is used. Arnphysics based "maneuver-effect-factor (MEF),” derivedrnusing the roll-angle and the pitch-rate, is used.rnFundamentally, the lateral vibration data show highrnvibration levels (up to 0.3 g’s) at low airspeeds (forrnexample, during landing flares) and at high airspeeds (forrnexample, during turns). The results show that thernadvance ratio and the gross weight together can predict thernvertical and the longitudinal vibration. However, thernadvance ratio and the gross weight together cannot predictrnthe lateral vibration. The hub accelerations and thernadvance ratio can be used to satisfactorily predict thernvertical, lateral, and longitudinal vibration. The presentrnstudy shows that neural network based representations ofrnall three UH-60A pilot floor vibration componentsrn(vertical, lateral, and longitudinal) can be obtained usingrnthe hub accelerations along with the gross weight and thernadvance ratio. The hub accelerations are clearly a factor inrndetermining the pilot vibration. The present conclusionsrnpotentially allow for the identification of neural networkrnrelationships between the experimental hub accelerationsrnobtained from wind tunnel testing and the experimentalrnpilot vibration data obtained from flight testing. Arnsuccessful establishment of the above neural networkrnbased link between the wind tunnel hub accelerations andrnthe flight test vibration data can increase the value ofrnwind tunnel testing.
机译：研究了满刻度飞行测试的轮毂加速度与相应的三个N / revrn飞行员地面振动分量（垂直，横向和纵向）之间的神经网络关系。当前的UH-60A黑鹰轮毂加速装置的定量动力考虑了横向和纵向振动。较早的研究已经考虑了垂直振动。使用了NASA / ArmyrnUH-60A空载计划飞行测试数据库。使用了基于Arnphysics的“机动效应因子（MEF）”（通过侧倾角和俯仰率得出）。rn从根本上讲，横向振动数据显示了低空速（例如降落时）的高振动水平（最大0.3 g）。结果表明，前进比和毛重一起可以预测垂直振动和纵向振动，但是前进比和毛重不能一起预测横向振动，轮毂加速度和研究表明，可以使用轮毂加速度和轮毂加速度来获得基于神经网络的UH-60A三种飞行员地面振动分量（垂直，横向和纵向）的神经网络表示。轮毂的加速度显然是决定飞行员振动的一个因素。本结论潜在地允许识别从风洞测试获得的实验轮毂加速度与从飞行测试获得的实验飞行员振动数据之间的神经网络关系。在风洞轮毂加速度和飞行测试振动数据之间建立上述基于神经网络的链接的成功建立可以增加风洞测试的价值。

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