The pitch links of rotor blades are essential hardware that provide direct control to rotorcraft. The pitch-link loads undergo large changes in magnitude as a result of flight conditions that range from those of relatively benign level flight to those associated with severe, complex maneuvers. In the present study, a ''complex'' maneuver was defined as one that involved simultaneous non-zero aircraft angle of bank (associated with turns) and aircraft pitch rate (associated with a pull-up or a push-over). Also, since a typical rotor blade pitch link operates in a highly dynamic environment, the pitch-link loads obtained from flight tests have associated with them a greater degree of uncertainty. Analytical prediction of pitch-link loads is thus difficult, and methods that provide accurate results are highly desirable. The objectives were (1) to obtain physical insights into the nature of complex maneuvers and (2) to apply neural networks to efficiently characterize maneuver-related rotorcraft blade pitch-link loads. The NASA/Army UH-60A Airloads Program database was used.

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