Bayesian Sensitivity Analysis of Flight Parameters in a Hard-Landing Analysis Process

Abstract

A flight parameter sensor simulation model was developed to assess the conservatism of the landing gear component loads calculated using a typical hard-landing analysis process. Conservatism exists due to factors of safety that are incorporated into any hard-landing analysis process to account for uncertainty in the measurement of certain flight parameters. The flight parameter sensor simulation model consists of 1) an aircraft and landing gear dynamic model to determine the “actual” landing gear loads during a hard landing; 2) an aircraft sensor and data acquisition model to represent the aircraft sensors and flight data recorder systems to investigate the effect of signal processing on the flight parameters; and 3) an automated hard-landing analysis process, representative of that used by airframe and equipment manufacturers, to determine the “simulated” landing gear loads. Using a technique of Bayesian sensitivity analysis, a number of flight parameters are varied in the flight parameter sensor simulation model to gain an understanding of the sensitivity of the difference between actual and simulated loads to the individual flight parameters in symmetric and asymmetric two-point landings. This study shows that the error can be reduced by learning the true value of the following flight parameters: longitudinal tire–runway friction coefficient, aircraft vertical acceleration (related to vertical descent velocity), lateral acceleration (related to lateral velocity), Euler roll angle, mass, center of gravity position, and main landing gear tire type. It was also shown that, due to the modeling techniques used, shock absorber servicing state and tire pressure do not contribute significantly to the error.

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