Digital Twin Approach for Component Health-Informed Rotorcraft Flight Parameter Optimization

Abstract

Rotorcraft components experience different stress levels based on the flight parameters and intensity of the rotorcraft mission, which in turn dictates the maintenance schedule as well as life expectancy for the component. To improve the resilience of a rotorcraft to safely complete a mission, the rotorcraft’s maneuvers can be designed to minimize the stress experienced by critical mechanical components. This paper discusses a methodology to optimize rotorcraft flight parameters to minimize stress on critical mechanical components based on their current and predicted states while satisfying the operational constraints. A digital twin approach is pursued to support probabilistic diagnosis, prognosis, and optimization. Comprehensive rotorcraft analysis and finite element models are used to predict the stress in the rotorcraft component under different flight conditions. Diagnostic information is used to estimate the health state and prognosis model error after every flight. Surrogate models are constructed for diagnosis and prognosis to increase the efficiency of the uncertainty and optimization analyses. The flight parameters are optimized for a future mission using the updated prognosis model by minimizing the stress in the component under uncertainty. The proposed methodology is demonstrated with a synthetic experiment for a simple flight path.