Machine Learning for Rotor Components Clearance Evaluation in Tiltrotor Maneuvers Simulation

Evaluating rotor component clearances is a multidisciplinary process aimed at ensuring that no contact occurs between rotor parts during a rotorcraft's operational life. It begins with calculating relative distances between components across all possible displacements and deformations combinations using a rotor kinematic model, and ends with clearance verification through flight data analysis and simulation. This task requires coupling detailed rotor aeroelasticity with flight mechanics to predict deformation under load, which is computationally expensive and unsuitable for real-time use. This work proposes a machine learning–based alternative: a neural network to estimate rotor clearances from flight mechanics inputs, with a specific application demonstrated in a simulated tiltrotor emergency maneuver with a pilot in the loop. The trained model successfully captures nonlinear relationships between maneuver parameters and rotor structural response, providing accurate predictions with reduced computational cost. The proposed framework can support both preliminary and detailed rotorcraft design and offers potential for real-time load prediction in applications where conventional aeroelastic tools are computationally expensive.