On genetic algorithm and artificial neural network combined optimization for a Mars rotorcraft blade

Abstract

The Mars rotorcraft can help the Mars rover to optimize the driving route and explore more efficiently. However, the thin and cold Martian atmosphere can heavily increase the consumed power of the rotorcraft blades and reduce the generated thrust, leading to the reduction in power loading. The aerodynamic force generated by the Mars rotorcraft blade can be significantly influenced by the blade structure, which has several main structural characteristics and needs to be well designed. A multivariable optimization design method of blade structure is proposed based on neural network, genetic algorithm, and computational fluid dynamics simulations. Experiments conducted in the Martian atmosphere simulator indicate that the Mars rotorcraft blades with the hyperbolic chord length distribution and twist have higher power loading in the constant collective pitch angle. • A model was built to predict rotor hover performance at low-Reynolds-number. • Established an optimization model of the planform of the Mars rotorcraft blade. • The optimized blade consumes 29% less power than the conventional blade. • The optimization rusult were verified experimentally in Mars simulated environment.