Genetic Algorithm Applied to the Time-Series Landing Flight Path and Control Optimization of a Supersonic Transport

A genetic algorithm (GA) which is a meta-heuristic approach was applied to optimize the landing flight path of a delta-winged supersonic transport (SST). However, at low speeds, particularly during take-off and landing, a complex flowfield surrounds the delta wing. This phenomenon requires time-series control optimization that yields an optimum control sequence by aerodynamic - flight dynamics with high-fidelity computational fluid dynamics to evaluate the flight path with the complex flowfield. To this end, we presented an efficient flight simulation based on Kriging-model-assisted aerodynamic estimation to carry out the global optimization via a GA. After establishing the efficient aerodynamics-flight dynamics optimization, we constructed the design of the flight and control sequence for the time-series optimization of an effective SST landing. Several solutions that provide an allowable SST landing performance, along with the knowledge on optimum flight and control sequence, are presented herein.