Evolutionary algorithm applied to time-series landing flight path and control optimization of supersonic transport

An evolutionary algorithm (EA) was applied in this study to optimize the landing flight path of a delta-winged supersonic transport (SST). However, it is difficult for a delta wing with a large sweepback angle to reduce the aerodynamic drag during supersonic cruising to gain sufficient lift force at low speeds, particularly during takeoff and landing. Besides, high-fidelity computational fluid dynamics is required to evaluate the flight path with a complex flowfield. This study performed an efficient flight simulation based on the Kriging model-assisted aerodynamic estimation to carry out global optimization. Then, the designs of the flight and control sequence were realized for time-series optimization of effective SST landing. To develop the EA, two design scenarios were considered; one involved only the elevator, which is an aerodynamic control surface that controls the aircraft, and the other involved introducing thrust control in addition to elevator control. In the scenario involving only elevator control, feasible solutions could not be obtained owing to the poor low-speed aerodynamic performance of the SST. This paper presents several feasible solutions enabling reasonable SST landing performance in the scenario involving the elevator and thrust controls along with descriptions regarding the optimum flight and control sequences. In addition, we analyzed the solutions by analyzing the variance to obtain qualitative information. Consequently, we determined that elevator control was considerably effective in cases with the microburst effect than in cases without the microburst effect.

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