Advanced optimization of gas turbine aero-engine transient performance using linkage-learning genetic algorithm: Part II, optimization in flight mission and controller gains correlation development

Part I has illustrated the procedures to apply the Linkage Learning Genetic Algorithm (LLGA) in Gas Turbine Engine (GTE) controller gains tuning and generated the optimization results for runway conditions from idle to takeoff. However, the total pressure and temperature of the engine inlet vary as the changing of altitude and Mach number, which would lead to the variation in fuel flow supply regulation. As a result, the optimized gains in runway might not be suitable for other flight conditions. In order to maintain the optimal control performance, the GTE controller gains should be adjusted according to the flight conditions. This paper extends the application of the LLGA method to other flight conditions and then simulates a complete flight mission with different gains and weather condition configurations. For this purpose, the control parameters in the Simulink model of the GTE controller are first corrected by the weather condition in altitude. Then, a typical flight mission is defined and divided into different flight segments based on the altitude and Mach number configuration. One representative point is selected from each segment as the datum point for optimization process. After this step, the LLGA method is used to find the best gains combinations for different flight conditions and the differences in optimization effects for different flight conditions are analyzed subsequently. The simulation results show that the optimization effect of the control performance of each flight condition is dependent on the value of VIM and the optimal Kpla in some flight conditions is approximately equal to 1/hd times of the Kpla value in sea level standard condition. Finally, the complete flight mission is simulated with different gains and weather condition configurations. The simulation results show that the engine performance has been greatly improved after optimization by LLGA in the transient state and the high altitude con-ditions. In other steady states, the optimization effect is not very obvious. (c) 2020 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This paper applies the LLGA method to adjust the gains of the GTE controller, finding that the optimized gains are suitable for different flight conditions, improving engine performance in transient and high altitude conditions, but the optimization effect is not significant in other steady states.