基于改进灰狼优化算法的涡扇发动机性能/喷流噪声综合优化控制研究

为保证整个飞行过程中满足噪声适航标准和飞行器的安全性,需要按照最严苛的噪声要求进行发动机设计,并留有很大的安全裕度,因而导致发动机的性能潜力未能得到发挥。本文对传统灰狼算法进行了改进,提出自适应概率变异策略,在优化过程中调整狩猎模式,提升了算法的全局搜索能力;基于该算法开展涡扇发动机性能/喷流噪声综合寻优控制研究,根据不同飞行需求对航空发动机性能进行优化,获得最佳控制量,在满足安全性和噪声指标的同时,提高发动机的性能。仿真结果表明,改进后的算法具有更好的全局寻优性能,最大推力模式下可提升推力13.45%,最小油耗模式可降低油耗3.19%,最低涡轮前温度模式可降低涡轮前温度2.07%。

Integrated Optimization Control of Performance and Jet Noise of Turbofan Engine Based on Improved Grey Wolf Optimization Algorithm

In order to meet the airworthiness standards and safety requirements of civil aviation aircraft within the full flight envelope, the engine needs to be designed with a large safety margin to meet the most stringent noise requirements. Therefore, the engine performance potential cannot be taken full use of. A self-adaptive mutation strategy is proposed to improve the traditional gray wolf algorithm. Along with the adjustment of optimization mode during the optimization process, the global optimization performance is improved. Integrated optimization control of performance and jet noise is studied based on the improved grey wolf optimization algorithm according to different flight requirements. The control variable is optimized to improve the engine performance while guaranteeing the safety and noise limits. Simulation results show that the thrust is increased by 13.45% under the maximum thrust mode, the fuel assumption is reduced by 3.19% under the least fuel consumption mode, and the temperature of the turbine is reduced by 2.07% under the lowest turbine inlet temperature mode.