可行SLP法在发动机在线优化中的应用

研究了航空发动机在线优化算法问题。基于序列可行方向法,提出了一种用于解决一般非线性优化问题改进的序列线性规划(SLP)在线优化算法——可行下降序列线性规划(FSLP)方法。其显著特点是通过适当的步长修正算法,在保证目标函数下降的同时,确保解的可行性。根据对偶理论证明了其核心算法的收敛性,对步长修正原理进行了数学分析,并详细介绍了算法实现途径。基于上述优化算法,以某型双转子涡扇发动机最大推力模式为仿真算例,验证了该算法在解决航空发动机在线优化问题时,相比传统的序列优化方法,在提高优化算法解的可行性方面效果更好。

Application of Feasible Descent Sequential Linear Programming to Aeroengine Online Optimization

Aeroengine online optimization methods are studied in this article. An improved sequential linear programming (SLP) method called feasible descent sequential linear programming (FSLP) is proposed to solve normal nonlinear optimization problems, and not only the optimality but also the feasibility is guaranteed by an appropriate step modification algorithm. The convergence of the core algorithm of FSLP is proved according to the duality theory. The principle of the step modification algorithm is analyzed, and the numerical recipe realizing this algorithm is introduced in detail. As a simulation example, the FSLP algorithm is applied to in the maximum thrust control mode for the performance of a certain two-spool turbofan engine. The simulation results show that FSLP possesses greater feasibility than the traditional sequential optimization programming method in solving aeroengine online optimization problems.