面向航程能力的固体火箭发动机方案设计优化

Conceptual design optimization of solid rocket motor for range capability

为解决飞行器航程最优为目标的固体火箭发动机方案设计优化问题,在固定规模约束下建立了复杂三维发动机装药、壳体和喷管的几何参数化模型及推力、质量的性能模型,同时,根据助推滑翔飞行器飞行环境严酷、性能要求高等特点,建立了多约束飞行器航程能力评估模型,然后采用改进自适应Legendre-Gauss-Radau伪谱法评估当前发动机方案对应的飞行器最大航程,构建了含约束的混合整数参数优化问题。针对高耗时混合整数参数优化问题,引入Kriging代理模型,对实数型序列近似优化算法进行了调整,加入了整数约束。针对传统全局加点准则收敛速度慢的问题,提出了局部增强策略,并进行了数值验证。仿真结果表明:采用改进自适应伪谱法得到的飞行轨迹满足全部约束要求,相对于初始固体火箭发动机方案,优化后的发动机方案对应的最大航程提升了12.89%,改进算法的优化结果与遗传算法一致,但耗时函数的调用次数仅为后者的2%,且相比于传统序列近似优化算法,该算法具有一定的全局搜索能力,有效改善了收敛速度。

In view of the conceptual design optimization problem of solid rocket motor(SRM)that takes the optimal range of aircraft as the objective,the geometric parametric models of grain,combustion chamber,and nozzle of complex three-dimensional motor as well as the performance models of thrust and mass were firstly established under fixed-scale constraints.Meanwhile,considering the harsh flight environment and high performance requirements of boost-glide aircraft,a multi-constraint range capability evaluation model was proposed.Then,the maximum range of aircraft corresponding to the current motor scheme was evaluated by adaptive Legendre-Gauss-Radau pseudo-spectral method,and a constrained mixed-integer parameter optimization problem was constructed.In order to solve the high time-consuming of the mixed-integer parameter optimization problem,the Kriging surrogate model was introduced,and the sequential approximate optimization algorithm for real number was adjusted by adding integer constraints.For slow convergence rate of the traditional global sampling criterion,a local enhancement strategy was proposed and verified numerically.Simulation results show that the flight trajectory obtained by the improved adaptive pseudo-spectral method satisfied all the constraints,and the optimized maximum range was increased by 12.89%compared with the original SRM scheme.The solution of the proposed algorithm was consistent with that of genetic algorithm,but the number of time-consuming function calls was only 2%of the genetic algorithm.Compared with the traditional sequential approximate optimization algorithm,the proposed algorithm had global search capability,and effectively improved the convergence rate.