固体火箭发动机成本与性能双目标优化设计

Dual-objective optimization design for cost and performance of a solid rocket motor

为求解某运载火箭上面级固体推进剂火箭发动机多属性价值优化问题,建立了发动机主要部件的参数成本模型,研究了一种改进的Pareto多目标遗传算法———IPGA算法,测试函数的计算结果表明,该算法收敛性优于NSGA Ⅱ算法.以运载火箭末速度增量最大和发动机制造成本最低作为目标函数,采用IP GA计算得到了壳体材料分别为APMOC和碳纤维时的Pareto非劣解集,采用理想点法得到非劣解集中费效比变化的拐点,计算结果表明,以该点为满意解设计方案可以使该运载火箭的有效载荷提高7.6% ,并使发动机的成本降低.

To solve the problem of multiple attributes value optimization in the upper stage solid propellant rocket engines of a launch vehicle, parameter-cost models of motor parts were established and an improved pareto genetic algorithms (IPGA) that combines the NSGA-II with local search algorithm was presented. Simulation test shows that the convergence of IPGA is better than that of NSGA-II. Setting the maximum terminal speed increment of the rocket motor and its manufacture cost as objective functions, Pareto optimal sets were obtained by using IPGA which motor case materials are APMOC and carbon respectively. Adapting the deal point method, an inflexion point of cost-effect ratio in Pareto optimal sets was gained. Setting this point as trade-off solution, the calculation results show that the effective load of the launch vehicle was increased by 7.6% and cost of the motor was decreased.