论文部分内容阅读
针对传统方法在电枢设计中的局限性,以电枢发射过程中应力、温度及质量为优化目标对象,提出了一种将响应面模型与多目标遗传算法相结合的优化设计方法。采用拉丁超立方试验的设计方法,在设计空间抽取样本点,建立了由喉部厚度、臂长、尾翼厚度及尾翼倾角等4个参数所决定的初始二阶响应面模型,利用多目标遗传算法NSGA-Ⅱ对响应面模型进行优化,求得Pareto最优解集,并根据最小距离选择法得到了最优妥协解。仿真结果表明,发射过程中电枢的应力、温度及质量不会同时达到最小,但选取的最优妥协解可使得上述3个指标中的其中1个在另外2个指标均不变差的前提下不会取得更小值,实现了电枢的优化设计,且该方法具有较高的准确性和可行性,为电枢结构的改进提供了有效的途径。
Aiming at the limitation of the traditional method in armature design and the stress, temperature and mass in the process of armature firing, the optimization design method combining response surface model and multi-objective genetic algorithm is proposed. Using the Latin hypercube test design method, the sample points were sampled in the design space, and the initial second-order response surface model was established, which was determined by four parameters of throat thickness, arm length, tail thickness and tail inclination. Using multi-objective genetic algorithm NSGA-Ⅱ is used to optimize the response surface model to obtain the Pareto optimal solution set, and the optimal compromise solution is obtained according to the minimum distance selection method. The simulation results show that the stress, temperature and mass of the armature during the firing process will not be minimized at the same time. However, the optimal compromise solution can make the premise that one of the above three indexes does not deteriorate in the other two indexes The smaller value will not be obtained, the optimal design of the armature is achieved, and the method has high accuracy and feasibility and provides an effective way for improving the armature structure.