【摘要】 为降低弹射装置的质量及体积占用并提升其缓冲性能，以某型弹射装置为研究对象，建立了弹射过程及缓冲过程的物理模型和数学模型，利用龙格库塔方法求解并进行仿真分析。利用拉丁超立方采样方法选取样本点，并在仿真程序中运行求解。随后基于BP神经网络建立输入与输出之间的代理模型，并以此代理模型为基础，利用NSGA-Ⅱ多目标优化方法进行优化。经过优化，与初始方案对比，装置质量降低15.52%,缓冲末速度降低54.58%,最大缓冲加速度降低23.15%,优化效果显著。更多还原

【Abstract】 In order to reduce the weight and volume occupation of ejection device and improve its buffering performance, the physical and mathematical models for ejection process and buffering process were established with a certain ejection device as the research object, and were solved and simulated by using the Runge Kutta method. The sample points are selected by using the Latin hypercube sampling method, and the solution is run in the simulation program. Then a proxy model between the input and the output is established based on BP neural network. Based on this proxy model, NSGA-Ⅱ multi-objective optimization method is used for optimization. After optimization, comparing with the initial scheme, the weight of the device is reduced by 15.52%, the final buffering speed is reduced by 54.58%, and the maximum buffering acceleration is reduced by 23.15%.更多还原