直升机惯性传感器结构的模态优化

提出了一种基于迭代抽样和径向基插值的自适应代理模型方法。这种自适应方法以减少仿真计算数量和提高代理模型自适应能力为目的,使用多岛遗传算法选择新增样本点并使新增样本点位于设计空间的稀疏区域,使得所有的样本点均匀分布于设计空间。标准误差用来判断代理模型的精度大小以决定是否对代理模型进行更新。这种自适应代理模型结合多岛遗传算法对直升机的惯性传感器结构模态进行优化。用拉丁超立方抽样方法选择10个样本点构建初始的代理模型,自适应代理模型的计算结果表明2%的误差条件下需要额外增加7个样本点。优化结果表明不同的权重系数对最优模态特性的影响很大,惯性传感器结构的一至六阶模态值更加远离直升机的激励频率。

Modal Optimization of Inertial Sensor Structure for helicopter

An adaptive surrogate model based on iteration sampling and extended radial basis function is proposed. The purpose of this adaptive method is reducing the number of simulation calculations and improving the surrogate model adaptive ability by multi-island GA algorithm. New sample points are located in the blank area and all the sample points are distributed in the design space uniformly. The precision of the surrogate model is checked using standard error measure to judge whether updating the surrogate model or not. Multi-island GA algorithm is combined with the adaptive surrogate model to find the optimum modal characteristic of an inertial sensor structure for electric helicopters. A total of ten training points are selected to construct the initial surrogate model using Latin hypercube sampling (LHS). The results of adaptive surrogate model show that seven new sampling points are needed to improve the accuracy of the surrogate model under the condition of 2% confidence bounds. The optimization results show that the selection of the weights for the objective functions will have a significant effect on the final optimum modal characteristic. And the optimization results indicate that the optimum modal characteristic makes the natural frequency away from the excitation frequency.