由于复合材料力学性能分散度较大、加工精度较低，复合材料薄壁吸能结构具有难以忽略的不确定性。为此，建立了考虑参数不确定性的复合材料薄壁吸能结构优化设计方法，旨在设计阶段识别参数的不确定性，并设计出具有最优吸能特性的薄壁吸能结构。首先，根据使用条件获取设计参数的范围，并利用中心组合实验设计确定设计参数的实验矩阵；然后，计算实验矩阵中每组设计参数吸能特性指标的平均值和标准差；接下来，分别确定不同复合材料薄壁结构设计参数组合与吸能特性指标的均值及标准差之间的响应面方程；最后，结合响应面方程和设计要求将其代入到优化后的数学表达式中，采用序列二次规划算法进行优化求解。本文以T700/3234复合材料薄壁圆管为研究对象，在规定特征尺寸范围内，分别对规定峰值载荷求比吸能最大，以及对规定峰值载荷求比吸能标准差最小问题，进行了优化求解，验证了所提方法的实用性。
Abstract
Due to larger dispersity of mechanical performance and lower machining precision of composite materials, thin-walled composite energy-absorbing structures have uncertainties hard to ignore. Here, an optimization design method for thin walled composite energy-absorbing structures with parametric uncertainty was established to identify parametric uncertainty in design stage and design thin walled composite structures with the optimal energy absorption feature. Firstly, parametric ranges were acquired according to using conditions, and the central combined test design was used to determine the test matrix of designed parameters, calculate the mean value and standard deviation of each group of designed parameters’ energy absorption characteristic index in the test matrix, and determine response surface equations between different design parametric groups and mean values, standard deviations of their energy absorption indexes, respectively. Finally, the sequence quadratic programming algorithm was adopted to get an optimization solution after substituting response surface equations and design requirements into the optimized mathematical expression. A T700/3234 composite thin-walled circular tube was taken as a study object. Within the specified ranges of feature sizes, under the specified peak load, the maximum ratio energy-absorbing problem and the ratio energy-absorbing’s minimum standard deviation problem were solved, respectively to verify the applicability of the proposed method.
关键词
复合材料薄壁吸能架构 /
不确定性 /
吸能特性 /
规划算法 /
优化设计
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Key words
thin-walled energy-absorbing composite structure /
uncertainty /
energy-absorbing characteristics /
programming algorithm /
optimization design
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