复合材料加筋结构的神经网络响应面优化设计

NEURAL NETWORK RESPONSE SURFACE OPTIMIZATION DESIGN FOR COMPOSITE STIFFENED STRUCTURES

针对复合材料加筋结构优化设计的复杂性,提出利用人工神经网络结构近似分析响应面来反映结构设计输入与结构响应输出的全局映射关系的优化方法。通过正交试验设计选取合适的结构有限元分析样本点,进行神经网络响应面的构建和训练：将神经网络响应面作为目标函数或者约束条件,汇同其他常规约束条件完成优化模型的建立,并应用遗传算法(GA)进行优化,从而形成一套适应性强的的高效优化方法。以复合材料翼身融合体帽型加筋板的质量优化为实例,建立加筋板模型的重量响应面目标函数、强度和翘曲稳定性响应面约束条件；通过PATRAN/NASTRAN有限元软件进行有限元计算,获取用于响应面训练的样本点数值。算例结果表明,该方法能以很少的有限元分析次数取得高精度的响应面近似模型,并且使优化计算耗时大为减少,优化效率大大提高。

To avoid drawbacks of conventional structural optimization approaches, a neural network （NN） response surface optimization method is proposed for the design of composite stiffened structures. Such NN-based structural analysis response surfaces can reflect the global mapping relationship between design inputs and structural response outputs, By using the orthotropic experiment method to select the appropriate structural finite element analysis samples, neural network response surfaces can be trained with reasonable accuracies. The constructed response surfaces can be either used as objective function or constraints or both, Together with other conventional constraints, an revised optimization design model can be formed which can be solved by using genetic algorithm （GA）. Taking a hat-stiffened composite panel of blended wing-body aircraft as example, the structural weight response surface is developed as objective function, and strength and buckling factor response surfaces as constraints. All these neural net- works are trained by finite element samples computed through PATRAN/NASTRAN software, The optimization results illus- trate that it can significantly reduce the cycles of finite element model analysis and achieve highly accurate response approximarion results. Eventually, the approach can greatly save the computation time and raise the efficiency ＆optimization process.