CVDA序贯采样法在全铝车架轻量化设计上的应用

Application of CVDA Sequential Sampling Method to Lightweight Design of Aluminum Alloy Frame

在铝合金车架的轻量优化设计中,通常建立近似模型代替有限元模型,以减少计算量。为进一步提高计算效率,用较少的样本点建立高精度近似模型,提出了一种改进的序贯采样方法—CVDA法,并通过一个算例将其与现有的3种采样方法(OLHD、MDA和CVA)进行对比。结果表明,在样本点数量均为21个的情况下,采用CVDA法采样建立近似模型的预测精度比另3种采样方法建立的近似模型分别高出11.1%,11.1%和30.3%,R^2值达到了0.95。将CVDA法运用到全铝车架弯扭刚度和弯扭模态工况的近似模型建立中,在相同数量的样本点下,采用CVDA法采样建立的近似模型,其预测精度明显高于其余方法。在此基础上对铝合金车架进行轻量优化,结果弯曲刚度、扭转刚度、1阶弯曲模态频率和1阶扭转模态频率分别提升了2.54%,3.38%,0.68%和2.75%,质量减轻7.47 kg,减重率达7.20%,优化效果明显。

In lightweight optimization design of aluminum alloy frame,a surrogate model is usually constructed to replace its finite element model for reducing computation effort.For further enhance computation efficiency so as to establish a high-accuracy surrogate model with lesser sample points,an improved sequential sampling method named CVDA is proposed and compared with three existing sampling methods(OLHD,MDA and CVA)through a calculation sample.The results show that the prediction accuracy of surrogate model built with CVDA sampling is 11.1%,11.1%and 30.3%higher than that with OLHD,MDA and CVA sampling methods respectively with a R2 value reaching 0.95.Then the CVDA method is applied to building the surrogate model of aluminum alloy frame in bending and torsion conditions,and with the same number of sample points,the prediction accuracy of surrogate model built with CVDA method is apparently higher than that with other sampling method.On this basis,a lightweight optimization is conducted on aluminum alloy frame,and as a result,the bending stiffness,torsional stiffness,1st order bending modal frequency and 1st order torsional modal frequency increase by 2.54%,3.38%,0.68%and 2.75%respectively,and the mass reduces by 7.47 kg with a mass reduction rate of 7.20%,showing an obvious optimization result.