摘要
针对目前机械结构优化中建立预测模型代价较高的问题,提出了一种基于数据学习的结构静力学性能预测方法。以悬臂梁为研究对象,建立有限元仿真模型以获取位移场数据,构建边界条件-位移场代理模型,预测结果表明位移场分布趋势与实际一致,载荷为1000 N和1600 N时最大位移相对误差分别为-0.02%和-0.47%。文中讨论了均布力大小和集中力作用位置对位移场预测结果的影响,结果表明,随着载荷幅值增加,预测误差有所增加。相比均布力,集中力载荷下的预测误差更大,且加载位置靠近边缘处的误差更大。反演问题分别将位移场作为输入,将均布力大小和集中力位置作为输出构建位移场-边界条件代理模型,载荷为1000 N和1600 N时的预测误差分别为0.15%和-0.48%,在5 mm和10 mm处的载荷位置预测误差分别为0.38%和-1.84%,实现了对力边界条件的高精度预测。所提方法从数据学习角度出发,可为机械结构的静力学性能预测提供一种新的思路。
Aiming at the high cost of establishing a prediction model in the current mechanical structure optimization,a prediction method of structural static performance based on data learning is proposed.The cantilever beam is taken as the research object,and the finite element model is established to obtain the displacement field data of the simulation results.Then the boundary condition-displacement field surrogate model is constructed.The results show that the trend of displacement field distribution is consistent with the actual situation,and the relative error of the maximum displacement is-0.02%and-0.47%under the load of 1000 N and 1600 N,respectively.The influences of the magnitude of the uniform force and the position of the concentrated force on the displacement field prediction are discussed.The results show that the prediction error increases with the increase of load amplitude.Compared with the uniform force,the prediction error under the concentrated force load is larger,and the error is larger when the loading position is near the edge.In the inversion problem,the displacement fields are taken as the input,the uniform forces and the positions of concentrated force are taken as the output to construct the displacement field-boundary condition surrogate model.The prediction errors under the uniform loads of 1000 N and 1600 N are 0.15%and-0.48%,respectively,and the prediction errors under the load positions at 5 mm and 10 mm are 0.38%and-1.84%.The method based on data learning can provide new thinking for the prediction of structural static performance.
作者
赵航
童水光
朱郑州
ZHAO Hang;TONG Shui-guang;ZHU Zheng-zhou(School of Mechanical Engineering,Zhejiang University,Hangzhou 310027,China;School of Software&Microelectronics,Peking University,Beijing 102600,China)
出处
《计算机科学》
CSCD
北大核心
2022年第4期140-143,共4页
Computer Science
基金
浙江省重点研发计划(2019C01057)。
关键词
数据学习
代理模型
神经网络
有限元分析
性能预测
Data learning
Surrogate model
Neural network
Finite element analysis
Performance prediction
