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彭林欣,罗伟嫚,黄钟民.薄板几何非线性弯曲分析的深度能量法[J].计算力学学报,2024,41(3):556~563
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薄板几何非线性弯曲分析的深度能量法
Deep energy method for geometrical nonlinear bending analysis of thin plates
投稿时间:2022-09-15  修订日期:2022-11-02
DOI:10.7511/jslx20220915001
中文关键词:  几何非线性  深度能量法  增量式神经网络  Von-Karman非线性理论
英文关键词:geometric nonlinearity  deep energy method  incremental neural network  Von-Karman nonlinear theory
基金项目:国家自然科学基金(12162004);国家重点研发计划(2019YFC1511103);广西科技重大专项(桂科AA18118029);广西重点研发计划(桂科AB22036007)资助项目.
作者单位E-mail
彭林欣 广西大学 土木建筑工程学院, 南宁 530004
广西大学 广西防灾减灾与工程安全重点实验室, 工程防灾与结构安全教育部重点实验室, 南宁 530004 
 
罗伟嫚 广西大学 土木建筑工程学院, 南宁 530004  
黄钟民 广西大学 土木建筑工程学院, 南宁 530004 huangzm@alu.gxu.edu.cn 
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中文摘要:
      发展了一种增量形式的深度能量法求解薄板几何非线性弯曲问题。根据最小势能原理和Von-Karman非线性理论,构建以薄板势能为驱动的增量式深度神经网络模型。首先用网格离散薄板求解域,通过Python读取网格数据计算Hammer积分点,并以此作为训练集代入网络模型预测板的弯曲位移,再将荷载分成一系列的荷载增量,每个增量步中计算薄板势能作为神经网络的损失函数,以最小化势能为目标,结合Adam优化算法更新网络模型参数,待势能取驻值后再继续下一个荷载步的计算。本文求解了不同形状、不同边界条件下薄板的几何非线性弯曲问题,并将计算结果与文献解或有限元Abaqus解进行对比,研究表明,本文方法在求解薄板的几何非线性弯曲问题上具备有效性和准确性,且增量式的神经网络模型能够减小计算内存,有效提高计算效率和模型的稳定性。
英文摘要:
      An incremental deep energy method is developed to solve geometric nonlinear bending problems of thin plates.According to the minimum potential energy principle and Von-Karman nonlinear theory,an incremental deep neural network model driven by thin plate potential energy is constructed.Firstly,the solution domain of the thin plate is discretized into a grid,and the Hammer integral points are calculated by reading the grid data in Python,which is used as training set to be substituted into the network model to predict the bending displacement of the plate.Then,the load is divided into a series of load increments.In each increment step,the potential energy of the thin plate is calculated as the loss function of the neural network.With the goal of minimizing the potential energy,the parameters of the network model are updated by Adam optimization algorithm,and the calculation of the next load step is continued after the potential energy takes a stationary value.In this paper,the geometric nonlinear bending problem of thin plates with different shapes and different boundary conditions is solved,and the calculated results are compared with the finite element Abaqus solution using Abaqus from the literattere.The research shows that this method is effective and accurate in solving the geometric nonlinear bending problem of thin plates,and the incremental neural network model needs a low computer,and can effectively improve the computational efficiency and the stability of the model.
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