计算机科学 ›› 2022, Vol. 49 ›› Issue (4): 140-143.doi: 10.11896/jsjkx.210300238
赵航1, 童水光1, 朱郑州2
ZHAO Hang1, TONG Shui-guang1, ZHU Zheng-zhou2
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1000N和1600N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1000N和1600N时的预测误差分别为0.15%和-0.48%,在5mm和10mm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。
中图分类号:
[1] DHAR V.Data science and prediction[J].Communications of the ACM,2013,56(12):64-73. [2] SCHMIT J R L A,FARSHI B.Some approximation concepts for structural synthesis[J].AIAA Journal,1974,12(5):692-699. [3] FORRESTER A,SOBESTER A,KEANE A.Engineering de-sign via surrogate modelling:apractical guide[M].Chichester:John Wiley & Sons,2008. [4] HAN Z H,XU C Z,QIAO J L,et al.Recent progress of efficient global aerodynamic shape optimization using surrogate-based approach[J].Acta Aeronautica et Astronautica Sinica,2020,41(5):30-70. [5] MU X F,YAO W X,YU X Q,et al.Research on surrogate mo-dels in multidisciplinary design optimization[J].Chinese Journal of Computational Mechanics,2005,22(5):608-612. [6] CHEN Y Y,ZHENG L.Simulation and Optimization of Vehicle Frontal Crashworthiness Based on Surrogate Model[J].Automotive Engineering:Zhejiang University,2018,40(6):673-678. [7] TONG S G,ZHAO H,LIU H Q,et al.Optimization calculation method for efficiency of multistage split case centrifugal pump[J].Journal of Zhejiang University (Engineering Science),2019,53(5):988-996. [8] QIAN J C,YI J X,CHENG Y S,et al.A sequential constraints updating approach for Kriging surrogate model-assisted engineering optimization design problem[J].Engineering with Computers,2020,36:993-1009. [9] TONG S G,ZHAO H,LIU H Q,et al.Multi-objective optimization of multistage centrifugal pump based on surrogate model[J].Journal of Fluids Engineering,2020,142(1):011101. [10] YANG G F.Algorithms and applications of some forward andinverse problems in mathematical physics[D].Shanghai:Fudan University,2007. [11] CLERMONT G,SVEN Z.The inverse problem in mathematical biology[J].Mathematical Biosciences,2015,260:11-15. [12] GU Y,LEI J,FAN C M,et al.The generalized finite difference method for an inverse time-dependent source problem associated with three-dimensional heat equation[J].Engineering Analysis with Boundary Elements,2018,91:73-81. [13] MA W C,WANG S L,GU J Y,et al.Deep feedback inverse problem solver[C]//European Conference on Computer Vision.Cham:Springer,2020:229-246. [14] WANG Y F.Calculation method of inversion problem and its application[M].Beijing:Higher Education Press,2007. [15] CHAO L M,XING C X,ZHANG Y.Data Science Studies:State-of-the-art and Trends[J].Computer Science,2018,45(1):1-13. [16] CHAO L M,WANG R.Data Science Platform:Features,Technologies and Trends[J].Computer Science,2021,48(8):1-12. |
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