吉林大学学报(工学版) ›› 2025, Vol. 55 ›› Issue (1): 74-83.doi: 10.13229/j.cnki.jdxbgxb.20230333

• 车辆工程·机械工程 • 上一篇    下一篇

基于投影优选法的结构静态位移多目标优化方法

麻凯(),孙建航,闫森康,陶炎,汪文涛,郭桂凯()   

  1. 吉林大学 机械与航空航天工程学院,长春 130022
  • 收稿日期:2023-04-10 出版日期:2025-01-01 发布日期:2025-03-28
  • 通讯作者: 郭桂凯 E-mail:makai@jlu.edu.cn;ggk@jlu.edu.cn
  • 作者简介:麻凯(1978-),男,副教授,博士. 研究方向:固体力学. E-mail: makai@jlu.edu.cn
  • 基金资助:
    国家自然科学基金项目(51675216)

Multi-objective optimization method of structural static displacement based on projection priority selection method

Kai MA(),Jian-hang SUN,Sen-kang YAN,Yan TAO,Wen-tao WANG,Gui-kai GUO()   

  1. School of Mechanical and Aerospace Engineering,Jilin University,Changchun 130022,China
  • Received:2023-04-10 Online:2025-01-01 Published:2025-03-28
  • Contact: Gui-kai GUO E-mail:makai@jlu.edu.cn;ggk@jlu.edu.cn

摘要:

本文提出了一种基于投影优选法的结构静态位移多目标优化方法。投影优选法是根据参数灵敏度向量在特定的多维子空间的投影角度和投影长度来对参数进行排序的参数选取方法。其中,本文采用的结构静态灵敏度的解析计算方法是结合Epsilon算法和改进的纽曼级数建立的。该多目标优化方法每步迭代都可按照给定的参数个数要求来选取合理的参数组合并得到对应参数的修改量,通过多次迭代计算得到优化解。该方法特别适用于限定参数个数的多目标结构优化问题。在算例中,使用该方法对一个桁架结构进行多目标优化。算例中还用随机选取的参数组与本文方法选定的参数组做优化结果对比。对比结果表明:应用本文方法的优化结果更理想。算例进一步证明了本文优化方法具有很高的工程应用价值。

关键词: 固体力学, 灵敏度分析, 投影优选法, 最优修改量, Epsilon算法

Abstract:

In this paper, a multi-objective optimization method of structural static displacement based on projection priority selection method is proposed. Projection priority selection method is a parameter selection method that sorts the parameters according to the projection angle and projection length of the parameter sensitivity vector in a specific multidimensional subspace. The structural static sensitivity is calculated by the analytical method based on Epsilon algorithm and improved Newman series. In each iteration of the optimization method, the reasonable combination of parameters can be selected according to the given number of parameters and the modification of the corresponding parameters can be obtained. The optimal solution can be obtained through multiple iterations. This method is especially suitable for solving multi-objective structural optimization problems with limited number of parameters. In the example, the proposed method is used for multi-objective optimization of a truss structure. The optimization results were compared between the randomly selected parameter group and the parameter group selected by the method in this paper. The results show that the optimization results of the Projection priority selection method are more ideal. Examples further prove that the proposed optimization method has high engineering application value.

Key words: solid mechanics, sensitivity analysis, projection priority selection method, optimal amount of modification, Epsilon algorithm

中图分类号: 

  • O342

图1

Epsilon算法计算流程(s=5)"

图2

投影优选法流程图"

图3

结构示意图"

表1

各节点位移"

序号初始位移/mm目标位移/mm最优参数组/mm对照1组/mm对照2组/mm对照3组/mm对照4组/mm
A64,A79,A55A61,A2,A69,A40A23~A29A34~A40A29,A31,A21A34,A36,A38,A40A44~A50
1119.72115119.04119.57119.72119.64119.56
268.596468.0668.4968.5568.5468.48
397.569397.0097.4897.5797.4997.48
452.544852.0152.4552.4952.4852.44
568.146367.7468.1068.1868.0668.10
649.584549.0749.5149.5049.5249.50
744.754044.4944.7844.6544.6444.77
849.194448.6949.1249.1349.1549.12
953.654953.2553.5953.6253.5653.59
1043.693943.0743.5243.6843.6643.51
????????
5028.462328.2428.4928.4628.4728.49
51124.47119124.01124.30124.48124.40124.30
5269.606569.1569.4269.5669.5569.42
53113.42108113.00113.29113.43113.34113.28
5463.725963.3263.5863.6863.6663.57
55115.93111115.24115.79115.94115.86115.79
5684.207983.5183.9984.1784.1683.98
57130.54126130.09130.37130.56130.48130.36
58105.94101105.20105.68105.91105.90105.67
59110.96106110.27110.82110.94110.86110.81
6052.334752.1052.3652.3352.3452.36

图4

修改截面 A64、 A79、 A55、 A61、 A2、 A69、 A40后的首次迭代位移图"

图5

修改截面A23~A29后的首次迭代位移图"

图6

修改截面A34~A40后的首次迭代位移图"

图7

修改截面 A29、 A31、 A21、 A34、 A36、 A38、 A40后的首次迭代位移图"

图8

修改截面 A44~ A50后的首次迭代位移图"

表2

首次迭代后不同设计参数组的位移误差向量均方值及误差"

参数最优参数组对照1组对照2组对照3组对照4组
A64,A79,A55A61,A2,A69,A40A23~A29A34~A40A29,A31,A21A34,A36,A38,A40A44~A50
位移误差向量均方值φrms21 317.5861 444.7121 483.8311 466.2611 394.268
εl/%09.64812.61711.2835.819

表3

每次迭代均基于投影优选法的多次迭代后的位移误差向量均方值"

迭代次数1100200300400500621
位移误差向量均方值φrms21 317.586437.361159.22463.77644.07128.9265.838

图9

多次迭代后位移误差向量均方值对比图"

表4

使用首次迭代中得到的优选参数组的多次迭代后的位移误差向量均方值"

迭代次数157102050100
位移误差向量均方值φrms21 317.586340.721223.508267.7411 412.20511 806.77014 707.850

图10

最终优化得到的位移云图"

表5

最终优化后各个桁架的横截面积 (mm2)"

序号横截面积序号横截面积序号横截面积序号横截面积序号横截面积序号横截面积
162.95215172.5312963.79743161.8465763.96171115.542
2153.7971670.9453099.99144105.29158137.5217279.695
310.0001771.07931199.33045113.4345998.7617367.712
488.7841889.38232193.5974610.3306093.91574106.772
5113.47819103.6153395.6024710.00061113.1857586.581
6120.7172010.0003486.2514818.0996256.4447661.457
790.5092191.3903584.33249198.21263103.59877113.593
897.67422100.0713631.61350166.6116497.91578113.169
9120.82823115.44837104.56951268.3306598.74579106.079
10120.36724124.7623858.33152125.73366124.250
11125.84525104.9203986.91253119.00867134.266
12161.71426101.8424010.0005464.4026889.820
13151.28527118.1434186.4085557.7806962.970
1411.48028121.1874273.13156127.69670119.673

表6

最终优化后的位移数据"

序号目标位移/mm实际位移/mm误差/%
1115115.279 050.24
26464.104 5630.16
39393.137 4070.15
44848.153 8870.32
56363.117 7850.19
64544.800 590.44
74040.153 1480.38
84444.226 4360.51
94949.081 90.17
103938.928 1280.18
????
502322.987 670.05
51119119.206 210.17
526565.012 7160.02
53108108.241 130.22
545959.069 8130.12
55111111.284 530.26
567979.022 3540.03
57126126.213 330.17
58101101.017 090.02
59106106.198 050.19
604747.069 8060.15
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