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现代试验设计及其在空气动力学中的应用进展

海春龙 何磊 梅立泉 钱炜祺

海春龙,何磊,梅立泉,等. 现代试验设计及其在空气动力学中的应用进展[J]. 实验流体力学,2022,36(3):1-10 doi: 10.11729/syltlx20220005
引用本文: 海春龙,何磊,梅立泉,等. 现代试验设计及其在空气动力学中的应用进展[J]. 实验流体力学,2022,36(3):1-10 doi: 10.11729/syltlx20220005
HAI C L,HE L,MEI L Q,et al. Modern design of experiment and its development in aerodynamics[J]. Journal of Experiments in Fluid Mechanics, 2022,36(3):1-10. doi: 10.11729/syltlx20220005
Citation: HAI C L,HE L,MEI L Q,et al. Modern design of experiment and its development in aerodynamics[J]. Journal of Experiments in Fluid Mechanics, 2022,36(3):1-10. doi: 10.11729/syltlx20220005

现代试验设计及其在空气动力学中的应用进展

doi: 10.11729/syltlx20220005
详细信息
    作者简介:

    海春龙:(1996—),男,河南许昌人,博士研究生。研究方向:试验设计方法,数值格式。通信地址:西安市长安区高桥街道西安交通大学创新港校区数学与统计学院2-2005(710049)。E-mail:15029615397@163.com

    通讯作者:

    E-mail:helei_email@163.com

  • 中图分类号: V21;TB1

Modern design of experiment and its development in aerodynamics

  • 摘要: 科学的试验设计方法能够显著提高学术研究和工业生产的质量和效率。以空气动力学试验设计为背景,介绍了现代试验设计方法的研究进展:总结了风洞试验中单因子试验设计方法OFAT(One Fact at A Time)和现代试验设计方法MDOE(Modern Design Of Experiments)在试验目的、组织策略和试验结果3个方面的区别,分析了现代试验设计方法的优势;从试验样本选取、模型建立和结果分析3个方面梳理了现代试验设计方法的现状,着重介绍了试验设计中的填充设计和序贯设计两大类试验样本选取方法;对所述试验设计方法进行了算例演示;讨论了当前存在的一些关键科学问题和未来研究方向。
  • 图  1  MDOE方法流程图

    Figure  1.  MDOE method flow chart

    图  2  BP神经网络序贯设计误差图

    Figure  2.  BP neural network sential design error plot

    图  3  Kriging序贯设计结果

    Figure  3.  Kriging sential design result plot

    图  4  气动数据图

    Figure  4.  Pneumatic data plot

    图  5  BP神经网络序贯设计误差图

    Figure  5.  BP neural network sential design error plot

    图  6  Kriging模型序贯设计误差图

    Figure  6.  Kriging sential design error plot

    表  1  BP神经网络模型误差表

    Table  1.   BP neural network model error table

    试验点数均匀设计 序贯设计
    最大误差均方差 最大误差均方差
    6 2.8434 1.4968 2.8434 1.4968
    11 3.4137 0.7648 1.9088 0.4882
    21 0.9548 0.0551 0.5627 0.0361
    41 0.3661 0.0031 0.0308 0.0002
    下载: 导出CSV

    表  2  Kriging模型误差表

    Table  2.   Kriging model error table

    试验点数均匀设计序贯设计
    最大误差均方差最大误差均方差
    6 2.2233 0.5849 2.2233 0.5849
    11 2.3497 0.4787 1.0646 0.4286
    21 0.7101 0.0172 0.3313 0.0176
    41 1.0194 0.0135 0.0690 0.0012
    下载: 导出CSV

    表  3  气动数据BP神经网络模型误差表

    Table  3.   Pneumatic data BP neural network model error table

    试验点数均匀设计序贯设计
    最大误差均方差最大误差均方差
    42 0.3242 1.7274 0.3242 1.7274
    84 0.1409 0.8970 0.1399 0.7284
    168 0.1070 0.3140 0.0503 0.0314
    336 0.0697 0.1016 0.0034 0.0014
    下载: 导出CSV

    表  4  气动数据Kriging模型误差表

    Table  4.   Pneumatic data Kriging model error table

    试验点数均匀设计序贯设计
    最大误差均方差最大误差均方差
    42 0.2837 1.4960 0.2837 1.4960
    84 0.2043 1.3659 0.1725 1.2984
    168 0.1399 0.6318 0.0712 0.0831
    336 0.1137 0.1134 0.0062 0.0077
    下载: 导出CSV
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  • 收稿日期:  2022-01-12
  • 录用日期:  2022-02-25
  • 修回日期:  2022-02-06
  • 网络出版日期:  2022-05-17
  • 刊出日期:  2022-06-25

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