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融入压力分布信息的气动力建模方法

赵旋 张伟伟 邓子辰

赵旋, 张伟伟, 邓子辰. 融入压力分布信息的气动力建模方法. 力学学报, 2022, 54(9): 1-11 doi: 10.6052/0459-1879-22-170
引用本文: 赵旋, 张伟伟, 邓子辰. 融入压力分布信息的气动力建模方法. 力学学报, 2022, 54(9): 1-11 doi: 10.6052/0459-1879-22-170
Zhao Xuan, Zhang Weiwei, Deng Zichen. Aerodynamic modeling method incorporating pressure distribution information. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(9): 1-11 doi: 10.6052/0459-1879-22-170
Citation: Zhao Xuan, Zhang Weiwei, Deng Zichen. Aerodynamic modeling method incorporating pressure distribution information. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(9): 1-11 doi: 10.6052/0459-1879-22-170

融入压力分布信息的气动力建模方法

doi: 10.6052/0459-1879-22-170
基金项目: 国家自然科学基金项目(基金号)资助
详细信息
    作者简介:

    张伟伟, 教授, 主要研究方向: 计算流体力学、智能空气动力学、气动弹性力学. E-mail:aeroelastic@nwpu.edu.cn

  • 中图分类号: V211.3

AERODYNAMIC MODELING METHOD INCORPORATING PRESSURE DISTRIBUTION INFORMATION

  • 摘要: 气动外形优化设计与飞行器性能分析中, 直接运用数值模拟或风洞实验获取气动力的成本高, 构建代理模型是提高外形优化和性能分析效率的重要途径. 然而, 构建模型的过程中, 研究者只关注积分后的气动力和力矩信息. 本文通过充分利用采样过程中所产生的压力分布信息, 来提高建模的精度和泛化性, 进而降低样本获取的成本. 提出了一种小样本框架下融入压力分布信息的气动力建模方法, 首先通过数值模拟或风洞试验获得不同流动参数状态下翼型表面的压力分布信息和气动系数, 其次通过本征正交分解技术对压力分布信息进行特征提取, 获取不同输入参数状态下压力分布信息对应的POD系数, 之后结合输入参数通过Kriging算法对压力分布信息进行建模, 将压力分布信息积分得到低精度气动系数的预测模型, 最后低精度气动系数结合输入参数通过Kriging算法构造高精度的气动系数预测模型. 通过同状态变翼型算例以及CAS350翼型变状态算例进行验证, 该方法相比于传统的克里金模型直接预测气动力, 有效提高了气动力的预测精度和模型的鲁棒性, 同时缩小了学习样本的数据量.

     

  • 图  1  算法框架流程

    Figure  1.  Algorithm framework process

    图  2  翼型样本空间

    Figure  2.  Airfoil samples space

    图  3  测试样本翼型

    Figure  3.  Test sample airfoils

    图  4  POD阶数对预测精度的影响

    Figure  4.  Effect of POD order on prediction accuracy

    图  5  训练样本数=30 前三阶压力分布基函数

    Figure  5.  Training number=30 The first three-order pressure distribution basis function

    图  6  训练样本数=30 测试集中POD反算得到的压力分布与CFD仿真对比

    Figure  6.  Training number=30 Comparison of pressure distribution obtained by POD inverse calculation and CFD simulation

    图  7  直接建模同间接建模误差对比

    Figure  7.  Comparison of error between direct modeling and indirect modeling

    图  8  训练样本数=30预测误差分布带

    Figure  8.  Training number=30 Prediction error distribution band

    图  9  训练样本数=70预测误差分布带

    Figure  9.  Training number=70 Prediction error distribution band

    图  10  CAS350翼型外形

    Figure  10.  CAS350 airfoil shape

    图  11  CAS350翼型部分压力分布曲线

    Figure  11.  Partial pressure distribution curve of CAS350 airfoil

    图  12  直接建模同间接建模误差对比

    Figure  12.  Comparison of error between direct modeling and indirect modeling

    图  13  样本数=116单次建模误差对比

    Figure  13.  Sample number=116 Comparison of single modeling error

    图  14  样本数=116预测误差分布带

    Figure  14.  Sample number =116 Prediction error distribution band

    表  1  气动系数预测误差

    Table  1.   Prediction error of aerodynamic coefficient

    305070
    Absolute errorDirect_CL0.00960.00840.0070
    Indirect_CL0.00710.000590.0054
    Direct_CM0.000860.000730.00064
    Indirect_CM0.000840.000710.00063
    PercentageCL26.04%29.76%22.56%
    CM2.32%2.74%1.56%
    Relative errorDirect_CL0.02330.01980.0192
    Indirect_CL0.01760.01540.0149
    Direct_CM0.07670.06620.0612
    Indirect_CM0.07610.06520.0593
    下载: 导出CSV

    表  2  气动系数预测误差

    Table  2.   Prediction error of aerodynamic coefficient

    4080116
    Absolute errorDirect_CL0.05880.04110.0376
    Indirect_CL0.05530.03100.0268
    Direct_CM0.006600.004250.00365
    Indirect_CM0.006500.004230.00329
    PercentageCL5.95%24.57%28.82%
    CM1.51%0.47%9.86%
    Relative errorDirect_CL0.18720.15270.1139
    Indirect_CL0.17140.12430.0826
    Direct_CM0.56790.34160.2053
    Indirect_CM0.56030.33050.2045
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-04-21
  • 录用日期:  2022-06-23
  • 网络出版日期:  2022-06-24

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