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人工智能在空腔气动/声学特性预测与控制参数优化中的应用

吴军强 杨党国 张林 龚天弛 周方奇 王岩 李阳

吴军强,杨党国,张林,等. 人工智能在空腔气动/声学特性预测与控制参数优化中的应用[J]. 实验流体力学,2022,36(3):1-11 doi: 10.11729/syltlx20210073
引用本文: 吴军强,杨党国,张林,等. 人工智能在空腔气动/声学特性预测与控制参数优化中的应用[J]. 实验流体力学,2022,36(3):1-11 doi: 10.11729/syltlx20210073
WU J Q,YANG D G,ZHANG L,et al. Investigation on artificial intelligence for the prediction of aeroacoustic performances and controlling parameters optimization of aircraft[J]. Journal of Experiments in Fluid Mechanics, 2022,36(3):1-11. doi: 10.11729/syltlx20210073
Citation: WU J Q,YANG D G,ZHANG L,et al. Investigation on artificial intelligence for the prediction of aeroacoustic performances and controlling parameters optimization of aircraft[J]. Journal of Experiments in Fluid Mechanics, 2022,36(3):1-11. doi: 10.11729/syltlx20210073

人工智能在空腔气动/声学特性预测与控制参数优化中的应用

doi: 10.11729/syltlx20210073
基金项目: 国家自然科学基金(11732016,11972360,52130603)
详细信息
    作者简介:

    吴军强:(1965—),男,陕西咸阳人,研究员。研究方向:内外流一体化,流固声耦合与控制技术。通信地址:四川省绵阳市涪城区二环路南段6号12信箱201分信箱(621000)。E-mail:cardc_wujunqiang@163.com

    通讯作者:

    E-mail:yangdg-cardc@163.com

  • 中图分类号: V211.7

Investigation on artificial intelligence for the prediction of aeroacoustic performances and controlling parameters optimization of aircraft

  • 摘要: 多参数多条件下的精准气动特性数据是进行飞行器快速设计、系统完善、性能评估、指标考核的基本前提和根本保证。基于人工智能的深度学习技术与流体力学交叉融合已成为当前发展趋势,并在湍流模型改造、系统理论建模、气动数据预测、控制参数优化、复杂流场重构等方面得到成功应用。为最大限度发挥深度学习的强大表征能力,围绕内埋弹舱作战运用和智能优化设计需求,构建了弹舱空腔气动特性多场载荷数据库,采用基于数据驱动的深度学习方法,建立了耦合因素影响下的空腔气动/声学特性智能分析深度前馈神经网络模型,实现了有限约束条件下的空腔气动/声学特性快速预测,并引入随机搜索和贝叶斯超参数优化方法增强了模型鲁棒性,为空腔噪声有效控制模型快速优化设计提供了数据基础和方法途径。
  • 图  1  C201空腔标模[23]

    Figure  1.  C201 cavity model[23]

    图  2  空腔底部对称线上的静压和脉动压力测量点

    Figure  2.  Static and fluctuate pressure measurement points

    图  3  前缘锯齿流动控制

    Figure  3.  Leading-edge serrations for flow control

    图  4  多输入–多输出的深度人工神经网络模型

    Figure  4.  Deep artificial neural network with multiple inputs and outputs

    图  5  空腔载荷智能模型算法训练流程

    Figure  5.  Flowchart for the training of the deep forward neural network models for the prediction of cavity loads

    图  6  损失函数随迭代次数的变化

    Figure  6.  Evolution of the loss function with iteration number

    图  7  训练集内不同马赫数下空腔静压系数试验值与模型预测值对比

    Figure  7.  Comparison of static pressure coefficients along the symme-tric lines at the bottom of the cavity between the experimental data and the results predicted by the DNN model in the training data sets

    图  8  测试集内空腔底部对称线上静压系数试验值与模型预测值对比

    Figure  8.  Comparison of static pressure coefficients along the symme-tric lines at the bottom of the cavity between the experimental data and the results predicted by the DNN model in the test data sets

    图  9  训练集内不同马赫数下空腔声压级试验值与模型预测值对比

    Figure  9.  Comparison of SPL along the symmetric lines at the bottom of the cavity between the experimental data and the results predicted by the DNN model in the training data sets

    图  10  测试集内空腔声压级试验值与模型预测值对比

    Figure  10.  Comparison of static pressure coefficients along the symme-tric lines at the bottom of the cavity between the experimental data and the results predicted by the DNN model in the test data sets

    图  11  马赫数0.6、迎角–4°时,训练集内不同位置上的噪声频率特性

    Figure  11.  Comparison of spectrum at different positions of the cavity at Ma=0.6 and $\alpha $=–4° between the experimental data and the results predicted by the DNN model in the training data sets

    图  12  马赫数1.5、迎角–4°时,测试集内不同位置上的噪声频率特性

    Figure  12.  Comparison of spectrum at different positions of the cavity at Ma=0.6 and $\alpha $=–4° between the experimental data and the results predicted by the DNN model in the test data sets

    图  13  Ma=1.5时,前缘锯齿噪声控制神经网络模型预测声压级对比

    Figure  13.  Comparison of SPL between the experimental data and the results predicted by the DNN model for cavity flows at Ma=1.5 with leading-edge serrations noise control

    图  14  齿角固定为60o时不同底高和齿高参数空间内最大声压级降幅值

    Figure  14.  Maximum decrease of SPL with different h and e

    图  15  前缘锯齿参数优化前后腔内声压级对比

    Figure  15.  Comparison of SPL for cavity flows with original leading-edge serrations control and optimized serrations control

    表  1  不同前缘锯齿流动控制装置几何参数

    Table  1.   Different geometric parameters of the leading-edge serra-tions for flow control

    组别底高h/mm齿高e/mm齿角β/(°)
    14.273.4660
    26.001.7360
    36.006.9360
    403.0030
    503.0045
    603.0060
    71.002.0030
    81.002.0045
    91.002.0060
    1001.0045
    1101.7045
    120.402.0045
    131.402.0045
    142.102.0045
    下载: 导出CSV

    表  2  深度神经网络模型超参数范围

    Table  2.   Ranges of hyper-parameters in DNN model

    nlayernneuralβeff
    3~108~5010–3
    下载: 导出CSV

    表  3  静压系数智能预测模型的训练集和测试集

    Table  3.   Training and test sets for the prediction model of the static pressure coefficient

    马赫数迎角α/(°)
    训练集0.6–6,–4,–2,0,2
    0.9–6,–4,0,2
    1.5–6,–4,–2,0,2
    2.0–6,–4,–2,0,2
    测试集0.9–2
    下载: 导出CSV

    表  4  声压级智能预测模型的训练集和测试集

    Table  4.   Training and test sets for the prediction model of the sound pressure level

    马赫数迎角α/(°)
    训练集0.6–6,–4,–2,0,2
    0.9–6,–2,0,2
    1.5–6,–4,–2,0,2
    2.0–6,–4,–2,0,2
    测试集0.9–4
    下载: 导出CSV

    表  5  频谱智能预测模型的训练集和测试集

    Table  5.   Training and test sets for the prediction model of sound spectrum

    马赫数迎角α/(°)
    训练集0.6–6,–4,–2,0,2
    0.9–6,–4,–2,0,2
    1.5–6,–2,0,2
    2.0–6,–4,–2,0,2
    测试集1.5–4
    下载: 导出CSV

    表  6  前缘锯齿噪声控制试验参数及智能预测模型的训练集

    Table  6.   Training sets for the prediction model of SPL with leading-edge serrations for noise control

    底高h/mm齿高e/mm齿角β/(°)迎角α/(°)
    4.273.4660–4,–2,2
    6.001.7360–6,–2,0,2
    6.006.9360–6,–4,–2,0,2
    03.0030–4,–2,0,2
    03.0045–2,0,2
    03.0060–6,–2,0,2
    1.002.0030–6,–2,0
    1.002.0045–6,–4,–2,0
    1.002.0060–6,–4,–2,0,2
    01.0045–6,–4,–2
    01.7045–6,–4,–2,0,2
    0.402.0045–4,–2,0,2
    1.402.0045–6,–4,–2,0,2
    2.102.0045–4,–2,0,2
    下载: 导出CSV

    表  7  前缘锯齿噪声控制试验参数及智能预测模型的测试集

    Table  7.   Test sets for the prediction model of SPL with leading-edge serrations for noise control

    底高h/mm齿高e/mm齿角β/(°)迎角α/(°)
    4.273.4660–6,0
    6.001.7360–4
    03.0030–6
    03.0045–4,–6
    03.0060–4
    1.002.0030–4,2
    1.002.00452
    01.0045–2,2
    0.402.0045–6
    2.102.0045–6
    下载: 导出CSV

    表  8  不同迎角下前缘锯齿结构优化后的参数及其噪声控制效果

    Table  8.   Optimized geometrical parameters for the leading-edge serrations and its noise control result

    马赫数迎角
    α/(°)
    原声压级
    下降值
    LSP/dB
    优化结构参数优化后的
    声压级
    LSP/dB
    h/mme/mmβ/(°)
    1.5 2 –9.69 1.80 6.85 60 –12.34
    0 –8.62 2.00 6.85 60 –11.22
    –2 –7.59 2.20 6.85 60 –10.23
    –4 –7.44 2.20 6.85 60 –10.04
    –6 –7.94 2.40 6.85 60 –10.57
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-07-19
  • 录用日期:  2022-01-26
  • 修回日期:  2021-12-28
  • 网络出版日期:  2022-05-07

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