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基于多模态融合的任意对称翼型结冰预测方法

屈经国 王强 彭博 易贤

屈经国, 王强, 彭博, 等. 基于多模态融合的任意对称翼型结冰预测方法[J]. 航空动力学报, 2024, 39(1):20220143 doi: 10.13224/j.cnki.jasp.20220143
引用本文: 屈经国, 王强, 彭博, 等. 基于多模态融合的任意对称翼型结冰预测方法[J]. 航空动力学报, 2024, 39(1):20220143 doi: 10.13224/j.cnki.jasp.20220143
QU Jingguo, WANG Qiang, PENG Bo, et al. Icing prediction method for arbitrary symmetric airfoil using multimodal fusion[J]. Journal of Aerospace Power, 2024, 39(1):20220143 doi: 10.13224/j.cnki.jasp.20220143
Citation: QU Jingguo, WANG Qiang, PENG Bo, et al. Icing prediction method for arbitrary symmetric airfoil using multimodal fusion[J]. Journal of Aerospace Power, 2024, 39(1):20220143 doi: 10.13224/j.cnki.jasp.20220143

基于多模态融合的任意对称翼型结冰预测方法

doi: 10.13224/j.cnki.jasp.20220143
基金项目: 国家自然科学基金重点基金(12132019); 国家重大科技专项(J2019-Ⅲ-0010-0054); 国家自然科学面上基金(12172372)
详细信息
    作者简介:

    屈经国(1996-),男,硕士生,主要从事深度学习与结冰预测方面的研究。E-mail:xvyn@qq.com

    通讯作者:

    易贤(1977-),男,研究员,博士,主要从事航空宇航科学与技术方面的研究。E-mail:yixian_2000@163.com

  • 中图分类号: V211.41

Icing prediction method for arbitrary symmetric airfoil using multimodal fusion

  • 摘要:

    为解决目前绝大多数神经网络冰形预测方法只能针对特定翼型且不具备面向多翼型特征的普适性的问题,采用基于多模态融合的深度神经网络方法,以翼型截面图像与结冰工况参数作为输入,以二维冰形曲线傅里叶级数拟合参数作为输出,建立深度神经网络预测模型,实现了对任意对称翼型结冰特征的预测能力。结果表明:提出的模型可以准确地预测任意对称翼型几何特征条件下的结冰外形,冰形面积与最大冰厚等冰形主要参数预测误差均保持在10%以下。

     

  • 图 1  结冰快速预测输入条件

    Figure 1.  Ice accumulation prediction input conditions

    图 2  研究流程框图

    Figure 2.  Research flow diagram

    图 3  网络结构

    Figure 3.  Network structure

    图 4  典型训练集翼型截面

    Figure 4.  Typical training set airfoil sections

    图 5  结冰外形的坐标转换[19]

    Figure 5.  Coordinate transformation of ice accretion shape[19]

    图 6  测试集翼型截面

    Figure 6.  Test set airfoil sections

    图 7  冰形特征参数[9]

    Figure 7.  Ice shape characteristic parameters[9]

    图 8  预测霜冰冰形与数值计算冰形的比较

    Figure 8.  Comparison of prediction rime ice shape and numerical calculation ice shape

    图 9  预测明冰冰形与数值计算冰形的比较

    Figure 9.  Comparison of prediction glaze ice shape and numerical calculation ice shape

    表  1  结冰工况参数取值

    Table  1.   Parameter values for icing conditions

    参数取值
    飞行攻角α/(°)3
    来流速度v/(m/s)90
    来流温度t/℃−35, −30, −25, −20, −15, −10
    液态水含量Clw/(g/m30.1, 0.2, 0.3, 0.4, 0.5, 0.7, 0.9, 1.1, 1.3, 1.5
    水滴平均体积直径Dmv/μm15, 20, 25, 30, 40, 50, 60
    结冰时长τ/min22.5
    下载: 导出CSV

    表  2  4组算例工况参数

    Table  2.   Parameters of 4 cases

    算例翼型α/(°)v/(m/s)t/℃Clw/(g/m3Dmv/μmτ/min
    1EPPLER 479390−300.42522.5
    2STCYR172390−350.55022.5
    3RAF 30390−301.54022.5
    4Ryan BQM-34390−100.95022.5
    下载: 导出CSV

    表  3  冰形特征参数相对误差

    Table  3.   Relative error of ice shape characteristic parameters

    算例损失
    函数
    相对误差/%
    SuSlSicehsphuhlθuθl
    1MSE2.401.536.281.61
    Huber7.097.8517.550.49
    2MSE13.6415.951.550.97
    Huber4.579.784.888.90
    3MSE3.2018.890.477.194.762.100.220.27
    Huber16.6316.1214.270.657.551.540.240.15
    4MSE0.3524.864.257.605.107.040.250.32
    Huber5.6921.496.008.016.018.480.220.20
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-03-21
  • 网络出版日期:  2023-09-04

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