超声速湍流密度脉动预测的神经网络方法

doi:10.7527/S1000-6893.2018.22244

流体力学与飞行力学

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超声速湍流密度脉动预测的神经网络方法

王正魁, 靳旭红, 朱志斌, 程晓丽

中国航天空气动力技术研究院, 北京 100074

收稿日期:2018-04-26 修回日期:2018-06-20 出版日期:2018-10-15 发布日期:2018-07-04
通讯作者: 靳旭红 E-mail:jinxuhong08@163.com

Neural network method for predicting density fluctuations in supersonic turbulence

WANG Zhengkui, JIN Xuhong, ZHU Zhibin, CHENG Xiaoli

China Academy of Aerospace Aerodynamics, Beijing 100074, China

Received:2018-04-26 Revised:2018-06-20 Online:2018-10-15 Published:2018-07-04

摘要/Abstract

摘要： 针对气动光学效应对湍流密度脉动预测的需求，发展了超声速湍流密度脉动预测的神经网络方法，从直接数值模拟（DNS）的超声速湍流边界层的流场数据中挖掘规律，建立了包含5个隐含层的密度脉动模型。实验结果表明，所发展的神经网络方法可以很好地预测密度脉动均方值，它不仅能很好地预测训练样本，对测试样本预测的精度和稳定性也显著高于传统模型，且具有一定的泛化能力。通过特征选择和加入先验信息，确定了密度脉动模型的7个输入参数特征量，进一步提高了模型的泛化能力和实用性。

关键词: 气动光学, 湍流, 密度脉动, 神经网络, 边界层

Abstract: Considering the demand for predicting density fluctuation in turbulence of in prediction of the aero-optical effect, this paper develops a neural network method to predict the density fluctuation in supersonic turbulence. A model for the density fluctuation with 5 hidden layers is built by mining the regularity from the flow field data of supersonic turbulent boundary layers, which are simulated by the Direct Numerical Simulation (DNS) method. The experimental results show that the neural network method proposed is able to predict the mean square values of density fluctuation accurately. The method can predict the training samples well, and can get the prediction of test samples with better accuracy and stability than traditional models, and has certain capability of generalization. By selecting features and adding prior information, the 7 features of input parameters of the density fluctuation model are determined to further improve the capability of generalization and practicability of the model.

Key words: aero-optics, turbulence, density fluctuation, neural network, boundary layer

中图分类号:

V211.3

王正魁, 靳旭红, 朱志斌, 程晓丽. 超声速湍流密度脉动预测的神经网络方法[J]. 航空学报, 2018, 39(10): 122244-122244.

WANG Zhengkui, JIN Xuhong, ZHU Zhibin, CHENG Xiaoli. Neural network method for predicting density fluctuations in supersonic turbulence[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2018, 39(10): 122244-122244.

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E-mail：hkxb@buaa.edu.cn

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主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

超声速湍流密度脉动预测的神经网络方法

Neural network method for predicting density fluctuations in supersonic turbulence

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