STUDY ON FLOW FIELD PARAMETERS OF WAKE TIME HISTORY TARGET RECOGNITION
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摘要: 浸入流场中的固体壁面会形成高度复杂且具有一定特征的尾流流场, 利用尾流所包含的信息对物体的外形特征进行识别具有重要的应用价值. 然而, 在较高雷诺数情况下尾流流场形态及其时序特征复杂, 难以通过传统的数学物理方法对流场信号进行特征的识别与提取. 本文提出了基于尾流时程数据深度学习的流场特征提取与分析方法, 实现了基于一点的物理量时程进行流场中物体外形的识别; 同时, 对流场中不同物理参数时程的识别精度与识别结果进行分析与研究, 得到适用于目标识别的最优物理量参数. 通过对圆柱和方柱的尾流数据研究结果表明, 本文提出的基于卷积神经网络的模型具有好的训练收敛性和高的预测精度, 能够识别并提取得到时程数据中包含的流场特征, 采用流场横向速度时程作为物体外形识别信号的模型准确率高. 证明了本方法用于浸入流场中物体外形识别的可行性, 是一种目标识别的高精度方法.Abstract: Wall immersed in fluid will form highly complex wake flow with specific features. Therefore, the extraction and analysis of flow feature has important research value. However, in the case of high Reynolds number, the wake flow field are complex, so it is difficult to identify and extract the flow features by traditional mathematical and statistical method. In this paper, a new flow field feature extraction and analysis method based on deep learning of wake time history data is proposed, and the shape recognition based on local time history is realized; At the same time, accuracy of different time history parameter is analyzed, and the optimal physical parameters suitable for target recognition are obtained. Research results on the flow field data of cylinder and square cylinder show that the model based on convolution neural network proposed in this paper has good training convergence and high prediction accuracy, and model using transverse velocity time history has highest accuracy. At the same time, it is proved that method proposed in this paper is a new high-precision method for target recognition immersed in fluid.
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图 5 典型测点的流场参数时程
Figure 5. Time history of flow field parameters at typical measuring points
表 1 样本与标签设置
Table 1. Sample and label settings
Label Shape Number of parameters Number of samples 0 circular cylinder 6 21600 1 square cylinder 6 21600 
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表 2 完全卷积神经网络模型参数
Table 2. Time convolution neural network model parameters
Layer Kernel number Size of kernel Activation function Output size input — — — 300 convolution 128 8 ReLU 300, 128 convolution 256 5 ReLU 300, 256 convolution 128 3 ReLU 300, 128 pooling — — — 128 FCL — — — 6
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表 3 全连接网络模型参数
Table 3. Time convolution neural network model parameters
Layer Dropout parameter Number of neural Trainable parameter input − − 0 FCL 1 0.1 500 150500 FCL 2 0.2 500 250500 FCL 3 0.3 500 250500 output 0.3 2 1002
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表 4 算例设置
Table 4. Parameters of cases
Case Model Parameters Training dataset Testing dataset F1 FCN Pressure P 4325 2875 M1 MLP Pressure P 4311 2889 F2 FCN velocity U 4316 2884 M2 MLP velocity U 4328 2872 F3 FCN velocity V 4320 2880 M3 MLP velocity V 4321 2879 F4 FCN velocity W 4326 2874 M4 MLP velocity W 4308 2892 F5 FCN magnitude 4323 2877 M5 MLP magnitude 4328 2872 F6 FCN vorticity 4307 2893 M6 MLP vorticity 4317 2883
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