DEEP LEARNING METHOD FOR FLOW FEATURE RECOGNITION BASED ON DIMENSIONLESS TIME HISTORY
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摘要: 流场的特征直接影响结构的流致振动状态，对结构绕流流场的特征分析具有重要的研究意义。在中高雷诺数与不同流速的情况下，尾流中隐含的流场特征不同，传统数学物理方法很难对其特征进行提取与识别。该文提出了采用无量纲的物理量时程进行流场特征识别的深度学习方法，消除了不同来流速度的影响，仅通过时程的时变特征进行特征识别，扩大了特征识别方法的应用范围。采用两种不同深度学习模型对三种棱柱的尾流进行了特征提取与识别，通过比较可以发现：归一化的时程中仍包含不同形状物体所引起流场的关键特征，可用于流场的特征提取；使用归一化时程进行流场特征识别可降低模型训练难度，又提高了特征提取的精度，是一种流场特征提取的新方法。Abstract: The characteristics of the flow field influence the flow-induced vibration state of structures, it thusly has a crucial significance to study the flow features around structures. However, in the case of the flow field with medium and high Reynolds numbers, the wake is highly complex, and it is not easy to extract and recognize complex features through traditional mathematical and physical methods. This paper proposes a deep learning method that uses the dimensionless time history of the flow variables to identify the flow features. The method eliminates the influence of different incoming flow speeds and only uses the time-varying features of the dimensionless time history for feature recognition, which improves the scope of the method. Two different flow time-history deep learning models are used to extract and identify the wakes of three types of prisms. Comparison results proved that unified time history maintains the critical features of wake caused by objects of different shapes. Furthermore, the unified time history can be used for the feature extraction of the flow field and improve the accuracy of the flow field feature extraction by the model. It is a feasible new method for flow field feature extraction.
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图 4 三棱柱尾流区域典型测点位置处流场参数时程
Figure 4. Time history at typical measuring points of flow field parameters
表 1 样本的标签设置
Table 1. Label settings for samples
形状 三棱柱 方柱 六棱柱 标签 1 2 3 变量 P, U, V, W P, U, V, W P, U , V, W 
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表 2 算例设置
Table 2. Setup of cases
算例 模型 样本 参数 C1 MLP 原始时程 P, U, V, W C2 MLP 归一化时程 P, U, V, W C3 FCNN 原始时程 P, U, V, W C4 FCNN 归一化时程 P, U, V, W
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表 3 各算例的特征提取精度
Table 3. Results of feature recognition accuracy
参数 C1 C2 精度提升率 C3 C4 精度提升率/(%) P 0.8598 0.9935 13.37 0.9841 0.9991 0.15 U 0.7361 0.9936 25.75 0.9926 0.9969 0.43 V 0.9995 0.9992 −0.03 0.9998 0.9975 −0.23 W 0.5930 0.6568 6.38 0.9652 0.9875 2.23
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