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首页> 外文期刊>Journal of visualization >Application of fully convolutional neural networks for feature extraction in fluid flow
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Application of fully convolutional neural networks for feature extraction in fluid flow

机译：全卷积神经网络在流体流动中提取的应用

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摘要

Accurate extraction of features in fluid flows is of importance due to the presence in many natural and technological systems. Recently, methods based on machine learning have emerged as an alternative to traditional Eulerian-based methods to extract features in fluid flows. One broad category in ML is the convolution operation-based methods. The precision of feature extraction in convolution operation-based methods increases by constraining the measurement box, such as dividing the input data into small patches and using them as input boxes in convolutional neural networks. In this work, we propose a method that transforms each cell of the computational domain into a detection pixel (measurement box) to perform the task of feature extraction at the smallest possible computational level. To demonstrate the performance, we extract the vortical structures in a benchmark two-dimensional lid-driven cavity flow employing a symmetric, fully convolutional network. The number of convolution and deconvolution blocks in the network's structure is studied to obtain the highest accuracy and yet to avoid the degradation problem. Different parameters, such as the Reynolds number and velocity boundary values, are considered in the complete and clipped cavity cases to create the training and test datasets. The semantic segmentation metrics, including Jaccard and Dice, yield values close to 1 for the test set on complete and clipped cavity cases with varying the Reynolds number or the velocity boundary values.
机译：由于许多自然和技术系统中存在，精确提取流体流体的特征是重要的。最近，基于机器学习的方法已经成为传统的基于欧拉的方法的替代方法，以提取流体流中的特征。 ML中的一个广泛类别是基于卷积的操作的方法。通过约束测量盒，基于卷积操作的方法的特征提取的精度增加，例如将输入数据划分为小块，并将它们用作卷积神经网络中的输入框。在这项工作中，我们提出了一种方法，该方法将计算域的每个小区转换为检测像素（测量框），以在最小可能的计算级别执行特征提取的任务。为了演示性能，我们在采用对称的全卷积网络中提取基准二维盖驱动腔流量中的涡流结构。研究了网络结构中的卷积和解卷积块的数量，以获得最高的精度，但却避免了劣化问题。在完整和剪裁的腔箱中，考虑不同的参数，例如雷诺数和速度边界值，以创建训练和测试数据集。语义分割度量，包括Jaccard和Dice，屈服值接近1，用于在完整和剪裁腔箱上的测试设置，其具有改变雷诺数或速度边界值。

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