Vortex identification and visualization are important means to understand the underlying physical mechanism of the flow field. Local vortex identification methods need to combine with the manual selection of the appropriate threshold, which leads to poor robustness. Global vortex identification methods are of high computational complexity and time-consuming. Machine learning methods are related to the size and shape of the flow field, which are weak in versatility and scalability. It cannot be extended and is suitable for flow fields of different sizes. Recently, proposed deep learning methods have long network training time and high computational complexity. Aiming at the above problems, we present a novel vortex identification method based on the Convolutional Neural Networks-Extreme Learning Machine (CNN-ELM). This method transforms the vortex identification problem into a binary classification problem, and can quickly, objectively, and robustly identify vortices from the flow field. A large number of experiments prove the effectiveness of our method, which can improve or supplement the shortcomings of existing methods.

中文翻译：

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基于极限学习机的涡流识别方法

涡流识别和可视化是了解流场潜在物理机制的重要手段。局部涡旋识别方法需要与手动选择适当的阈值相结合，这会导致鲁棒性差。全局涡旋识别方法具有很高的计算复杂度和耗时。机器学习方法与流场的大小和形状有关，但通用性和可伸缩性较弱。它不能扩展，适用于不同大小的流场。最近，提出的深度学习方法具有较长的网络训练时间和较高的计算复杂度。针对上述问题，我们提出了一种基于卷积神经网络-极限学习机（CNN-ELM）的涡旋识别新方法。该方法将涡流识别问题转换为二元分类问题，并且可以快速，客观，可靠地从流场识别涡流。大量实验证明了本方法的有效性，可以改善或弥补现有方法的不足。