利用深度学习技术的二维过渡流动分析

Lee Tae Hwan, Park,Jin-Hyun
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引用次数: 0

摘要

对二维瞬态流在矩形障碍物上的流动进行了分析。并对每种纵横比下的190个速度分布进行成像,为卷积神经网络学习提供输入数据。根据给定的速度分布,采用分类和回归方法估计纵横比。结果表明,该分类方法比回归方法更精确。但从成像速度分布来看,分类和回归方法均能较准确地预测出定义的纵横比。这证实了深度学习技术适用于流分析。
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딥러닝 기법을 사용한 이차원 과도유동 해석
The flow analysis of two dimensional transient flow over the obstacles with rectangular cross sections was performed. And 190 velocity distributions for each aspect ratio were imaged to provide input data for convolutional neural network learning. The classification and regression methods were used in estimating the aspect ratio from given velocity distributions. As a result the classification method was more exact than the regression method. But both the classification and regression methods gave relatively accurate prediction of the defined aspect ratio judging from the imaged velocity distributions. This confirms that the deep learning technique is applicable to the flow analysis.
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