| 基于超分辨率重构方法的湍流流场重构 |
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| 引用本文： | 江昊,王伯福,庄启亮,卢志明.基于超分辨率重构方法的湍流流场重构[J].实验流体力学,2022,36(3):102-109. |
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| 作者姓名： | 江昊 王伯福 庄启亮 卢志明 |
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| 作者单位： | 1.上海大学力学与工程科学学院 上海市应用数学和力学研究所，上海 200072 |
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| 基金项目： | 国家自然科学基金（12072185，11732010，11972220） |
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| 摘 要： | 从低分辨率流场数据中获取精细流场信息具有重要的研究意义。基于卷积神经网络的超分辨率重构方法是近年来发展的一种较为有效的精细流场重构方法。本文采用高效亚像素卷积神经网络（Efficient Sub-Pixel Convolutional Neural Network，ESPCN），对Rayleigh–Bénard（RB）对流的数值模拟数据和湍流边界层（Turbulent Boundary Layer，TBL）的实验测量数据进行了超分辨率重构，并与双三次插值方法（Bicubic Interpolation）的重构结果进行对比。对比结果表明:在较小的下采样比下，ESPCN方法和Bicubic方法的重构精度相当；在较大的下采样比下，ESPCN方法的重构精度明显优于Bicubic方法。此外，ESPCN方法对数据梯度较大区域的超分辨率重构效果优于Bicubic方法。
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| 关 键 词： | 超分辨率重构方法 湍流 卷积神经网络 流场重构 |
| 收稿时间： | 2021-11-29 |
Reconstruction of turbulent fields based on super-resolution reconstruction method |
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| Affiliation: | 1.Shanghai Institute of Applied Mathematics and Mechanics, School of Mechanics and Engineering Science, Shanghai University, Shanghai 200072, China2.Shanghai Frontiers Science Base for Mechanoinfomatics, Shanghai 200072, China |
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| Abstract: | It is an important issue to obtain detailed flow fields from limited flow fields data. The convolutional-neural-networks-based super-resolution reconstruction methods developed in recent years are effective methods to obtain detailed flow fields. The efficient sub-pixel convolutional neural network（ESPCN） method is used to reconstruct Rayleigh–Bénard（RB） convection numerical simulation data and turbulent boundary layer（TBL） experimental measured data, and obtain high resolution flow fields data. The reconstructed high resolution flow fields data obtained using ESPCN is then compared to the results from the traditional super-resolution reconstruction method, the bicubic interpolation method. The results indicate that the flow fields reconstructed by the ESPCN method and the bicubic method agree well with the original high-resolution flow fields data when the down-sampling ratio is small. But, when the down-sampling ratio is large, the accuracy of the flow fields reconstructed by the ESPCN method is significantly better than that constructed by the bicubic method. In addition, the ESPCN method has a better performance than the bicubic method in areas with large gradients. |
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