基于深度学习的超分辨率重构方法在CAARC标模绕流流场重构中的应用

梁仍康; 张伟; 杨思帆; 黄刚; 李朗

doi:10.7638/kqdlxxb-2022.0157

欢迎来到能源电力期刊网！

English Version 期刊登录

您当前的位置：

首页 >

文章列表页 >

基于深度学习的超分辨率重构方法在CAARC标模绕流流场重构中的应用

研究论文 | 更新时间：2026-05-28

- 基于深度学习的超分辨率重构方法在CAARC标模绕流流场重构中的应用
- Applications of deep learning-based super-resolution for reconstruction of flow around the CAARC benchmark model
- 空气动力学学报 2023年41卷第11期页码：116-126
- 作者机构：
  
  1. 西南科技大学土木工程与建筑学院,绵阳,621010
  2. 中国空气动力研究与发展中心超高速空气动力研究所,绵阳,621000
  3. 空天飞行空气动力科学与技术全国重点实验室,绵阳,621000
- 作者简介：
  
  梁仍康（1996—），男，广东茂名人，硕士研究生，研究方向：空气动力学. E-mail：1309603019@qq.com
  张伟*（1984—），男，副教授，研究方向：计算流体力学和机器学习研究. E-mail：zwell@mail.ustc.edu.cn
- 基金信息：
  
  国家自然科学基金（11902278）;光合基金A类项目（202302015072）;空天飞行空气动力科学与技术全国重点实验室项目
- DOI：10.7638/kqdlxxb-2022.0157
  中图分类号： O355;TP391.7;TU972+.8
- 纸质出版：2023
- 稿件说明：
移动端阅览
梁仍康, 张伟, 杨思帆, 等. 基于深度学习的超分辨率重构方法在CAARC标模绕流流场重构中的应用[J]. 空气动力学学报, 2023, 41(11): 116−126.

LIANG Rengkang, ZHANG Wei, YANG Sifan, et al. Applications of deep learning-based super-resolution for reconstruction of flow around the CAARC benchmark model[J]. 2023, 41(11): 116-126.
梁仍康, 张伟, 杨思帆, 等. 基于深度学习的超分辨率重构方法在CAARC标模绕流流场重构中的应用[J]. 空气动力学学报, 2023, 41(11): 116−126. DOI： 10.7638/kqdlxxb-2022.0157

LIANG Rengkang, ZHANG Wei, YANG Sifan, et al. Applications of deep learning-based super-resolution for reconstruction of flow around the CAARC benchmark model[J]. 2023, 41(11): 116-126. DOI： 10.7638/kqdlxxb-2022.0157.

摘要

基于深度学习的超分辨率重构方法是近年来发展的一种有效的流场精细化方法。本文超分辨率重构模型以卷积神经网络为基础，结合了混合下采样跳跃连接多尺度模型，并应用于CAARC标准建筑模型表面风压场和建筑绕流速度场的重构。通过对比分析对不同欠分辨率流场的高分辨重构能力，结果表明该深度学习模型重构高分辨率流场具有良好的精度，重构效果优于原始的卷积神经网络模型和传统的双三次插值方法。该方法具有一定的普适性，可推广应用到具有复杂湍流流动的任意建筑结构风场的超分辨率重构。

Abstract

The deep-learning-based super-resolution reconstruction methods developed in recent years are effective methods to obtain detailed flow fields. A deep learning-based super-resolution reconstruction method was applied to reconstructing high-resolution wind field of flow around building structures in this paper. The super-resolution reconstruction model was based on the convolutional neural network (CNN) and combined with the mixed downsampled skip-connection multi-scale (Multi-scale CNN) model. The super-resolution reconstruction model was applied to the reconstruction of the surface pressure on and the velocity field around the CAARC benchmark model. The reconstruction ability of the deep learning-based model for different under-resolution flow fields was investigated. The results show that the proposed deep learning model can greatly enhance the super-resolution reconstruction performance and the reconstruction accuracy is better than the original convolutional neural network model and the traditional bicubic interpolation method. Due to its universal applicability

this method can be extended to super-resolution reconstruction of wind field of any building structure with complex turbulent flow.

关键词

Keywords

references

HOLMES J D. Wind loading of structures[M]. Florida: CRC Press, 2001.

WU B G, WU J R, FU J Y. Non-stationary analysis on field measured wind speed data of tall buildings[J]. Acta Aerodynamica Sinica, 2014, 32(3): 410-415(in Chinese).

HUANG J, GU M. Experimental investigation of blockage effects on mean pressure of rectangular tall buildings in the wind tunnel[J]. Acta Aerodynamica Sinica, 2015, 33(3): 414-419, 426(in Chinese).

CAO H L, QUAN Y, GU M. Wind tunnel test on across-wind aerodynamic damping of rectangular super-high-rise buildings[J]. Acta Aerodynamica Sinica, 2014, 32(3): 351-359(in Chinese).

ZHU W L, YANG Q S, CAO S Y. Applicability research on the turbulent inflow for building LES simulation[J]. Acta Aerodynamica Sinica, 2011, 29(1): 124-128(in Chinese).

YANG X, WANG C Q, FEI S M. An adaptive method for simultaneous image super-resolution and motion estimation based on nonlinear least square[J]. Journal of Image and Graphics, 2010, 15(12): 1714-1719(in Chinese).

SUN R H, SHI L F, HUANG L Y, et al. Reversible data hiding based on image interpolation and reference matrix[J]. Journal of Guangxi Normal University(Natural Science Edition), 2019, 37(2): 90-104. doi: 10.16088/j.issn.1001-6600.2019.02.011

XI Z H, YUAN K P. Super-resolution image reconstruction based on residual channel attention and multilevel feature fusion[J]. Laser Optoelectronics Progress, 2020, 57(4): 041504 (in Chinese). doi: 10.3788/lop57.041504

ZHAO J T. Single-image defogging algorithm based on deep learning[J]. Laser Optoelectronics Progress, 2019, 56(11): 146-153(in Chinese).

DONG C, LOY C C, HE K M, et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2): 295-307. DOI: 10.1109/TPAMI.2015.2439281

DONG C, LOY C C, TANG X O. Accelerating the super-resolution convolutional neural network[M]//Computer Vision - ECCV 2016. Cham: Springer International Publishing, 2016: 391-407. doi: 10.1007/978-3-319-46475-6_25

FUKAMI K, FUKAGATA K, TAIRA K. Super-resolution reconstruction of turbulent flows with machine learning[J]. Journal of Fluid Mechanics, 2019, 870: 106-120. DOI: 10.1017/jfm.2019.238

FUKAMI K, FUKAGATA K, TAIRA K. Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows[J]. Journal of Fluid Mechanics, 2021, 909: A9. DOI: 10.1017/jfm.2020.948

LIU B, TANG J P, HUANG H B, et al. Deep learning methods for super-resolution reconstruction of turbulent flows[J]. Physics of Fluids, 2020, 32(2): 025105. DOI: 10.1063/1.5140772

KIM H, KIM J, WON S, et al. Unsupervised deep learning for super-resolution reconstruction of turbulence[J]. Journal of Fluid Mechanics, 2021, 910: A29. DOI: 10.1017/jfm.2020.1028

BRUNTON S L, NOACK B R, KOUMOUTSAKOS P. Machine learning for fluid mechanics[J]. Annual Review of Fluid Mechanics, 2020, 52: 477-508. DOI: 10.1146/annurev-fluid-010719-060214

MELBOUME W H. Comparison of measurements on the CAARC standard tall building model in simulated model wind flows[J]. Journal of Wind Engineering and Industrial Aerodynamics, 1980, 6(1-2): 73-88. DOI: 10.1016/0167-6105(80)90023-9

ELSHAER A, ABOSHOSHA H, BITSUAMLAK G, et al. LES evaluation of wind-induced responses for an isolated and a surrounded tall building[J]. Engineering Structures, 2016, 115: 179-195. DOI: 10.1016/j.engstruct.2016.02.026

LU C L, LI Q S, HUANG S H, et al. Simulation of large eddy of wind load on a long-span complex roof structure[J]. China Civil Engineering Journal, 2011, 44(1): 1-10(in Chinese).

SKYTTE M, THORDAL, . Review for practical application of CFD for the determination of wind load on high-rise buildings[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2019, 186: 155-168. DOI: 10.1016/j.jweia.2018.12.019

DAGNEW A K, BITSUAMLAK G T. Computational evaluation of wind loads on a standard tall building using LES[J]. Wind and Structures, 2014, 18(5): 567-598. DOI: 10.12989/was.2014.18.5.567

YAN B W, LI Q S. Inflow turbulence generation methods with large eddy simulation for wind effects on tall buildings[J]. Computers Fluids, 2015, 116: 158-175. DOI: 10.1016/j.compfluid.2015.04.020

YUE, ZHANG, . Predicting wind-induced vibrations of high-rise buildings using unsteady CFD and modal analysis[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2015, 136: 165-179. DOI: 10.1016/j.jweia.2014.11.008

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于卷积神经网络的Bolund岛流场重构

基于深度学习的非定常周期性流动预测方法

基于深度神经网络的粒子图像测速算法

基于U-Net的磁浮飞行风洞气动力干扰剥离算法

飞行器模型表面三维温度场重建方法