Flow field reconstruction for Bolund Island based on CNN

LI Yifeng; XUE Ming; WU Dingcong; ZHANG Wei; NI Zhangsong

doi:10.7638/kqdlxxb-2023.0012

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Flow field reconstruction for Bolund Island based on CNN

更新时间：2026-05-28

- Flow field reconstruction for Bolund Island based on CNN
- Vol. 41, Issue 9, Pages: 38-47(2023)
- 作者机构：
  
  1. 西南科技大学土木工程与建筑学院,绵阳,621010
  2. 成都流体动力创新中心,成都,610072
- 作者简介：
- 基金信息：
- DOI：10.7638/kqdlxxb-2023.0012
  CLC： TP183;P425.3
- Published：2023
- 稿件说明：
移动端阅览
李怡锋, 薛明, 吴定聪, 等. 基于卷积神经网络的Bolund岛流场重构[J]. 空气动力学学报, 2023, 41(9): 38−47.

LI Yifeng, XUE Ming, WU Dingcong, et al. Flow field reconstruction for Bolund Island based on CNN[J]. 2023, 41(9): 38-47.
李怡锋, 薛明, 吴定聪, 等. 基于卷积神经网络的Bolund岛流场重构[J]. 空气动力学学报, 2023, 41(9): 38−47. DOI： 10.7638/kqdlxxb-2023.0012

LI Yifeng, XUE Ming, WU Dingcong, et al. Flow field reconstruction for Bolund Island based on CNN[J]. 2023, 41(9): 38-47. DOI： 10.7638/kqdlxxb-2023.0012.

摘要

基于卷积神经网络的流场重构方法是风资源评估领域的一种新兴方法，具有成本低、周期短、精度高等优点，在风电场建设评估和风资源利用等领域具有较强的学术价值和工程应用背景。以风资源评估中常用的Bolund岛为研究对象，采用CFD数值模拟的输入风廓线和输出流场为样本标签，构建了以多层融合卷积神经网络为基础的流场重构方法。以缩短计算时间为目标，研究了样本量对流场重构方法精度的影响，同时对重构方法的外插能力进行了分析。研究结果表明：建立的基于卷积神经网络的流场重构方法可以较好地还原流场信息；当重构目标处于训练样本范围内，全场最大重构误差不高于2%，且在样本量从100减小到25时，全场最大重构误差仍在5%以内；构建的重构方法具有一定的外插能力，但其能力有限，当重构目标超出训练样本范围，全场最大重构误差增加到20%以上。

Abstract

Flow field reconstruction based on the convolutional neural network (CNN) is a new method in the field of wind resource assessment. It has the advantages of low cost

short evaluation and high precision for the wind farm construction and wind resource utilization

which provides values in both academic research and engineering practice. By selecting Bolund Island as a case study

the flow field reconstruction method based on a multi-layer fusion convolution neural network has been developed

which adopts sample labels of the input wind profile and the output flow field as used in the CFD. Aiming at shortening the computational time

this study examines the influence of the sample number on the accuracy of the flow field reconstruction method

and analyzes its extrapolation ability in the meantime. The results suggest that the flow field reconstruction method based on CNN can restore the flow field information well. The maximum reconstruction error is less than 2% when the reconstruction target is within the training sample range

and it keeps within 5% when the number of samples is reduced from 100 to 25. The reconstruction method has some extrapolation ability

but is limited within some range. When the reconstruction target is beyond the training sample range

the maximum reconstruction error rises from less than 5% to more than 20%.

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Keywords

references

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