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Wind and Structures
  Volume 38, Number 4, April 2024 (Special Issue) pages 231-244
DOI: https://doi.org/10.12989/was.2024.38.4.231
 


Reconstruction of wind speed fields in mountainous areas using a full convolutional neural network
Ruifang Shen, Bo Li, Ke Li, Bowen Yan, Yuanzhao Zhang

 
Abstract
    As wind farms expand into low wind speed areas, an increasing number are being established in mountainous regions. To fully utilize wind energy resources, it is essential to understand the details of mountain flow fields. Reconstructing the wind speed field in complex terrain is crucial for planning, designing, operation of wind farms, which impacts the wind farm's profits throughout its life cycle. Currently, wind speed reconstruction is primarily achieved through physical and machine learning methods. However, physical methods often require significant computational costs. Therefore, we propose a Full Convolutional Neural Network (FCNN)-based reconstruction method for mountain wind velocity fields to evaluate wind resources more accurately and efficiently. This method establishes the mapping relation between terrain, wind angle, height, and corresponding velocity fields of three velocity components within a specific terrain range. Guided by this mapping relation, wind velocity fields of three components at different terrains, wind angles, and heights can be generated. The effectiveness of this method was demonstrated by reconstructing the wind speed field of complex terrain in Beijing.
 
Key Words
    complex mountain; convolution; deconvolution; surrogate model; wind speed fields reconstruction
 
Address
Ruifang Shen:School of Civil Engineering, Chongqing University, 400045, China

Bo Li:1)School of Civil Engineering, Chongqing University, 400045, China 2)Beijing's Key Laboratory of Structural Wind Engineering and Urban Wind Environment, Beijing 100044, China

Ke Li:1)School of Civil Engineering, Chongqing University, 400045, China 2)Key Laboratory of New Technology for Construction of Cities in Mountain Area (Chongqing University),Ministry of Education, Chongqing, 400045, China

Bowen Yan:1)School of Civil Engineering, Chongqing University, 400045, China 2)Key Laboratory of New Technology for Construction of Cities in Mountain Area (Chongqing University),Ministry of Education, Chongqing, 400045, China

Yuanzhao Zhang:School of Civil Engineering, Beijing Jiaotong University, Beijing 100044, China
 

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