Optimizing wind energy harvester with machine learning

Shun WENG; Liying WU; Zuoqiang LI; Lanbin ZHANG; Huliang DAI

doi:10.1007/s10483-025-3279-6

Applied Mathematics and Mechanics >

2025 , Vol. 46 >Issue 8: 1417 - 1432

DOI: https://doi.org/10.1007/s10483-025-3279-6

Optimizing wind energy harvester with machine learning

Shun WENG ,
Liying WU ,
Zuoqiang LI ,
Lanbin ZHANG ,
Huliang DAI

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^1.School of Civil and Hydrolic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
^2.Faculty of Engineering, China University of Geosciences, Wuhan 430074, China
^3.School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Huliang DAI, E-mail: daihulianglx@hust.edu.cn

Received date: 2025-03-05

Revised date: 2025-06-02

Online published: 2025-07-28

Supported by

Project supported by the National Key R&D Program of China (No. 2021YFF0501001), the National Natural Science Foundation of China (Nos. 52308315, 51922046, and 52192661), the Research Funds of Huazhong University of Science and Technology (No. 2023JCYJ014), the China Postdoctoral Science Foundation (No. 2023M731206), the Research Funds of China Railway Siyuan Survey and Design Group Co. Ltd. (Nos. KY2023014S, KY2023126S, 2021K085, 2020K006, and 2020K172), and the Autonomous Innovation Fund of Hubei Province of China (No. 5003242027)

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Abstract

Optimizing wind energy harvesting performance remains a significant challenge. Machine learning (ML) offers a promising approach for addressing this challenge. This study proposes an ML-based approach using the radial basis function neural network (RBFNN) and differential evolution (DE) to predict and optimize the structural parameters (the diameter of the spherical bluff body $D$ , the total spring stiffness $k$ , and the length of the piezoelectric cantilever beam $L$ ) of the wind energy harvester (WEH). The RBFNN model is trained with theoretical data and validated with wind tunnel experimental results, achieving the coefficient-of-determination scores $R 2$ of 97.8% and 90.3% for predicting the average output power $P avg$ and aero-electro-mechanical efficiency $η aem$ , respectively. The DE algorithm is used to identify the optimal parameter combinations for wind speeds $U$ ranging from 2.5 m/s to 6.5 m/s. The maximum $P avg$ is achieved when $D = 57.5$ mm, $k = 28.8$ N/m, $L = 112.1$ mm, and $U = 4.6$ m/s, while the maximum $η aem$ is achieved when $D = 52.7$ mm, $k = 29.2$ N/m, $L = 89.2$ mm, and $U = 4.7$ m/s. Compared with that of the non-optimized structure, the WEH performance is improved by 28.6% in $P avg$ and 19.1% in $η aem$ .

Key words： wind energy harvester (WEH); vortex-induced vibration (VIV); piezoelectric effect; machine learning (ML); radial basis function neural network (RBFNN); differential evolution (DE)

Cite this article

Shun WENG , Liying WU , Zuoqiang LI , Lanbin ZHANG , Huliang DAI . Optimizing wind energy harvester with machine learning[J]. Applied Mathematics and Mechanics, 2025 , 46(8) : 1417 -1432 . DOI: 10.1007/s10483-025-3279-6

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