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Machine learning based prediction of piezoelectric energy harvesting from wake galloping

机译：基于机器学习的压电能源收割预测

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摘要

Wake galloping is a phenomenon of aerodynamic instability and has vast potential in energy harvesting. This paper investigates the vibration response of wake galloping piezoelectric energy harvesters (WGPEHs) in different configurations. In the proposed system, a stationary obstacle is placed upstream, and a cuboid bluff body mounted on a cantilever beam with piezoelectric sheets attached to it, is placed downstream. Three different types of WGPEHs were tested with different cross-section S~* of the upstream obstacles, namely square, triangular, and circular. At the same time, the tests were conducted by changing the equivalent diameter ratio η = 1 ~ 2.5 of the upstream and downstream objects, the dimen-sionless distance between two objects' centers L~* = L/D = 2 ~ 8, and the velocity span U~* = 2.93 ~ 14.54. The results reveal that S~*, η,L~* and U~* have significant effect on the vibration response of WGPEHs. Then, considering these four parameters as input features, this study has trained machine learning (ML) models to predict the root mean square values of the voltage (V_rms) and the maximum displacement (ymax), respectively. The performance of three different ML algorithms including decision tree regressor (DTR), random forest (RF), and gradient boosting regression trees (GBRT) on predicting V_rms and y_max were compared. Among them, the GBRT model performed optimally in predicting the V_rms and y_max* The GBRT model provides accurate predictions to V_rms and y_max within the test range of S~*,η, L~* and U~*.
机译：唤醒疾驰是空气动力学不稳定的现象，在能量收集方面具有巨大潜力。本文调查了不同配置唤醒宏观压电能量收割机（WGPEH）的振动响应。在所提出的系统中，将固定障碍物放置在上游，并且安装在具有附着在其上的压电片的悬臂梁上的长方体诈唬主体放置在下游。用上游障碍物的不同横截面S〜*测试了三种不同类型的WGPEH，即方形，三角形和圆形。同时，通过改变上游和下游物体的等效直径比η= 1〜2.5来进行测试，两个物体中心之间的Dimen-Sionless L〜* = L / D = 2〜8速度跨越U〜* = 2.93〜14.54。结果表明，S〜*，η，l〜*和u〜*对WGPEHS的振动响应具有显着影响。然后，将这四个参数视为输入特征，本研究已经培训了机器学习（ML）模型，以预测电压（V_RMS）的根均方值和最大位移（Ymax）。比较了三种不同ML算法的性能，包括决策树回归（DTR），随机林（RF）和渐变回归树（GBRT）预测V_RMS和Y_MAX。其中，GBRT模型在预测V_RMS和Y_MAX * GBRT模型中进行了最佳地进行了对S〜*，η，l〜*和u〜*的测试范围内的v_rms和y_max提供了准确的预测。

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  • 来源
    《Mechanical systems and signal processing》 |2021年第11期|107876.1-107876.18|共18页
  • 作者

    Chengyun Zhang; Gang Hu; Daniil Yurchenko; Pengfei Lin; Shanghao Gu; Dongran Song; Huayi Peng; Junlei Wang;

  • 作者单位

    School of Mechanical and Power Engineering Zhengzhou University Zhengzhou 450000 China;

    School of Civil and Environmental Engineering Harbin Institute of Technology Shenzhen 518055 China;

    Institute of Mechanical Process & Energy Engineering Heriot-Watt University Edinburgh EH14 4AS UK;

    School of Civil and Environmental Engineering Harbin Institute of Technology Shenzhen 518055 China;

    School of Mechanical and Power Engineering Zhengzhou University Zhengzhou 450000 China;

    School of Automation Central South University Changsha 410083 China;

    School of Civil and Environmental Engineering Harbin Institute of Technology Shenzhen 518055 China;

    School of Mechanical and Power Engineering Zhengzhou University Zhengzhou 450000 China;

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  • 正文语种 eng
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  • 关键词

    Piezoelectric energy harvesting; Wake galloping; Machine learning; Gradient boosting regression trees;

    机译：压电能量收获;醒目的疾驰;机器学习;渐变促进回归树;

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