Machine learning based prediction of piezoelectric energy harvesting from wake galloping

Abstract

Wake galloping is a phenomenon of aerodynamic instability and has vast potential in
energy harvesting. This paper investigates the vibration response of wake galloping piezo-
electric 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 g ¼ 1 2:5 of the upstream and downstream objects, the dimen-
sionless distance between two objects’ centers L ¼ L=D ¼ 2 8, and the velocity span
U ¼ 2:93 14:54. The results reveal that S , g, 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 val-
ues of the voltage (V rms ) and the maximum displacement (ymax), respectively. The perfor-
mance of three different ML algorithms including decision tree regressor (DTR), random
forest (RF), and gradient boosting regression trees (GBRT) on predicting V rms and ymax were
compared. Among them, the GBRT model performed optimally in predicting the V rms and
ymax . The GBRT model provides accurate predictions to V rms and ymax within the test range of S , g, L and U.

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Identifiers

ORCID for Daniil Yurchenko: orcid.org/0000-0002-4989-3634

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