Browse > Article
http://dx.doi.org/10.5407/jksv.2021.19.3.031

Prediction of Energy Harvesting Efficiency of an Inverted Flag Using Machine Learning Algorithms  

Lim, Sehwan (Department of Mechanical Engineering, Seoul National University of Science and Technology)
Park, Sung Goon (Department of Mechanical and Automotive Engineering, Seoul National University of Science and Technology)
Publication Information
Journal of the Korean Society of Visualization / v.19, no.3, 2021 , pp. 31-38 More about this Journal
Abstract
The energy harvesting system using an inverted flag is analyzed by using an immersed boundary method to consider the fluid and solid interaction. The inverted flag flutters at a lower critical velocity than a conventional flag. A fluttering motion is classified into straight, symmetric, asymmetric, biased, and over flapping modes. The optimal energy harvesting efficiency is observed at the biased flapping mode. Using the three different machine learning algorithms, i.e., artificial neural network, random forest, support vector regression, the energy harvesting efficiency is predicted by taking bending rigidity, inclination angle, and flapping frequency as input variables. The R2 value of the artificial neural network and random forest algorithms is observed to be more than 0.9.
Keywords
Machine learning; Inverted flag; Energy harvesting;
Citations & Related Records
연도 인용수 순위
  • Reference
1 L. Brelman, Random forests., Machine Learning 45, 5-32, 2001.   DOI
2 W. S. Noble, What is a support vector machine?., Nature biotechnology 24, 1565-1567, 2006.   DOI
3 X.-F. He and J. Gao, Wind energy harvesting based on flow-induced-vibration and impact. Microelectronic Engineering 111, 82-86, 2013.   DOI
4 H. Kim, S. Kang and D. Kim, Dynamics of a flag behind a bluff body., Journal of Fluids and Structures 71, 1-14, 2017.   DOI
5 K. Shoele and R. Mittal, Energy harvesting by flow-induced flutter in a simple model of an inverted piezoelectric flag., Journal of Fluid Mechanics 790, 582-606, 2016.   DOI
6 G. W. Taylor, J. R. Burns, S. M. Kammann, W. B. Powers and T. R. Welsh, The Energy Harvesting Eel: a small subsurface ocean/river power generator., IEEE Journal of Oceanic Engineering 26(4), 539-547, 2001.   DOI
7 M. Riedmiller, Advanced supervised learning in multi-layer perceptrons - From backpropagation to adaptive learning algorithms, Computer Standards & Interfaces 16, 265-278, 1994.   DOI
8 Kwon, B., Ejaz, F., and Hwang, L. K., Machine Learning for Heat Transfer Correlations., International Communications in Heat and Mass Transfer, 116, 104694, 2020   DOI
9 S. Michelin and O. Doare, Energy harvesting efficiency of piezoelectric flags in axial flows., Journal of Fluid Mechanics 714, 489-504, 2013.   DOI
10 J. J. Allen and A. J. Smits, Energy harvesting eel., Journal of Fluids and Structures 15, 629-640, 2001.   DOI
11 D. Kim, J. Cosse, C. H. Cerdeira, and M. Gharib, Flapping dynamics of an inverted flag., Journal of Fluid Mechanics 736, R1, 2013.   DOI
12 J. Ryu, S. G. Park, B. Kim and H. J. Sung, Flapping dynamics of an inverted flag in a uniform flow., Journal of Fluids and Structures 57, 159-169, 2015.   DOI
13 M. Babanezhad, I. Behroyan, A. Taghvaie Nakhjiri, M. Rezakazemi, A. Marjani, S. Shirazian, Prediction of turbulence eddy dissipation of water flow in a heated metal foam tube., Scientific Reports, 10, 2020, Article 19280