한국가시화정보학회지 (Journal of the Korean Society of Visualization)

  • 제19권3호
  • /
  • Pages.31-38
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  • 2021
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  • 1598-8430(pISSN)
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  • 2093-808X(eISSN)
한국가시화정보학회 (The Korean Society of Visualization)
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머신 러닝 알고리즘을 이용한 역방향 깃발의 에너지 하베스팅 효율 예측

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)
  • 투고 : 2021.07.23
  • 심사 : 2021.10.26
  • 발행 : 2021.12.31
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초록

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.

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과제정보

본 연구는 한국연구재단의 지원을 받아 수행되었습니다. (NRF-2021R1C1C1008791)

참고문헌

  1. S. Michelin and O. Doare, Energy harvesting efficiency of piezoelectric flags in axial flows., Journal of Fluid Mechanics 714, 489-504, 2013. https://doi.org/10.1017/jfm.2012.494
  2. X.-F. He and J. Gao, Wind energy harvesting based on flow-induced-vibration and impact. Microelectronic Engineering 111, 82-86, 2013. https://doi.org/10.1016/j.mee.2013.02.009
  3. J. J. Allen and A. J. Smits, Energy harvesting eel., Journal of Fluids and Structures 15, 629-640, 2001. https://doi.org/10.1006/jfls.2000.0355
  4. 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. https://doi.org/10.1109/48.972090
  5. H. Kim, S. Kang and D. Kim, Dynamics of a flag behind a bluff body., Journal of Fluids and Structures 71, 1-14, 2017. https://doi.org/10.1016/j.jfluidstructs.2017.03.001
  6. D. Kim, J. Cosse, C. H. Cerdeira, and M. Gharib, Flapping dynamics of an inverted flag., Journal of Fluid Mechanics 736, R1, 2013. https://doi.org/10.1017/jfm.2013.555
  7. 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. https://doi.org/10.1016/j.jfluidstructs.2015.06.006
  8. 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. https://doi.org/10.1017/jfm.2016.40
  9. Kwon, B., Ejaz, F., and Hwang, L. K., Machine Learning for Heat Transfer Correlations., International Communications in Heat and Mass Transfer, 116, 104694, 2020 https://doi.org/10.1016/j.icheatmasstransfer.2020.104694
  10. 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
  11. M. Riedmiller, Advanced supervised learning in multi-layer perceptrons - From backpropagation to adaptive learning algorithms, Computer Standards & Interfaces 16, 265-278, 1994. https://doi.org/10.1016/0920-5489(94)90017-5
  12. L. Brelman, Random forests., Machine Learning 45, 5-32, 2001. https://doi.org/10.1023/A:1010933404324
  13. W. S. Noble, What is a support vector machine?., Nature biotechnology 24, 1565-1567, 2006. https://doi.org/10.1038/nbt1206-1565