Journal of the Korean Society of Visualization (한국가시화정보학회지)

The Korean Society of Visualization (한국가시화정보학회)
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Reverse tracking method for concentration distribution of solutes around 2D droplet of solutal Marangoni flow with artificial neural network

인공신경망을 통한 2D 용질성 마랑고니 유동 액적의 용질 농도 분포 역추적 기법

  • Kim, Junkyu (Department of Mechanical Engineering, KAIST) ;
  • Ryu, Junil (Department of Mechanical Engineering, KAIST) ;
  • Kim, Hyoungsoo (Department of Mechanical Engineering, KAIST)
  • Received : 2021.07.14
  • Accepted : 2021.08.22
  • Published : 2021.08.31
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Abstract

Vapor-driven solutal Marangoni flow is governed by the concentration distribution of solutes on a liquid-gas interface. Typically, the flow structure is investigated by particle image velocimetry (PIV). However, to develop a theoretical model or to explain the working mechanism, the concentration distribution of solutes at the interface should be known. However, it is difficult to achieve the concentration profile theoretically and experimentally. In this paper, to find the concentration distribution of solutes around 2D droplet, the reverse tracking method with an artificial neural network based on PIV data was performed. Using the method, the concentration distribution of solutes around a 2D droplet was estimated for actual flow data from PIV experiment.

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References

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