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

김준규; 류준일; 김형수

doi:10.5407/jksv.2021.19.2.032

인공신경망을 통한 2D 용질성 마랑고니 유동 액적의 용질 농도 분포 역추적 기법
Reverse tracking method for concentration distribution of solutes around 2D droplet of solutal Marangoni flow with artificial neural network 원문보기

한국가시화정보학회지= Journal of the Korean society of visualization, v.19 no.2, 2021년, pp.32 - 40

김준규 (Department of Mechanical Engineering, KAIST) , 류준일 (Department of Mechanical Engineering, KAIST) , 김형수 (Department of Mechanical Engineering, KAIST)

Abstract ▼ AI-Helper

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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표/그림 (4)

그림 Fig. 1. (a) Experimental apparatus for 2D solutal Marangoni flow (b) Top: Snapshot of particle image velocimetry image (PIV) and Bottom: PIV result.
$Fig. 2. (a) Schematic of Michell solution to calculate velocity field <TEX>$\underline{u}$</TEX> (r, <TEX>$\theta$</TEX>) inside a 2D droplet for a given solute concentration profile at the liquid-gas interface (x(<TEX>$\theta$</TEX>)) and artificial neural network as the inverse operation. (b) Structure of the artificial neural network. Velocity field <TEX>$\underline{u}$</TEX> (r, <TEX>$\theta$</TEX>) is processed with Least Squares method and Fourier sine transformation (FST). Solute concentration distribution x(<TEX>$\theta$</TEX>) is obtained from inverse Fourier cosine transformation (IFCT) of the output of the artificial neural network. S, C indicate Fourier sine and cosine coefficients, respectively. The subscript indicates the order of each coefficient.$ 그림 Fig. 2. (a) Schematic of Michell solution to calculate velocity field $\underline{u}$ (r, $\theta$) inside a 2D droplet for a given solute concentration profile at the liquid-gas interface (x($\theta$)) and artificial neural network as the inverse operation. (b) Structure of the artificial neural network. Velocity field $\underline{u}$ (r, $\theta$) is processed with Least Squares method and Fourier sine transformation (FST). Solute concentration distribution x($\theta$) is obtained from inverse Fourier cosine transformation (IFCT) of the output of the artificial neural network. S, C indicate Fourier sine and cosine coefficients, respectively. The subscript indicates the order of each coefficient.
$Fig. 3. Comparison between loss and validation loss based on artificial neural network learning where Loss = <TEX>$\frac{1}{L}{\sum}_{l=1}^{L}(c_l-{\widehat}{c}_l)^2$</TEX>$ 그림 Fig. 3. Comparison between loss and validation loss based on artificial neural network learning where Loss = $\frac{1}{L}{\sum}_{l=1}^{L}(c_l-{\widehat}{c}_l)^2$
그림 Fig. 4. Velocity field of solutal Marangoni flow inside a droplet and the estimation of the concentration of solutes along the interface. (a) PIV result of velocity field and (b) solute concentration distribution along the interface (r = R) based on the artificial neural network using reverse tracking method. Yellow arrows indicate velocity vector at each position.

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인공신경망을 통한 2D 용질성 마랑고니 유동 액적의 용질 농도 분포 역추적 기법
Reverse tracking method for concentration distribution of solutes around 2D droplet of solutal Marangoni flow with artificial neural network 원문보기

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인공신경망을 통한 2D 용질성 마랑고니 유동 액적의 용질 농도 분포 역추적 기법 Reverse tracking method for concentration distribution of solutes around 2D droplet of solutal Marangoni flow with artificial neural network 원문보기

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인공신경망을 통한 2D 용질성 마랑고니 유동 액적의 용질 농도 분포 역추적 기법
Reverse tracking method for concentration distribution of solutes around 2D droplet of solutal Marangoni flow with artificial neural network 원문보기

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