TY - JOUR

T1 - Boundary Heat Flux Estimation for Natural Convection in a Square Enclosure Containing a Cylinder

T2 - An Inverse Approach

AU - Jakkareddy, Pradeep S.

AU - Pandey, Sudhanshu

AU - Ha, Man Yeong

N1 - Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) through a grant awarded by the Korean government (MSIT) (NRF-2019R1A5A8083201).
Publisher Copyright:
© 2023, King Fahd University of Petroleum & Minerals.

PY - 2023/9

Y1 - 2023/9

N2 - In this study, the unknown boundary heat fluxes in a square enclosure containing a cylinder were estimated by an inverse technique. A series of computations was conducted for the two-dimensional, steady-state, and buoyancy-driven heat transfer in a square section containing a cylinder with variable heat fluxes and at a Rayleigh number (Ra) of 106 and Prandtl number (Pr) of 0.7. The generated datasets were used to construct a physics-based neural network, which acted as a proxy model for natural convection to reduce the computational time for inverse estimation. The trained network was embedded in a genetic algorithm and Bayesian framework to estimate the boundary conditions of the heat fluxes from synthetic experimental temperatures. The results indicated that the genetic algorithm accurately predicted the heat flux, but the estimation failed with increasing measurement error/noise. The solutions of the genetic algorithm were then used as informative priors for the Bayesian framework, which outperformed the genetic algorithm at estimating unknown boundary heat fluxes with measurement noise. The estimated heat fluxes were then used as input for the direct problem and investigated the thermal and flow characteristics in an enclosure containing a cylinder.

AB - In this study, the unknown boundary heat fluxes in a square enclosure containing a cylinder were estimated by an inverse technique. A series of computations was conducted for the two-dimensional, steady-state, and buoyancy-driven heat transfer in a square section containing a cylinder with variable heat fluxes and at a Rayleigh number (Ra) of 106 and Prandtl number (Pr) of 0.7. The generated datasets were used to construct a physics-based neural network, which acted as a proxy model for natural convection to reduce the computational time for inverse estimation. The trained network was embedded in a genetic algorithm and Bayesian framework to estimate the boundary conditions of the heat fluxes from synthetic experimental temperatures. The results indicated that the genetic algorithm accurately predicted the heat flux, but the estimation failed with increasing measurement error/noise. The solutions of the genetic algorithm were then used as informative priors for the Bayesian framework, which outperformed the genetic algorithm at estimating unknown boundary heat fluxes with measurement noise. The estimated heat fluxes were then used as input for the direct problem and investigated the thermal and flow characteristics in an enclosure containing a cylinder.

KW - Bayesian inference

KW - Genetic algorithm

KW - Heat flux

KW - Inverse heat transfer

KW - Natural convection

UR - http://www.scopus.com/inward/record.url?scp=85152281573&partnerID=8YFLogxK

U2 - 10.1007/s13369-023-07678-z

DO - 10.1007/s13369-023-07678-z

M3 - Article

AN - SCOPUS:85152281573

SN - 2193-567X

VL - 48

SP - 12439

EP - 12453

JO - Arabian Journal for Science and Engineering

JF - Arabian Journal for Science and Engineering

IS - 9

ER -