Heat and mass transport of nano-encapsulated phase change materials in a complex cavity: An artificial neural network coupled with incompressible smoothed particle hydrodynamics simulations

Abstract

<abstract> <p>This work simulates thermo-diffusion and diffusion-thermo on heat, mass transfer, and fluid flow of nano-encapsulated phase change materials (NEPCM) within a complex cavity. It is a novel study in handling the heat/mass transfer inside a highly complicated shape saturated by a partial layer porous medium. In addition, an artificial neural network (ANN) model is used in conjunction with the incompressible smoothed particle hydrodynamics (ISPH) simulation to forecast the mean Nusselt and Sherwood numbers ($ \stackrel{-}{Nu} $ and $ \stackrel{-}{Sh} $). Heat and mass transfer, as well as thermo-diffusion effects, are useful in a variety of applications, including chemical engineering, material processing, and multifunctional heat exchangers. The ISPH method is used to solve the system of governing equations for the heat and mass transfer inside a complex cavity. The scales of pertinent parameters are fusion temperature $ {\theta }_{f} = 0.05-0.95 $, Rayleigh number $ Ra = {10}^{3}-{10}^{6} $, buoyancy ratio parameter $ N = -2-1 $, Darcy number $ Da = {10}^{-2}-{10}^{-5} $, Lewis number $ Le = 1-20 $, Dufour number $ Du = 0-0.25 $, and Soret number $ Sr = 0-0.8 $. Alterations of Rayleigh number are effective in enhancing the intensity of heat and mass transfer and velocity field of NEPCM within a complex cavity. The high complexity of a closed domain reduced the influences of Soret-Dufour numbers on heat and mass transfer especially at the steady state. The fusion temperature works well in adjusting the intensity and location of a heat capacity ratio inside a complex cavity. The presence of a porous layer in a cavity's center decreases the velocity field within a complex cavity at a reduction in Darcy number. The goal values of $ \stackrel{-}{Nu} $ and $ \stackrel{-}{Sh} $ for each data point are compared to those estimated by the ANN model. It is discovered that the ANN model's $ \stackrel{-}{Nu} $ and $ \stackrel{-}{Sh} $ values correspond completely with the target values. The exact harmony of the ANN model prediction values with the target values demonstrates that the developed ANN model can forecast the $ \stackrel{-}{Nu} $ and $ \stackrel{-}{Sh} $ values precisely.</p> </abstract>