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    Adjoined ISPH method and artificial intelligence for thermal radiation on double diffusion inside a porous L-shaped cavity with fins
    (Emerald Publishing, 2024) Elshehabey, Hillal M.; Çolak, Andaç Batur; Aly, Abdelraheem
    Purpose: The purpose of this study is to adapt the incompressible smoothed particle hydrodynamics (ISPH) method with artificial intelligence to manage the physical problem of double diffusion inside a porous L-shaped cavity including two fins. Design/methodology/approach: The ISPH method solves the nondimensional governing equations of a physical model. The ISPH simulations are attained at different Frank–Kamenetskii number, Darcy number, coupled Soret/Dufour numbers, coupled Cattaneo–Christov heat/mass fluxes, thermal radiation parameter and nanoparticle parameter. An artificial neural network (ANN) is developed using a total of 243 data sets. The data set is optimized as 171 of the data sets were used for training the model, 36 for validation and 36 for the testing phase. The network model was trained using the Levenberg–Marquardt training algorithm. Findings: The resulting simulations show how thermal radiation declines the temperature distribution and changes the contour of a heat capacity ratio. The temperature distribution is improved, and the velocity field is decreased by 36.77% when the coupled heat Cattaneo–Christov heat/mass fluxes are increased from 0 to 0.8. The temperature distribution is supported, and the concentration distribution is declined by an increase in Soret–Dufour numbers. A rise in Soret–Dufour numbers corresponds to a decreasing velocity field. The Frank–Kamenetskii number is useful for enhancing the velocity field and temperature distribution. A reduction in Darcy number causes a high porous struggle, which reduces nanofluid velocity and improves temperature and concentration distribution. An increase in nanoparticle concentration causes a high fluid suspension viscosity, which reduces the suspension’s velocity. With the help of the ANN, the obtained model accurately predicts the values of the Nusselt and Sherwood numbers. Originality/value: A novel integration between the ISPH method and the ANN is adapted to handle the heat and mass transfer within a new L-shaped geometry with fins in the presence of several physical effects
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    Integrating artificial intelligence with numerical simulations of Cattaneo-Christov heat flux on thermosolutal convection of nano-enhanced phase change materials in Bézier-annulus
    (Elsevier, 2024) Elshehabey, Hillal M.; Aly, Abdelraheem M.; Lee, Sang-Wook; Çolak, Andaç Batur
    The numerical analysis based on incompressible smoothed particle hydrodynamics (ISPH) is introduced to examine the impacts of Cattaneo-Christov (Ca-Ch) heat flux and exothermic chemical reaction on thermosolutal convection of nano-enhanced phase change materials (NEPCM) in Bézier-annulus. The used annulus is formed between inner connected Bézier curves and outer connected spline-Bézier curves. The inner shape of connected Bézier curves is maintained at Th&Ch, left/right walls of spline-Bézier curves are kept at Tc&Cc and other walls are adiabatic. The governing equations, after being converted into non-dimensional form, have been solved by ISPH which is an accurate meshless algorithm the treatment of internal flows inside complex geometries. The simulations are executed for Frank-Kamenetskii number FK, Ca-Ch heat flux ?C, buoyancy ratio parameter N, Soret-Dufour numbers SrDu, Rayleigh number Ra, and nanoparticle parameter ? on thermosolutal convection of a suspension fluid. From the numerical simulation values for the average Nusselt and Sherwood numbers (Nu¯, Sh¯) were obtained for some fluid flow scenarios. Then, based on those values an artificial neural network (ANN) model was developed to predict the values of Nu¯, and Sh¯ without the need to perform the ordinary simulations again for the new cases which is a high cost compared to ANN. From the obtained simulations, it was concluded that the ANN model is an accurate tool to be used to predict the needed values. Also, the Frank-Kamenetskii number significantly influences the enhancement process of the temperature distributions and velocity field as well as phase change material in Bézier-annulus.