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    Integrating artificial intelligence in investigating magneto-bioconvection flow of oxytactic microorganisms and nano-enhanced phase change material in H-type cavity
    (Elsevier, 2024) Hussain, Shafqat; Aly, Abdelraheem M.; Alsedias, Noura; Çolak, Andaç Batur
    Nano-enhanced phase change materials is an effective way to improve the thermal characteristics and to minimize energy consumption. The bioconvection flow of nano-enhanced phase change materials is gaining more attention in recent investigations due to its significant applications in engineering and medical sciences. The present study aims to numerically explore magneto-bioconvection flow of nano-enhanced phase change materials in H-type cavity including oxytactic microorganisms. The cavity is constantly heated from the left and a right wall is maintained at cold temperature. The major focus of the current investigation is analyzing the flow and thermal features of the suspension of nano-enhanced phase change materials and a host fluid. The governing system is reduced to the dimensionless form by applying the appropriate transformation. Impact of pertinent parameters, porosity, cavity aspect ratio, Darcy, Hartmann, Lewis, Rayleigh, bioconvection Rayleigh numbers, radiation parameter, and Péclet number on bioconvection flow of oxytactic microorganisms in H-type cavity has been analyzed. Six various artificial neural network models are explored in order to estimate critical parameters with an artificial intelligence approach. It is found that the variations of a cavity aspect ratio are enhancing the bioconvection flow and phase change material. Increasing Hartmann number reduces the nanofluid velocity and distributions of oxygen and microorganisms. The Rayleigh and bioconvection Rayleigh numbers are playing an importance role in enhancing bioconvection flow and varying phase change material.As Ha increases from 10 to 100, at ?=900, there is a 1.67% decrease in the values of Nuavg and a 0.247% increase in Shavg. Among the study findings, the developed artificial neural networks can predict each parameter with high accuracy.
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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.