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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.