Bioconvection flow of NEPCM and oxytactic microorganisms within a porous circular cylinder containing a circular cylinder fin using an artificial neural network in conjunction with the ISPH method

This article investigates the bioconvection flow within a porous circular cylinder filled with oxytactic microorganisms and incorporating nano-encapsulated phase change materials (NEPCMs). The circular cylinder, equipped with T-fins, is situated within the cylinder's domain. The governing equations are solved using the ISPH method based on a time-fractional derivative. An ISPH simulation, coupled with an artificial neural networks (ANN) model, is employed to predict the values of the average Nusselt number (Nu¯). Utilizing bioconvection flow within a porous circular cylinder containing oxytactic microorganisms and integrating NEPCMs presents opportunities for progress in thermal management, environmental engineering, energy storage, and materials science. During the conducted simulations, we have investigated the impacts of Darcy, Lewis, Peclet, Rayleigh, and bioconvection Rayleigh numbers on a range of parameters, encompassing isotherms, heat capacity ratio, concentration profiles of oxygen and microorganisms, velocity field, and Nu¯. The results obtained highlight the significant influence of changes in the inner cylinder's radius and fin length on regulating the intensity of bioconvection flow and improving heat transfer within a cavity. Additionally, the fusion temperature causes a shift in the heat capacity ratio toward the heat sources located within the cavity. The velocity field decreases by 97.39% as the Darcy number decreases from 10−2 to 10−5 due to the challenges posed by porous media. Increasing the length of the T-fin enhances the cooling area, causing the isotherms to contract and altering the heat capacity ratio. In comparison to the target values, the ANN model demonstrates accurate prediction of Nu¯, bolstering confidence in its predictions.