Artificial neural network valid at ion of MHD natural bioconvection in a square enclosure : entropic analysis and optimization
Thisstudynumericallyinvestigatesinclinedmagneto-hydrodynamicnaturalconvectioninaporouscavityfilledwithnanofluid containinggyrotacticmicroorganisms.Thegoverningequationsarenondimensionalizedandsolvedusingthefinitevolume method. The simulations examine the impact of keyparameters suchas heat source lengthandposition, Peclet number, porosity,andheatgeneration/absorptiononflowpatterns, temperaturedistribution,concentrationprofiles,andmicroorganism rotation.Resultsindicatethatextendingtheheatsourcelengthenhancesconvectivecurrentsandheattransferefficiency,while optimizing the heat sourceposition reduces entropygeneration.Higher Peclet numbers amplify convective currents and microorganismdistribution complexity.Variations inporosityandheat generation/absorption significantly influence flow dynamics. Additionally, the artificial neural networkmodel reliably predicts themeanNusselt andSherwood numbers ( ) Nu Sh & ,demonstratingitseffectiveness for suchanalyses.Thesimulationresults reveal that increasingtheheat source lengthsignificantlyenhancesheat transfer, asevidencedbya15%increaseinthemeanNusseltnumber.