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Artificial Intelligence-Driven FVM-ANNModel for Entropy Analysis ofMHD natural bioconvection in Nanofluid-Filled Porous Cavities

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Frontiers in Heat and Mass Transfer 2024年第5期22卷 1277-1307页

作者： Noura Alsedais Mohamed Ahmed Mansour Abdelraheem M.Aly Sara I.Abdelsalam Department of Mathematical Sciences College of SciencePrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Mathematics Department Faculty of SciencesAssiut UniversityAssiut71515Egypt Department of Mathematics College of ScienceKing Khalid UniversityAbha62529Saudi Arabia Basic Science Faculty of EngineeringThe British University in EgyptAl-Shorouk CityCairo11837Egypt Instituto de Ciencias Matemáticas ICMAT CSICUAMUCMUC3MMadrid28049Spain

The research examines fluid behavior in a porous box-shaped *** fluid contains nanoscale particles and swimming microbes and is subject to magnetic forces at an *** circulation driven by biological factors is *** analysis combines a traditional numerical approach with machine learning *** equations describing the system are transformed into a dimensionless form and then solved using computational *** artificial neural network(ANN)model,trained with the Levenberg-Marquardt method,accurately predicts(Nu)values,showing high correlation(R=1),low mean squared error(MSE),and minimal error *** analysis reveals significant effects of parameters,length and location of source(B),(D),heat generation/absorption coefficient(Q),and porosity parameter(ε).Increasing the cooling area length(B)reduces streamline intensity and local Nusselt and Sherwood numbers,while decreasing isotherms,isoconcentrations,and *** Bejan number(Be+)decreases with increasing(B),whereas(Be+++),and global entropy(e+++)*** in(Q)slightly affect streamlines but reduce isotherm intensity and average Nusselt ***(D)significantly impacts isotherms,iso-concentrations,andmicro-rotation,altering streamline contours and local Bejan number ***(ε)enhances streamline strength and local Nusselt number profiles but has mixed effects on average Nusselt *** findings highlight the complex interactions between cooling area length,fluid flow,and heat transfer *** combining finite volume method(FVM)with machine learning technique,this study provides valuable insights into the complex interactions between key parameters and heat transfer,contributing to the development of more efficient designs in applications such as cooling systems,energy storage,and bioengineering.

关键词： ANN model finite volume method natural bioconvection flow magnetohydrodynamics(MHD) porous media

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