Analysis of transport processes in a reacting flow of hybrid nanofluid around a bluff-body embedded in porous media using artificial neural network and particle swarm optimization

This paper investigates heat and mass transfer in a hybrid nanofluid flow impinging upon a cylindrical bluff-body embedded in porous media and featuring homogenous and heterogeneous chemical reactions. The analysis includes mixed convection and local thermal non-equilibrium in the porous medium as well as Soret and Dufour effects. Assuming single-phase mixture, a laminar flow of Al₂O₃-Cu-water (Aluminium oxide-Copper-water) hybrid nanofluid is considered and coupled transport processes are simulated computationally. Due to the significant complexity of this problem, containing a large number of variables, conventional approaches to parametric study struggle to provide meaningful outcomes. As a remedy, the simulation data are fed into an artificial neural network to estimate the target responses. This shows that the volume fraction of nanoparticles, interfacial area of the porous medium and mixed convection parameter, are of primary importance. It is also observed that small variation in the volume fraction of nanoparticles can considerably alter the response of thermal and solutal domains. Further, it is shown that the parameters affecting the thermal process can modify the problem chemically. In particular, raising the volume fraction of nanoparticles enhances the production of chemical species. Furthermore, particle swarm optimization is applied to predict correlations for Nusselt and Sherwood numbers through a systematic identification of the most influential parameters. The current study clearly demonstrates the advantages of using the estimator algorithms to understand and predict the behaviours of complex thermo-chemical and solutal systems.