Impact of chemical reaction on the thermal stability of micropolar nanofluid with rough boundaries and passive control on nanoparticles: Neural networking

Abstract

This paper demonstrates the impact of chemical reactions on the onset of thermal convection in micropolar nanofluid using no flux and rough boundaries. For solving rough boundaries, we have implemented a Saffman-interface condition. Here, we consider the micropolar non-Newtonian nanofluid, which has an important role in many industrial applications. For linear stability, we use the normal mode technique (NMT) to convert the controlling system of a partial differential equation (PDE) into an eigenvalue problem (EVP) and solve it numerically using the finite difference based three stage Lobatto IIIa method. The stationary mode is found to be the dominant mode of convection. Increasing the values of coupling coefficient between heat flux and spin Hall effect (δ), the coupling coefficient between spin Hall effects and vorticity (K), roughness parameters (λ₁, λ₂), chemical reaction parameter (Cr) and modified diffusivity ratio (N_A) and parameter of micro-polarity (A) in the nanofluid delays the convection, thus stabilizing the system. Later, with 81 data points, an artificial neural network (ANN) embedded with multiplayer perception (MLP) is used to determine the relationship between four controlling parameters and Rayleigh critical number.

Keywords

nanoliquid / Rayleigh-Benard convection flow / FDM method / rough surface / chemical reaction / neural computing

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