A machine learning investigation of the ZnO–water nanofluid flow with magnetic field through convergent and divergent channels: a numerical study

Abstract

This investigation focuses on how ZnO–H₂O nanofluid flow affects the thermal processes in divergent/convergent channels under the effects of magnetohydrodynamics (MHD). Advanced machine learning techniques are utilised to comprehensively quantify the interactions among the implications of nanoparticles on thermal transmission and fluid actions. Our analysis encompasses the MHD and Joule emission effects, transforming the intricate system of nonlinear system of equations to ordinary differential equations (ODEs) following the strategy of non-dimensionalisation. Leveraging the integration of artificial neural networks (ANN) integrated a hybrid approach for numerical solutions. The optimum value of ANN-based fitness function is up to 5.53$\times {10}^{-5}$. The optimal weights and biases of ANN through ABC–WCA are ranging from $-10$ to$10$. These results are meticulously compared with shooting method and homotopy perturbation method (HPM) solutions. The absolute error between the ANN, shooting and HPM are equal up to four (4) decimal places. Further, the ANN-based fitness function optimised through ABC-WCA over 250 independent runs for the efficiency of algorithms. It is found that the current results are much more authentic and powerful than the existing less converging and slow analytic and numerical methods.

KEYWORDS:

• Artificial neural networks (ANNs)
• water cycle algorithm (WCA)
• artificial bee colony (ABC)
• hybrid approach
• nanofluids
• convergent and divergent channels

Disclosure statement

No potential conflict of interest was reported by the author(s).