Thermal analysis of bioconvection flow of CNTs/water based hybrid nanofluid with gyrotactic microbes using artificial neural network

Abstract This study uses the Levenberg–Marquardt strategy with feed forward neural networks (LMS-FNN) to inspect the Soret-Dufour effect on radiative hybrid nanofluid flow across a Riga plate with gyrotactic microorganisms. The suggested model, which investigates the thermal behavior of bioconvection flow in CNTs/water based hybrid nanofluid, gyrotactic microbes, when considered alongside Soret-Dufour impact, contribute notably to important uses in biotechnology, energy systems, and industrial heat management. It is important to optimize bioreactors that require high microbial activity and heat transfer, to improve the design of innovative cooling systems that use hybrid nanofluid, and to aid in the creation of efficient microfluidic devices. Artificial neural networks provide accurate prediction of complex fluid-microbe interactions, which aids in the design and control of next-generation thermal and bioengineering processes. From reference results, execute LMS-FNN validation, training, and testing to get approximated solutions for variations connected with the physical system and to demonstrate the correctness of the suggested LMS-FNN. The Mean squared error, histograms, and regression analysis are used to examine the performance of LMS-FNN, and the problem is satisfactorily solved. The microorganism profile profile declines as the Peclet number increases.