Numerical investigation of magnetized Boger nanofluids with ohmic heating under permeable revolving disk: LMANN approach

Abstract

For a variety of applications, the flow of fluid that is induced by rotating disks has become a problem that is worth addressing. Nanofluids that flow over rotating disks display a complicated relationship between their behaviour, the viscosity of their fluid, and the inertia of their fluid properties. The applications including polymer-based treatment, reactive systems, accelerators, storage equipment, lubricating processes, nuclear reactors, a MHD-based generators, and microfluidics all require improvements in heat transfer and performance. These improvements are necessary. Within the scope of this investigation, an LMANN-numerical method is utilized to investigate a magnetised Boger nanofluid that undergoes heating by ohmic current while rotating. The Levenberg–Marquardt Artificial Neural Network(LMANN), in conjunction via a back propagation based artificial neural network, is utilised in order for investigating the steady, incompressible movement of a Boger nanofluid. The investigation focuses on mathematical modelling and characterization of Boger nanofluids flowing with a spinning disc. The bvp4c algorithm is utilized in order to acquire numerical solutions corresponding to the reduced equations. The bvp4c solver is then utilised in order to simulate the numerical behaviour that has been observed. The research investigates the impacts of relevant parameters on profiles of temperature, concentration, velocity, and motile microbes, as well as the Nusselt number and the Sherwood number. An artificial neural network that is driven by the Levenberg-Marquardt algorithm includes the additional feature of being able to simulate and forecast flow behaviour in an extremely efficient manner. A training dataset consisting of 70 % is followed by a validation dataset consisting of 15 % and a testing dataset including 15 %. Simulations using numerical data and the Levenberg–Marquardt Artificial Neural Network(LMANN) approach are in good agreement. For the purpose of evaluating and validating Levenberg–Marquardt Artificial Neural Network (LMANN) performance with relation to variations in physical parameters, the study made use of regression techniques, training process procedure (MSE) analyzing graphs. The utilization of this hybrid framework allows for the efficient management of heat management systems in industrial applications. When it comes to developing accurate predictive models, this study will be helpful to scientists and engineers who are interested in understanding flow characteristics and how they behave. Increasing the magnetic parameter from M  = 0.5 to M  = 2.0 changes the skin friction coefficient by 51.7 %, reflecting the retarding Lorentz force on the flow; increasing the Brownian-motion parameter from Nb = 0.5 to Nb = 2.0 reduces the local Nusselt number by 66.4 %, as enhanced nanoparticle motion raises the near-wall fluid temperature and weakens the wall temperature gradient. These results are relevant to the design of rotating-disk contactors, MHD-based pumps and generators, and spinning-disk bioreactors, where the present LMANN surrogate offers a computationally inexpensive route to predicting heat- and mass-transfer rates across operating conditions without repeatedly re-solving the full boundary value problem.