Numerical simulation and machine learning‑driven optimization of nanofluid flow with microbial interactions for enhanced heat transfer

The nanofluids augmented by the motile microbes exhibit better flow and thermal properties that are beneficial in various engineering and bio-medical applications. Nanofluids with improved heat transfer properties can significantly improve thermal management in microchips and circuit boards. The microbial motility in these fluids helps to maintain a stable flow. The use of nanoparticles together with motile microorganisms can also be found in biomedical devices like implants, pacemakers, or wearable health monitors that generate heat. An innovative approach is presented in this paper to analyze the nanofluid flow with microbial interactions for enhanced heat transfer. The nonlinear governing system is treated numerically with the Successive Over-Relaxation (SOR) technique, whereas a recurrent neural network (RNN) is incorporated to model and optimize the nonlinear dynamics of nanofluid flow with microbial interactions. The numerical procedure is appraised by a numerical comparison. The results are found to be in better correlation with the earlier ones. The outcomes of the study evidently disclose the fact that the temperature rises due to an increase in the thermophoresis as well as the heat generation parameter. A substantial enhancement is noticed in the microorganisms’ density with the effect of the Peclet number and the microbes’ parameter.