Neural network-based analysis of thermal and mass transfer in nanofluid flow through a microchannel under pollutant concentration effect
摘要
The efficient thermal management of electronic devices remains barrier in the advancement of global digitalization. A major challenge to global digitalization is managing the cooling and heat control of electronic devices. Recently, scientific breakthroughs have been the result of creative and inventive research using machine learning technique in order to reach the needs of society. The integration of machine learning in research has catalyzed novel scientific discoveries pertinent to current challenges. The study investigates nanofluid flow within micro-channels, incorporating the Buongiorno model to assess the impacts of microorganisms, nanoparticle concentrations, and waste discharge. Employing similarity transformations, the study converts partial differential equations into dimensionless ordinary differential equations, subsequently addressed via the fourth-order Runge–Kutta technique. Furthermore, a novel application of artificial neural networks is presented to predict the thermal and solutal transport phenomena in micro-channel utilizing a Levenberg–Marquardt-based neural network. The main findings of this investigation demonstrate that the thermal transfer rate model achieves its validation performance at epochs 882, exhibiting an incredibly best MSE of