Details

Autor(en) / Beteiligte

• Rani, Hari Ponnamma
• Leela, V.
• Nagabhushana, Pulla
• Reddy, R. Gangadhara

Titel

Heat Transfer Characteristics of Mixed Convective Magnetohydrodynamic Counter-current Two-Phase Flow in Rotating Inclined Microporous Channel With Slip Velocity and Asymmetric Thermal Boundary Conditions Using LTNE Model

Ist Teil von

• Journal of thermal science and engineering applications, 2022-06, Vol.14 (6)

Erscheinungsjahr

2022

Beschreibungen/Notizen

• Abstract The heat transfer characteristics of a mixed convective two-phase flow in an inclined rotating microporous channel kept in a transverse magnetic field are investigated numerically. The counterflow arrangement is assumed within the channel. Slip velocity and asymmetric thermal boundary conditions are assumed. The governing energy equation involves the local thermal non-equilibrium (LTNE) between the two phases. The LTNE implications of the control parameters on the flow field variables and the average Nusselt number, Nu, are highlighted, and pertinent observations are documented. When confined to a few specific cases, the current results are consistent with previous research work. The effect of inclination angle on fluid velocity is determined by the wall temperature difference ratio. According to the findings, for certain values of the wall temperature differential ratio, the velocity increases with the angle; however, it takes on a dual character for other values. The Nusselt number (Nu) is expected to increase with the Biot number, Hartmann number, and rotation parameter, while Nu decreases as the Knudsen number increases. The results also show that as the wall temperature ratio increases, the Nu converges to a common minimum value. This research combines computational fluid dynamics (CFD) simulation and artificial neural network (ANN) analysis. The database was generated from the validated CFD model covering a range of control parameters arising in the system. The multilayer perceptron (MLP) networks were trained using this CFD data set to predict Nu. It is observed that the predicted data given by the ANN model is in good accordance with the estimated values of Nu. The average relative error in Nu's prediction is found to be ±2%.