Artificial neural network joined with lattice Boltzmann method to study the effects of MHD on the slip velocity of FMWNT/water nanofluid flow inside a microchannel

Present research evaluates a wide range of methods to boost the performance of microchannel via ANN & lattice Boltzmann method (LBM). Hence Artificial Nueral Network method by Levenberg-Marquardt algorithm is applied to find all the data in the specific domain. LBM is developed to join with the artificial neural network (ANN) to study the effects of magneto hydro dynamic (MHD) on the slip velocity of FMWNT/water nanofluid flow inside a microchannel. Two methods are used to increase the efficiency of the cooling system. One is the effect of several fins (rectangular, triangular, and trapezoidal) and the second is the effect of magnetic field on thermo-hydraulic behavior. The magnetic field is caused to reduce the thermal boundary layer in the vicinity of the microchannels which means augmenting the performance by diminishing the vortexes. Finally, by rising the slip velocity coefficient from 0 to 0.1, the heat transfer saw a substantial enhancement of 6.1% and 5.4% in microchannel with rectangular at the lowest and highest amount of magnetic field, respectively. The original correlations regarding calculation of Nuave was obtained with R-square around 0.99.

Automated summary

This research evaluates various methods to enhance the performance of microchannels using Artificial Neural Network (ANN) and lattice Boltzmann method (LBM). The study combines LBM with ANN to investigate the impact of magneto hydro dynamic (MHD) on slip velocity of nanofluid flow inside microchannels. The efficiency of the cooling system is increased by using multiple fins and applying a magnetic field to reduce thermal boundary layer and enhance heat transfer.