The effects of L-shaped heat source in a quarter-tube enclosure filled with MHD nanofluid on heat transfer and irreversibilities, using LBM: numerical data, optimization using neural network algorithm (ANN)

In this paper, the process of natural convection heat transfer (NCHT) and production of entropy (POF) in a portion of a tube has been modeled two-dimensionally. The examined problem is a quarter-tubular enclosure (quarter of a tube) which is filled with water-alumina nanofluid and subjected to a magnetic field (MF) of strength B0 at angle ω relative to horizon. Lattice Boltzmann Method (LBM) is used to simulate this problem. The ranges of parameters used in this investigation are: 0 < ω < 90, 0 < Ha < 60, and 0.1 < L, H < 0.5, and the obtained results include the Nusselt number (Nu), generated entropy, and Bejan number (Be). The results of thermal and dynamic analyses indicate that by growing the Hartmann number (Ha), the NCHT and POF values go up and the Bediminishes. Heat transfer is also improved by increasing the length of the enclosure’s hot walls. The highest amount of heat transfer occurs at the MF angle of 60o, and it is 10.3% greater than the amount of heat transfer occurring at horizontal MF. Finally, an artificial neural network was used to simulate the cavity performance based on these parameters. An optimization is performed on the parameters of heat source length and Ha. The optimization is aimed at finding suitable parameter values that lead to the highest heat transfer rate and lowest POF. A table listing a number of optimal points has been presented at the end of the paper. The optimization results indicate a desirability of about 0.56, which is achieved under the best conditions at defined parameter ranges. In this case, the values of Nu and POF are 7.26 and 3.54, respectively. The optimal state occurs at the non-dimensional hot wall length of about 0.5 and Ha of 37.7.