This computational work is exploited in an isosceles right triangular cavity to delineate the effect of different shapes of base hot wall i.e. smooth, triangular zig-zag (TZ) and caterpillar shape (CS) on natural convection cooling. The side and inclined wall of the enclosure is presumed as cold walls. The two dimensional mass, momentum and energy equations are solved by finite volume method based on SIMPLE algorithm. The results are drawn for different types of hot wall with varying aspect ratio (σ) and Rayleigh number (10 5 -10 7 ). From the investigation, it is found that the heat transfer rate is increased for both triangular zig-zag and caterpillar curve shape walls and with the increase of their aspect ratios but the maximum rate of heat transfer is obtained for caterpillar shape.
The study of laminar mixed convection is conducted in an air filled the lid-driven triangular cavity with a static circular cylinder. The circular cylinder is fixed at the center of the triangular cavity whose diameter is varied with respect to the height of the cavity and the variation is shown in terms of aspect ratio (AR = D/H). Sides of the cavity and the cylinder are maintained at uniform temperature whereas the cylinder temperature is considered higher than the walls of the cavity. Finite volume method is adopted to solve the governing equations. The present problem is investigated for different constraint parameters such as moving walls, different geometries of the hot wall, different aspect ratio, Richardson number (Ri) and Grashof number (Gr). The outcomes of the investigation reported that the average Nusselt number is found significant at AR = 1/2, Ri = 0.1 and Gr = 105. An Artificial Neural Network (ANN) tool is employed in the study to validate the obtained numerical results of stream functions and Nusselt number.
The present numerical study is carried out for mixed convection in a nanofluid-filled lid-driven triangular cavity. The base wall of the cavity is in a caterpillar shape, which is assumed as a hot wall while the side and inclined walls are considered as cold walls. The finite volume method along with the SIMPLE algorithm is used to discretize the governing equations. The study is evaluated for constrained parameters, such as volume fraction of the nanoparticles, sliding direction of the side wall, Richardson number, and Grashof number. Fluid flow and heat transfer are presented in terms of streamlines and isotherms and rate of enhancement has been shown by local and average Nusselt number. It is observed from the study that the heat transfer rate is enhanced for each volume fraction of nanoparticles, for both directions of sliding wall, Richardson number, and Grashof number. The obtained numerical results are validated with the predicted results of artificial neural network (ANN). Good agreement is reported between the numerical results and the predicted results.
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