Mixed Convection Heat Transfer Enhancement in Lid-Driven Cavities Filled with Nanofluids

Mixed convection heat transfer in enclosures has been studied in order to enhance the associated heat transfer performance through the use of either different convective fluid types, domain configurations, boundary conditions, or combinations thereof. Analysing the enhancement in heat transfer has been accomplished through the isotherm and streamline contours, temperature isosurfaces, flow vectors, mean and root mean square velocity profiles, turbulence kinetic energy profiles and Nusselt number profiles. Firstly, laminar mixed convection in a lid-driven trapezoidal cavity using different nanoparticle types and various parameters other than configuration parameters has been investigated. It was found that any nanofluid types can provide greater heat transfer than water, especially, at high nanoparticle volume fraction and low diameter. Heat convection can be affected by changing either rotational and inclination angles, aspect ratio, or flow direction. Secondly, turbulent mixed convection due to the moving sidewalls of a lid-driven cuboid has been analysed. Remarkable enhancement in heat transfer has been achieved by either increasing the turbulent flow circulation or using nanofluids. Thirdly, turbulent mixed convection in a top wall lid-driven cuboid containing a clockwise- or anticlockwise-rotating cylinder has been studied. Significant enhancement in heat convection was noticed with the use of the rotating cylinder, especially when the direction of rotation can enhance the top wall movement. In addition, the Reynolds number and nanofluids have a positive impact on the heat transfer in the presence of the rotating cylinder. Finally, the study has been extended by artificially roughening the heated wall in order to increase the heat transfer rate. A noteworthy enhancement has been found due to the use of two rib shapes, particularly in combination with the rotating cylinder. Overall, in terms of the comparison between the URANS and LES predictions, even though both methods have performed well, the LES approach is more successful in capturing more detail of the secondary eddies.