Abstract
In this numerical study, the phase change dynamics of a 3D cylinder containing hybrid nanofluid and phase change material (PCM) is investigated with a finite element solver. The PCM consists of spherical encapsulated paraffin wax, and the flow is under the forced convection regime. The dynamic features of the phase change process are studied for different values of the Reynolds number (between Re=100 and 300), the rotational Reynolds number of the inner disk (Rew=0 and 300), and the size of the rotating disk (length between 0.1L and 0.55L; height between 0.001H2 and 0.4H2). The flow dynamics and separated flow regions are found to be greatly influenced by the rotational speed and size of the inner disk. As Re is increased, the difference between the transition times at different rotational disk speeds decreases. At Re=100, a 21% reduction in the phase transition time is observed when the inner disk rotates at the highest speed as compared to the motionless case. Up to a 26% variation in the phase transition time occurs when the size of the inner rotating disk is varied. A 5 input-1 output feed-forward artificial neural network is applied to achieve fast and reliable predictions of the phase change dynamics. This study shows that introducing rotational effects can have a profound effect on the phase change dynamics of a hybrid nanofluid system containing phase change material.