Impacts of magnetic field and hybrid nanoparticles in the heat transfer fluid on the thermal performance of phase change material installed energy storage system and predictive modeling with artificial neural networks - Selimefendigil, Fatih; Öztop, Hakan Fehmi (Elsevier, 2020)
In this study, impacts of magnetic field and hybrid nanoparticle addition in the heat transfer fluid are numerically studied for a thermo-fluid system with embedded phase change material during charging mode of operation by using finite element method. The magnetic field is uniform in radial direction while different strengths are considered in various domains of the computational model. Parametric numerical investigation is performed for various values of Hartmann numbers (Ha(1) and Ha(2) between 0 and 40), solid nanoparticle volume fraction of hybrid particles (between 0 and 0.02) and porosity of the medium (between 0.40 and 0.60). It was observed that the charging time is reduced with the imposed magnetic field while fast phase transitions occur near the walls. Dynamic characteristic of liquid fraction curves are changed with the magnetic field effects, nanoparticle inclusion and porosity of the medium. A reduction up to 40% is achieved in the charging time when the magnetic field strength of the phase change material domain is increased to Ha=40. The improved thermal transport features with hybrid nanoparticle inclusion in the heat transfer fluid is obtained. As water and nanofluid at the highest solid volume fractions are compared, 18.5%, 17.8% and 15% of reduction in the charging time is attained when cases with various magnetic field strength combinations at (Ha(1), Ha(2))=(40,40), (0, 40) and (40,0) are considered. Finally, estimation of charging time for different magnetic field strengths of domains and solid volume fractions of the hybrid particles is performed by using the artificial neural network based modeling approach which delivers fast and accurate results.
Keywords: Energy storage; Phase change material; Hybrid nanofluid; Neural networks; Magnetic field; Finite element method