Simulation of cold storage process via Galerkin approach implementing nanoparticles

The aim of this research is to simulate the unsteady cold storage process in a tank with wavy walls and fins, designed to improve the solidification of the working fluid. The loading of alumina nanoparticles within water significantly accelerates the freezing process, improving the system's overall efficiency. This paper focuses on analyzing the effects of two critical factors: the fraction (ϕ) and the diameter (dp) of the additives. The simulations, performed using the Galerkin method, include a dynamically adapted mesh to accurately track the solidification front. Results show that initially increasing the nanoparticle diameter (dp) enhances the freezing rate by around 20.77%. However, beyond a certain size, further augments in dp lead to a reduction in freezing rate by about 50.33%. Thus, the optimal nanoparticle size for this system is identified as 40 nm. Moreover, increasing ϕ expedite rates the freezing process, reducing the total freezing time by approximately 41.13%.