Magnetohydrodynamic free convection of nano-encapsulated phase change materials between two square cylinders: Mapping the thermal behavior using neural networks

The study focused on investigating the convective heat transfer of nano-encapsulated phase change suspensions in the presence of a non-uniform magnetic field within an annuli space between two square cylinders. The principal equations for the fluid flow and phase change heat transfer were formulated as partial differential equations and then represented into dimensionless format. The finite element method was used to solve these equations and simulate the free convection heat transfer. The effect of various factors, including Hartmann, Rayleigh, Eckert and Stefan numbers, geometry aspect ratio, nanoparticles’ concentration, and fusion temperature, on the heat transfer rate was examined. A neural network was also introduced and trained to establish the connection between the control parameters (inputs) and the heat transfer rate (output). The outcomes were presented in the form of the modified Nusselt number, along with isotherms, heat capacity ratio (phase change) contours, and streamlines. The results demonstrated that the neural network could accurately predict the heat transfer rate and provide a comprehensive map of heat transfer with respect to the control parameters. Nano-Encapsulated Phase Change Materials (NEPCMs) can be considered as a new type of nanofluids, in which the nanoparticle consists of a core and a shell. The core part is made of a Phase Change Material (PCM) which can undergo solid-liquid phase change at a certain fusion temperature, and absorb/release a significant amount of energy due to latent heat of the phase change

Mixed convection of nano-encapsulated phase change suspensions in a wavy wall lid-driven trapezoid cavity

The improvement of heat transfer and energy storage are crucial tasks in many renewable energy applications. The thermal and hydrodynamic performances of a nano-encapsulated phase change material (NEPCM) suspension are investigated under a mixed convective heat transfer regime inside a trapezoidal enclosure. As the host fluid and dispersed NEPCM particles circulate in the enclosure, the nanoparticle cores absorb/release heat and undergo a phase transition process, enhancing heat transfer. The governing equations were scaled into a general non-dimensional format and then solved by the finite element method. The influence of nanoparticles fusion temperature and concentration, as well as the wavy wall characteristics and Richardson number, was addressed on heat transfer. A suitable fusion temperature of the nanoparticles can boost the heat transfer rate by 8%. Furthermore, by employing 5% NEPCM particles at a dimensionless fusion temperature of 0.1, the Nusselt number achieved was 9.05. This marks a significant 37% rise when contrasted with a base fluid, which only had a Nusselt number of 5.7

Improving phase change heat transfer in an enclosure filled by uniform and heterogenous metal foam layers: A neural network design approach

Phase change materials (PCMs) inherently store and release large amounts of energy during phase transitions. In this research, the potential of two metal foam (MF) layers in enhancing the thermal energy storage unit's heat transfer was probed, with one layer having distinct attributes at an anisotropic angle, ω. Utilizing the finite element method to understand the system dynamics, model accuracy was affirmed through rigorous checks. The impact of the heterogeneous parameter (0 < Kn < 0.3), heterogeneous angle (0 < ω < 90°), and porosity 0.9 < ε < 0.975 was addressed on the melting process. To circumvent the high simulation costs, an artificial neural network (ANN) was trained on 7838 data points. Noteworthy findings indicate that a slight 7.5 % increase in porosity can reduce the melting time by 66 %. Moreover, the 0° anisotropic angle emerged as the most efficient in heat transfer due to its superior thermal properties. The incorporation of ANN analytics was a pivotal shift, bypassing the traditionally high computational demands of phase change heat transfer studies. Once fully trained, the ANN adeptly demonstrated melting volume fraction (MVF) nuances under varied conditions. Further, optimal melting efficiencies were pinpointed at the ω = 0° angle, with a specific porosity zone, ε ∼ 0.925, showing minimal MVF and the benefits of a higher porosity (ε = 0.94) becoming evident at t = 3000 s. Ultimately, this investigation harmoniously integrates traditional analytical tools with ANN technology, offering profound insights into PCM heat transfer dynamics and laying the groundwork for future energy-efficient thermal storage solutions