Thermal storage, reduced order model, validation, optimization
Energy storage devices are key components to enable stable, more efficient, and controllable energy flow in systems. Furthermore, they are facilitators for maximizing the use of renewable primary energy sources, which often do not meet immediate supply-demand needs. Thermal batteries can be configured as tube and flat fin heat exchangers using water as working fluid inside the tubes and surrounded by a Phase-Change Material (PCM) on the external surface. These components typically require transient high-order modeling physics to simulate their behavior that are generally computationally intensive. This paper presents a low computational cost tool for design optimization of the heat exchangers for these batteries during discharge, i.e., solidification of the PCM. The approach consists of evaluating the heat transfer rate for an average PCM mass fraction of 50% and assuming a quasi-steady-state condition. The PCM thermal resistance is predicted using metamodels derived from validated CFD simulations. Using experimental data, the solver prediction matched the heat transfer rate during phase change from 2.3% to 22.9% for the same battery at different water inlet temperatures. The proposed solver is more than four orders of magnitude faster than the full transient model for a single design. This allows for using optimization such as Multi-Objective Genetic Algorithms (MOGA) to explore novel designs in only a few minutes. Finally, the optimization study suggested that for a particular battery, there is a trade-off where one may save more than 22% in material cost for the same performance or increase more than 6% in thermal performance for the same cost. Two distinct points in a Pareto front were selected and evaluated with the full transient model; the results provided good evidence that the proposed solver is sufficiently robust in predicting battery thermal performance and degradation.