# Demonstration of Phase Change Thermal Energy Storage in Zinc Oxide Microencapsulated Sodium Nitrate
## 4 Discussion
Thus, FEM simulations not only reproduce the experimental temperatures, but are useful to estimate the accuracy of other methods (e.g., DSC and adapted calorimetric method) by selecting, from multiple fitting (simulation) sessions, a specific set of values for the material thermal parameters. Multiple applications are designed to simulate thermal transfer ([PERSON] et al., 2013) [11]. While designing a standalone CFD application [17] capable of reproducing complex geometries, it was observed that a simplified cylinder model of the storage tank is sufficiently informative and fast to produce end-simulation results. The FEM simulations require as input the (a) tank geometry, (b) thermal properties of the storage material (from laboratory measurements), and (c) time course of temperature on the tank shell (monitored in tank charge-discharge experiments). The simulation output is a time course for temperatures and thermal energy distribution in the virtual tank, confirmed by thermocouple temperature monitoring in the center of the prototype storage tank. The FEM model allows to evaluate to which degree accuracy is needed for thermal property inputs (thermal conductivity and specific heat), and whereas polynomial fits are good descriptors for experimental data. The unusual choice of Microsoft Excel for the implementation of FEM simulations is based on the cylindrical symmetry and the simplicity of the storage tank, allowing for massive reduction in the mesh size. The validity of the simplification was tested by the convergence of the result, in each cell, for an interval of time steps ranging from 0.1 to 0.5 s. Although in terms of computing resources needed at a glance, Excel simulation approach is more demanding than an equivalent runtime simulation application, it has the advantage of facilitating development of features such as graphically augmented, transparent, and explicative input and output data ([PERSON] et al., 2009) [23]. The main question to which this study responded is if high temperature thermal energy may effectively be recovered from storage in microcapsules. In a simplistic evaluation of the tank-stored latent energy, the latent heat of microcapsules is multiplied by their mass, providing a target value (maximal) for the potential of the tank. In real thermal storage cycles, a PCM material is not uniformly exposed to energy transfer during tank charging; melting of all available PCM mass depends on factors such as thermal conductivity of the microcapsules, charging time, and temperature in the heat exchanger circuit. This is evident in the charge-self-discharge cycle 3 (Figure 6), when a plateau temperature (quasi-steady state) was not reached at charging time, therefore the high-temperature discharge phase was shorter. Ideally, when charging is complete and all NaNO\({}_{3}\) content is liquid, most of its latent heat is retrievable at temperatures that correspond to latent heat storage. Figure 8 presents the high temperature energy that can be retrieved from NaNO\({}_{3}\)-ZnO microcapsules, which corresponds to specific energy storage of 80.5 kJ/kg as the low estimate and 99.5 kJ/kg as the high estimate. These values are comparable with specific energies for other inorganic salt-based PCMs [24,25]. The corresponding energy density range (1.45 \(\times\) 105 to 1.79 \(\times\) 105 kJ/m\({}^{3}\)), considering 1800 kg/m\({}^{3}\) for the microcapsule material density (after melting-solidification cycles), is also as expected. Noticeable differences of the NaNO\({}_{3}\) tank-stored energy exist (Figure 8) when calculations are based on literature data versus this work's DSC measurements. This is explained by a drop of the NaNO\({}_{3}\) latent heat after the first melting-cooling cycle, probably due to partial nitrate decomposition ([PERSON] et. al 2013) [21].