A dynamic method to optimize cascaded latent heat storage systems with a genetic algorithm: A case study of cylindrical concentric heat exchanger

The flow of heat to and from Phase Change Materials (PCMs) is governed by the temperature differences. One of the most viable passive strategy to enhance temperature differences is by using cascaded PCMs. Based on a dynamic heat transfer model coupled with a genetic algorithm, a method for optimizing the performance of cascaded latent heat storage systems is proposed. In this method, the optimization variables and thermal performance based on different objective functions and boundary conditions are investigated. The results show that the mass of the PCM and the number of transfer units (NTU) in each cascaded stage should not always be the same under different objective functions and boundary conditions, unlike in the literature. Additionally, the objective function based on charged exergy is better than that based on charged energy or entransy. Increasing the charging time would increase the charged energy, exergy and entransy, but it will result in a decrease in the efficiencies. As the heat transfer fluid (HTF) has a flow rate greater than 0.2 kg/s, the energy, exergy and entransy efficiencies drop sharply, but have no significant influence on the charged energy, exergy and entransy. For a steady state HTF, an increase of inlet temperature, causes the charged energy, exergy and entransy to increase linearly. However, in this case the rate of temperature increase of the PCMs increase as expected, but the efficiencies decrease slightly. For an unsteady HTF, as the fluctuation in the temperature increases, the charged energy, exergy, entransy along with the efficiencies decrease linearly. In addition, the latent heat capacity of PCMs in different stages will have a significant influence on the optimization variables and thermal performance. In the model in this study, the recommended charging time and HTF flow rate are 3000 s and 0.2 kg/s, respectively.