Transient calculation of charge and discharge cycles in thermally stratified energy storages

• Published in: Solar energy 2013-11, Vol.97, p.505-516
• Main Authors: Steinert, P., Göppert, S., Platzer, B.
• Format: Article
• Language: English
• Subjects: Applied sciences; Comparative analysis; Direct energy conversion and energy accumulation; Electrical engineering. Electrical power engineering; Electrical power engineering; Energy; Energy accumulation; Energy efficiency; Energy. Thermal use of fuels; Exact sciences and technology; Heat transport; Simulation; Solar energy; Storage tanks; Stratification; Temperature distribution; Thermal energy storage (TES); Thermal stratification; Transport and storage of energy; Turbulence; Turbulent jet flow
• ISSN: 0038-092X; 1471-1257
• DOI: 10.1016/j.solener.2013.08.039

•A model for charging stratified water storages was implemented in MATLAB.•For the analytical approach, a numerical solution procedure has been proposed.•Two simulation studies were conducted using different levels of model complexity.•The storage temperature distribution can be calculated with a mean error of 0.4K.•The MATLAB program required 1000 times less computation time in comparison with CFD. A stable thermal stratification in solarthermal storage tanks increases the energy efficiency of these systems. Especially in charging and discharging cycles, mixing occurs due to jet flows. The reliable prediction of the influence of the storage and of the charging device geometry on the loading behaviour is essential for the layout and improvement of stratified storage systems. A model approach for the computational calculation of the time-dependent temperature distribution in stratified storage tanks based on the one-dimensional heat transport equation is described in the present study. The numerical solution was obtained by application of the first order Upwind-discretization scheme. This basic approach was further refined by the consideration of charging jet flows and local turbulences in the area of stratification according to the strategies of Jirka (2004) and Mott and Woods (2009) and implemented in MATLAB. Two simulation examples of different complexity have shown that the enhanced model could increase the calculation accuracy in comparison to similar CFD and experimental studies. The results of the MATLAB program were reached with much less calculation effort than the results of the CFD simulation.