Numerical modelling of thermal stratification scenarios in horizontal electric hot water storage tanks

Hot water storage tanks are important in residential and industrial heating systems, ensuring a reliable hot water supply. Despite their importance, there is a lack of studies on immersion of electrical heating through a helical coil in horizontally oriented hot water storage devices, especially considering combined heating and water draw. This study investigates thermal stratification and energy distribution under various operational scenarios using an experimentally validated computational fluid dynamics (CFD) model of a horizontally oriented electric hot water storage cylinder with a resistive immersion heating coil. The CFD model showed an overall mean relative error (MRE) of 4.6% and an overall root mean square error (RMSE) of 1.5 degrees C when compared to experimental data. The operational scenarios explored in Ansys Fluent are: (1) heating only, (2) heating with water draw, and (3) cooling with thermally adiabatic tank walls. The heating simulation shows clear steep thermal stratification with a 45 degrees C temperature difference between the warmer upper layer and the colder bottom layer after two hours of heating. The combination of heating with water draw highlighted the coil's impact on a discharging tank, showing a 4 degrees C increase during the onset, with 11.5% increase in stratification using the normalised stratification factor (ST*). The cooling simulation provided insights into the rate of thermal diffusion between the observed water layers. The initially stratified thermal state, with an absolute temperature difference of 30.5 degrees C, converged to a near-equilibrium state with an absolute difference of 5 degrees C within 45 h. The findings from this study and the validated CFD model will support future research on horizontal electric hot water storage tanks, facilitating the development, improvement, and validation of computationally efficient thermal models for flexible applications. Additionally, key parameters were identified, such as the characteristic temperature T-c and the characteristic time t(c), that offer practical guidance for optimising heating cycles, improving control strategies and modelling strategies, resulting in more energy-efficient systems for both domestic and industrial uses.