Residential water heaters contain water stratified by temperature-driven density differences. This implies that a water tank can reach a state in which the top and bottom sections have different temperatures, unless mixing happens. A high degree of thermal stratification can improve the efficiency of some heat pump water heaters, by saving the amount of energy required for the heat-up process. Studies of stratification became popular in the 1970s and it remains an active research topic today. The research has led to the development of different modeling techniques to better predict and define a stratified tanks behavior. By comparing these models and techniques used previously to describe thermal stratification, the phenomenon could be better understood, exploited, and used to increase efficiency and thermal energy capacity in modern water tanks. From the historical models, we found the one-dimensional standard plug-flow and a multi-node model to be appropriate for analyzing the processes of the heat up and cool-down in a water tank. These two models are based on energy balances. Our project involved comparing the accuracy and computational effort needed to implement these models. We decided to re-build the historical models and compare them to collected experimental data which included a heat up process that used an external heat pump. This external process included a layering process that has an eddy diffusivity at five times the rate of thermal diffusion. For this project, we re-created the models in MATLAB, the multi-paradigm numerical computing environment. To verify accuracy of the modeled temperatures, we averaged the Root Mean Square Error (RMSE) for the modeled data using an experimental model with the same starting conditions and relevant inputs for flow and temperature. Comparing these and the computational time taken to run the simulation gives us a way to contrast the performance of each model and a way to rate it. The models were run using several ranges of spatial-nodes for the multi-node, and different temperature bin threshold values for the plug flow inputs. Additionally, the data was run using different time step lengths for the same data. Comparing the values for the accuracy alongside those for computational time should direct us to an accurate, efficient, and easily implemented water tank model. This research can be used to validate the pre-existing models and possibly improve the modern water tank. Moving forward, the research will compare results with other models derived by 2D, 3D and potentially neural-network modeling.


Comparison of Plug Flow and Multi-Node Stratified Tank Modeling Approaches Regarding Computational Efficiency and Accuracy

Division / Sponsors: 
Aerospace, Bioengineering, Applied Mechanics, Design Engineering Division, Advanced Energy Systems, Manufacturing Engineering Division, Noise Control and Acoustics, Nondestructive Evaluation, Diagnosis, and Prognosis (NDPD), Nuclear Engineering Division, Process Industries Division, Fluids Engineering, Heat Transfer, Materials, Microelectromechanical Systems, Safety Engineering and Risk Analysis, Computers & Information in Engineering


Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2020/volume/84478

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