Stratified tank models are used to simulate thermal storage in applications such as residential or commercial hot-water storage tanks, chilled-water storage tanks, and solar thermal systems. The energy efficiency of these applications relates to the system components and the level of stratification maintained during various flow events in the tank. One-dimensional (1D) models are used in building energy simulations because of the short computation time but often do not include flow-rate dependent mixing. The accuracy of 1D models for plug flow, plug flow with axial conduction, and two convection eddy-diffusivity models were compared with experimental data sets for discharging a 50-gal residential tank and recharging the tank with hot water from an external hot-water source. A minimum and maximum relationship for the eddy diffusivity factor were found at Re <2100 and >10,000 for recirculation of hot water to the top of the tank and vertical tubes inletting cold water at the bottom. The root mean square error decreased from >4 °C to near 2 °C when considering flow-based mixing models during heating, while the exponential decay of the eddy diffusion results in a root mean square error reduction of 1 °C for cone-shaped diffusers that begin to relaminarize flow at the inlet.
Windage is the effect of aerodynamic drag on the surfaces of a rotating system due to fluid shear effects. The fluid-friction losses that occur on the rotor of rotating machines often constitute a non-negligible drag on the system that must be estimated for proper sizing of the driving or driven element. This is especially true in high-pressure environments, such as hermetic compressors and turbines. Fluid-friction loss modeling is based on the size and rotation speed of the shaft, the density of the fluid, and an empirically-determined drag coefficient. The drag coefficient is generally a function of the Reynolds number but may also be dependent on the Taylor number. Several papers have provided empirical predictions for drag coefficients based on the Reynolds and Taylor numbers of the fluid, but other factors such as rotor shapes, assemblies, and surrounding fluid conditions can also affect the drag coefficient. There are two main geometries for a rotor: a face parallel to the axis of rotation, and a face that is perpendicular. The gap between the rotating component and the stationary housing also plays an important role in the drag coefficient. This review summarizes and compares these findings in a way that makes it easy for the reader to predict the total windage losses on a system for any rotor shape, speed, or operating condition. A quick reference table is presented in the conclusions section.
This paper demonstrates the modelling of a novel power cycle for coal-fired indirect supercritical carbon dioxide (sCO2). and thermal energy storage (TES). TES integrated with coal fired power plant can improve the efficiency, making the power generator responsive to ramp rate. The parallel models were developed in IDAES, an open-source code developed by the Department of Energy (DOE), and Aspen Plus V.10 by Aspen Technology, Inc., which is used in many industries to model and optimize a variety of chemical processes. The benefit of using Aspen Plus for coal combustion is its ability to model solids and multiphase fluids that will be present in the various coal and sCO2 power cycles. The property database and equations of state (EOS) available in the Aspen and IDAES software provide accurate results for the process conditions present in the cycles involved in this project. A baseline model was selected from DOE studies and compared against results from the system modelling. Current results from the model are close to those found using Aspen and those in the baseline model. The system model was also connected to a dual-media thermocline thermal energy storage system (DMTES) that uses a Python script to determine temperatures of molten salt provided by the DMTES. The code uses variables of heat flow, mass flow, and temperature to determine the performance of the sCO2 power block IDAES model. From this, the power block output is determined for steady state and quasi steady state conditions. The DMTES system is modeled for dynamic situations and is shown to have near-constant temperature during charge or discharge, depending on the position of the DMTES system thermal transition zone. The results show how temperature is maintained across a large range of state-of-charge.
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 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 models and 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 existing 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. This work involved comparing the accuracy and computational effort needed to implement these models. To assess accuracy, we compared both types of existing models to experimental data (also collected in this work) which included a heat up process using 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 implemented the models in MATLAB, the multi-paradigm numerical computing environment. We quantified model accuracy using the root mean squared error between modeled data and experimental data for six measured tank temperatures. Comparing the accuracy and the computational time taken to run the simulation provides a method to contrast the performance of each model and a way to rate it. The multi node model was run using from 6 to 96 spatial nodes; the plug flow model was run using 1 to 0.001 °C temperature bin sizes. Additionally, timesteps were varied from 4 to 236 s. The results quantify the tradeoff between accuracy and computational time, providing guidance for simulations to intelligently select the best model type and simulation parameters. This research can be used to validate the pre-existing models and possibly improve the modern water tank.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.