Temperature Drainage and Environmental Impact of Water Source Heat Pump Energy Station
1. Introduction
Based on the above literature, most of the research on the impact of water temperature in the Yangtze River was based on hydropower projects. At present, there has been little research on the impact of WSHP drainage temperature in the Yangtze River. With the development of more and more WSHP projects, rigorous scientific studies about the impact of water temperature are needed. Therefore, this study filled the gap in this field, and the applied research models and methods provide a reference for the study of similar problems. The research results show that ecological protection and economic and social development in the Yangtze River Basin can be synchronized. It also has significant application value. In this study, we developed a threedimensional model of the river channel of the River Water Source and Energy Station in the Wuhan Hankou Binjiang International Business District using the water quality and temperature data from the five years prior to project approval. We constructed a governing equation of temperature and drainage during the project using the Reynolds mean equation method and applied FLUENT software for computational fluid dynamics (CFD) simulations. This approach provides an efficient evaluation of the potential impact of warm water drainage on the surrounding aquatic environment and will be helpful when considering WSHP projects in similar environments.
It is important to note that different WSHP energy stations exhibit varying technical challenges, such as building type, water intake distance, and source water quality. Therefore, our method serves as a valuable reference for evaluating warm water drainage during WSHP projects in the middle and lower reaches of the Yangtze River and can be applied to other WSHP projects facing similar environmental challenges around the world.
2. Materials and Methods
2.1. Numerical Simulation Model and Solution Method
$$\rho \frac{\partial ({u}_{i}{u}_{j})}{\partial {x}_{j}}\frac{\partial}{\partial {x}_{j}}$$
$$\rho \frac{\partial ({u}_{j}T)}{\partial {x}_{j}}=\frac{\partial}{\partial {x}_{j}}$$
where ${x}_{i}(i=1,2,3)$ denotes the Cartesian coordinate and ${u}_{i}(i=1,2,3)$ is the velocity component along direction $i$. ${f}_{i}(i=1,2,3)$ is the mass force along direction $i$. $p$ is the pressure, T is the timeaveraged temperature, $\rho $ is the gas density, and ${\sigma}_{T}=0$
. To close the equations, a turbulence model should be introduced.
To select the optimal turbulence model for the flow problems involved in this project, the preliminary calculations of the simulations were carefully compared between the laminar model, Spalart–Allmaras model, and standard k–ε model by considering the flow problems involved in this project. In a practical flow situation, when the turbulence intensity is high, the convergence performances of laminar and SA models are poor. In contrast, the standard k–ε model exhibits the best characteristics of convergence during calculations. It can significantly shorten the convergence process and generate a more realistic flow field structure that can effectively reflect the flow field state.
$$\rho \frac{\partial (k{u}_{i})}{\partial {x}_{i}}=\frac{\partial}{\partial {x}_{j}}$$
$$\rho \frac{\partial (\epsilon {u}_{i})}{\partial {x}_{i}}=\frac{\partial}{\partial {x}_{j}}$$
$${\mu}_{t}={C}_{\mu}\rho \frac{{k}^{2}}{\epsilon}$$
where $k$ is the turbulent kinetic energy, $\epsilon $ is the dissipation rate, and ${\mu}_{t}$ is the turbulent viscosity. The constants involved in the model were ${C}_{\epsilon 1}=1.44$, ${C}_{\epsilon 2}=1.92$, ${\sigma}_{\epsilon}=1.3$, ${\sigma}_{k}=1.0$, and ${C}_{\mu}$ = 0.09.
The analysis revealed that the standard k–ε model yielded the best convergence properties in the calculation process, which can significantly shorten the convergence process, and the obtained flow field structure was more realistic and reflected the flow state well. Therefore, in this project, we adopted the standard k–ε model.
Due to the temperature difference between backwater and river water, the water density changes, resulting in the thermal buoyancy effect. To account for this effect, additional models were needed to describe the density change with temperature. There are currently three commonly used models, namely, the Boussinesq approximation (BA), polynomial linear density (PLD), and nonlinear density (NLD) models.
$$\rho ={\rho}_{0}\alpha {\rho}_{0}\Delta T$$
2.2. Energy Station’s Basic Water Temperature Conditions
2.3. ThreeDimensional Model Reconstruction of the River Channels
The geometry of the river channel directly impacts the flow of the river at this location. To simulate the river flow as accurately as possible, this research used modeling software like AutoCAD and Solidworks to accurately portray the riverbed in the river model. The riverbed profile of a river section generally converges between 50 m and 100 m. In order to take an accurate value, we extracted the outline of the riverbed within 2000 m of the river section at equal intervals of 50 m. This approach met the accuracy requirements of the river geometry model for these simulations. On the other hand, the research object of this article was the Wuhan section of the Yangtze River Basin, which is an irregular natural river channel. And the shape and size of its crosssections are different. It was not necessary to consider the issue of obtaining water surface profiles.
2.4. Calculation Domain and Grid Division
2.5. Boundary Conditions and Return Water Outlet
Based on the design documents, the inlet of river water was placed 115 m upstream of the recession inlet to account for boundary conditions. This location was determined after preliminary calculations to eliminate the inlet boundary’s significant influence. If the inlet boundary was set farther away from the drainage outlet, the calculated results were similar to those at 115 m upstream of the drainage outlet because the water inlet temperature was basically constant. If the inlet boundary was set too close to the outlet, the temperature influence range of the outlet might not be fully covered. To reflect the velocity distribution in the inlet section as accurately as possible, a mass flow inlet was established as the inlet boundary for the river. Additionally, the classical rigid cover assumption was used in the CFD simulations to better represent the flow situation at the river surface. This approach achieves a favorable balance between computational accuracy and efficiency and is widely used in hydraulic engineering to address river surface flow.
To simulate the temperature field more accurately, a nonstationary solver was used in this study to calculate and determine the river water velocity and temperature distributions over 7 days. However, it should be noted that the actual contact of the recession inlet with the river body varied significantly due to changes in the water level. During the summer months, the high water level reached approximately 25 m, while in winter, the water level dropped to only 15 m, and water was then drained via the drop bucket. The outlet should be rectangular with a width of 7.2 m, and the height can be chosen as 0.3 m based on parameters such as the outlet discharge and the gradient of the drop bucket.
3. Results and Discussion
In order to meet the energy usage demands, the drainage velocity of the WSHP energy station should reach up to 3.97 m^{3}/s. As previously mentioned, the average velocity of the Yangtze River is about 1.3 m^{3}/s. Because the drainage velocity of the WSHP energy station is much larger than the real velocity of the river, it can cause a change in the river’s temperature when they are mixed. However, the Yangtze River has a huge flow, with about 7144 million m^{3} per year. The upper and lower layers of the river can form a strong convection current, and thus, heat exchange continuously occurred in the Yangtze River. Moreover, it can not form the temperature stratification in the direction of the water depth. The temperature of the water in a small area did not change by more than 3%. Under the most unfavorable working conditions (8 °C temperature difference), the change in water temperature did not exceed 0.3 °C. Therefore, the change in the river’s temperature was very small, which might not affect the organisms in the Yangtze River basin.
Under working conditions S1, S2, and W1, the influence range remained within 700 m in the flow direction and 250 m in the width direction. This range was limited compared with the entire river body and met the relevant standards in China. Under working condition W2, although the influence range extended a long distance along the upstream direction, it mainly occurred in the coastal area, with minimal impact on the river’s main body. Additionally, it should be noted that during the winter dry season, backflow phenomena may occur on the shore, causing limited impacts on the water body upstream of the outlet in terms of scope and degree.
However, the construction of this project has not been completed yet. It is not possible to collect the actual water temperature of the drainage outlet for comparison with the simulation results. It is necessary to continue the research on this topic by collecting the drainage temperature data after the project is completed to validate the reliability and accuracy of the model. Furthermore, this study did not discuss the impact of warm drainage on aquatic ecology. It will systematically analyze the potential impact of drainage temperature on various aspects of aquatic ecosystems in future research.
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