Method for calculating sand peak transport time under real-time reservoir regulation

Abstract

The application relates to a reservoir real-time scheduling sand peak transport time calculation method, which comprises the following steps: a beta coefficient is obtained by fitting calculation according to the sand peak transport time of measured data and the transport time of reservoir water flow particles; the relationship between the water depths h1 and h2 of the reservoir tail and the dam front under different reservoir inflow or scheduling is deduced; the relationship between the water level and the reservoir capacity is deduced; the dam front water level, the reservoir inflow and outflow are substituted into the sand peak transport time calculation formula under the reservoir real-time scheduling, so that the time of sand peak transport from the reservoir tail to the dam front can be quickly calculated. The application closely combines the reservoir real-time scheduling, deduces the change of the flow velocity along the sand peak in the reservoir, finally obtains the sand peak transport formula suitable for the reservoir and having a certain mechanism, and combines the reservoir inflow and outflow conditions and the reservoir scheduling, so that the time of the sand peak transport from the reservoir tail to the dam front can be quickly and conveniently predicted and estimated, the dam gate is timely opened for sand discharge, the working efficiency and the result precision can be greatly improved, and the sand scheduling of the reservoir is effectively supported.

Description

Technical Field

[0001] This application relates to the fields of hydraulics and river dynamics, hydrological forecasting, and reservoir operation, and in particular to a method for calculating the peak transport time of sand under real-time reservoir operation. Background Technology

[0002] Accurate sediment forecasting is crucial for successful sediment management in reservoirs. Precisely determining the arrival time of sediment peaks in front of the dam, and promptly opening the gates to increase discharge and remove high-concentration sediment-laden water from the reservoir, is a current research focus. Since sediment peaks take several days to move from the reservoir tail to the dam (3-7 days for the Three Gorges Reservoir, depending on inflow and scheduling), and are subject to disturbances from different reservoir scheduling methods, the transport velocity of sediment peaks is a complex, nonlinear process, making accurate prediction of their arrival time in front of the dam difficult. Currently, the prediction of sediment peak arrival in front of the dam generally uses a one-dimensional hydro-sediment mathematical model to simulate sediment transport and predict the arrival time. This method requires topographic data and the construction of a complex numerical discrete model.

[0003] Meanwhile, Huang Renyong also derived the relationship V / Q using the reservoir flood retention time, as follows:

[0004]

[0005] The premise is that the research object is a natural river channel, where the flow velocity along the river is directly replaced by the average U or the arithmetic mean of the inflow and outflow, which is basically an empirical relationship. However, when the research section is a reservoir, due to the large difference between the water depth in front of the dam and the water depth at the tail of the reservoir (3 to 6 times), the flow velocity in the reservoir area also varies greatly. At the same time, the reservoir operation has a very significant impact on the transport of sediment peaks. Therefore, the application of this method in reservoirs is questionable. Summary of the Invention

[0006] The purpose of this application is to provide a method for calculating the time of sand peak transport under real-time reservoir scheduling, which can quickly and accurately provide the time information of sand peak transport to the front of the dam for reservoir scheduling, and provide important technical support for the optimized scheduling of reservoir sediment.

[0007] To achieve the above objectives, this application provides the following technical solution:

[0008] This application provides a method for calculating the peak transport time of sediment under real-time reservoir scheduling, including the following steps:

[0009] Step 1: Calculate the β coefficient by fitting the measured data of sand peak transport time and reservoir water flow particle transport time;

[0010] Step 2: Determine the relationship between the water depths h1 and h2 at the reservoir tail and in front of the dam under different inflow rates or scheduling;

[0011] Step 3: Determine the relationship between water level and reservoir capacity to express the linkage between different scheduling and reservoir capacity;

[0012] Step 4: Substitute the water level in front of the dam and the inflow and outflow into the formula for calculating the sand peak transport time under the real-time reservoir scheduling, and you can quickly calculate the time it takes for the sand peak to transport from the tail of the reservoir to the front of the dam.

[0013] The formula for calculating the peak transport time under real-time reservoir scheduling is as follows:

[0014]

[0015] In the formula, V is the reservoir capacity, Q1 is the inflow rate, and Q2 is the outflow rate.

[0016] The specific details of deriving the relationship between the water depths h1 and h2 at the reservoir tail and in front of the dam under different inflow rates or scheduling are as follows:

[0017] By plotting the relationship between the inflow rate and the tailwater depth h1, the tailwater depth h1 is obtained. Then, by plotting the relationship between the water level in front of the dam and the water depth h2 in front of the dam, the water depth h2 in front of the dam is obtained.

[0018] Compared with the prior art, the beneficial effects of the present invention are:

[0019] 1. This method has a more rigorous mathematical formula derivation process, and the model and method flow are more reliable and have a stronger mechanism than those that only use empirical formulas.

[0020] 2. This method takes into account the differences in flow velocity along the reservoir, unlike natural rivers where the average U or the arithmetic mean of inflow and outflow can be directly used as a substitute. It is more suitable for calculating the peak transport time of sand in reservoirs.

[0021] 3. This method requires minimal data, only the water level in front of the dam and the inflow and outflow rates. It is also fast, convenient, and highly accurate.

[0022] This application enables rapid and convenient prediction of the time it takes for the peak sediment load to reach the dam front, allowing for timely opening of the dam gates for sediment discharge. This significantly improves work efficiency and result accuracy, providing strong support for reservoir sediment management. Attached Figure Description

[0023] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1This is a schematic diagram of reservoir regulation and storage;

[0025] Figure 2 This is a flowchart of the calculation method of the present invention;

[0026] Figure 3 This is a graph showing the relationship between inflow rate and tailwater depth.

[0027] Figure 4 This is a diagram showing the relationship between the water level and the water depth in front of the dam.

[0028] Figure 5 It is a curve showing the relationship between water level and reservoir capacity;

[0029] Figure 6 This is a comparison chart of the calculated values ​​by the method of this invention and the calculated values ​​by the mathematical model;

[0030] Figure 7 This is a comparison chart of the calculated values ​​and the measured values ​​using the method of this invention. Detailed Implementation

[0031] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings. It should be noted that similar reference numerals and letters in the following drawings indicate similar items; therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0032] The terms “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0033] The terms “first,” “second,” etc., are used only to distinguish one entity or operation from another, and should not be construed as indicating or implying relative importance, nor as requiring or implying any such actual relationship or order between these entities or operations.

[0034] Please see Figure 1 As shown, this invention discloses a method for calculating the peak transport time of sand in real-time reservoir scheduling.

[0035] Generally, the transport time of sand peaks is directly proportional to the length of the river section and inversely proportional to the average velocity of the water flow within the river section. The formula for the transport time is:

[0036]

[0037] In the formula: T represents the transport time of water particles, L represents the length of the river section, A represents the average cross-sectional area of ​​the river section, U represents the average flow velocity of the cross section, Q represents the average flow rate of the river section, and V represents the water storage volume of the river section (corresponding to the flood detention capacity of the reservoir).

[0038] When the river section under study is a reservoir, the difference in water depth between the upstream and downstream areas is significant, resulting in substantial variations in flow velocity within the reservoir region. This differs from natural river channels where the average U can be directly used as a substitute. (See Appendix) Figure 1 For a reservoir, if we ignore the water level changes caused by different inflow and outflow rates, and assume that the flow velocity and water depth along the flow path change linearly, then the flow velocity at a certain cross-section of the reservoir can be expressed by the following linear equation:

[0039]

[0040] In the formula: u is the flow velocity at a certain cross section of the reservoir area, U1 and U2 are the flow velocities at the inlet and outlet cross sections of the reservoir, L is the length of the reservoir area, and x is the distance between the cross section and the tail of the reservoir.

[0041] By transposing and integrating the above equation, we get:

[0042]

[0043]

[0044] Meanwhile, the reservoir capacity is: V = 0.5 × (h1 + h2) × BL

[0045] Furthermore, we can obtain:

[0046]

[0047] Because reservoirs have "lower sediment concentration at the top and higher concentration at the bottom, and higher flow velocity at the top and lower flow velocity at the bottom," the peak sediment transport generally lags behind the transport time of water particles. By multiplying the formula by a coefficient β and making certain corrections, the formula for calculating the peak sediment transport time under real-time reservoir operation can be obtained as follows:

[0048]

[0049] V represents the reservoir capacity, Q1 represents the inflow rate, and Q2 represents the outflow rate.

[0050] The above formula shows that the time of water particle movement in the reservoir area is not only related to the flood storage capacity of the reservoir area, but also to the inflow and outflow of the reservoir, and the water depth at the reservoir tail and dam site.

[0051] In special cases where the equation contains singularities, the following expression can be used instead:

[0052] When U2 = U1, the flow velocity along the reservoir is consistent. Substituting the average flow velocity U of the river section, we get:

[0053]

[0054] When h2 = h1, the water depth in the reservoir is consistent along the entire length of the reservoir. Therefore:

[0055]

[0056] When Q2 = Q1, the inflow and outflow of the reservoir are the same, then:

[0057]

[0058] Please see Figure 2 This application provides a method for calculating the peak transport time of sediment under real-time reservoir scheduling, including the following steps:

[0059] Step 1: Calculate the β coefficient by fitting the measured data of sand peak transport time and reservoir water flow particle transport time;

[0060] Step 2: Determine the relationship between the water depths h1 and h2 at the reservoir tail and in front of the dam under different inflow rates or scheduling;

[0061] Step 3: Determine the relationship between water level and reservoir capacity to express the linkage between different scheduling and reservoir capacity;

[0062] Step 4: Substitute the water level in front of the dam and the inflow and outflow into the formula for calculating the sand peak transport time under the real-time reservoir scheduling, and you can quickly calculate the time it takes for the sand peak to transport from the tail of the reservoir to the front of the dam.

[0063] This application focuses on calculating the peak transport time of sediment in real-time scheduling of the Three Gorges Reservoir. The specific implementation method is as follows:

[0064] (1) Calculation of β coefficient

[0065] The sand peak propagation time T from Cuntan to the dam site of the Three Gorges Reservoir 沙 It is more important than the propagation time T of water particles. 水 To make it longer, based on the measured data, the β coefficient is fitted to be 1.33. Therefore, formula (6) can be expressed in the following form:

[0066]

[0067] (2) Calculation of reservoir tail water depth h1

[0068] During the flood season, the average water depth in the Cuntan-Changshou section of the Three Gorges Reservoir area is not significantly related to the water level in front of the dam, but mainly depends on the Cuntan discharge. A graph showing the relationship between the Cuntan discharge and the average water depth in the Cuntan-Changshou section is attached. Figure 3 By fitting the curve, the relationship between the tailwater depth of the reservoir under different inlet flow rates can be obtained:

[0069] h1 = 0.00043Q 寸+10.62 (11)

[0070] (3) Calculation of water depth h2 in front of the dam

[0071] A plot of the relationship between the average water depth and the water level in front of the dam in the Three Gorges Reservoir area during the flood season (from Miaohe to the dam site) is shown in the attached diagram. The two are basically linearly related. Figure 4 It can be expressed using the following relationship:

[0072] h2 = 0.57Z 坝前 +1.22 (12)

[0073] (4) Storage capacity curve

[0074] A diagram showing the relationship between the water level and capacity of the Three Gorges Reservoir is attached. Figure 5 After polynomial fitting, the following relationship can be expressed:

[0075]

[0076] (5) Calculation of sand peak transport time

[0077] Substituting formulas (11), (12), and (13) into formula (10), and replacing the inflow with the Cuntan flow rate, only the upstream water level Z needs to be known. 坝前 Inbound and outbound flow Q 入 and Q 出 This allows us to calculate the time it takes for sand peaks to travel from the tail of the reservoir to the front of the dam.

[0078] The calculated values ​​obtained using the method described in this application are compared with the results calculated using the mathematical model, as shown in the appendix. Figure 6 The differences between the two are minimal. A comparison with measured values ​​since the Three Gorges Dam began storing water is shown in the appendix. Figure 7 As shown in the figure, the calculated and measured values ​​mostly cluster near the diagonal, indicating a high degree of agreement. This demonstrates that the method of this invention, although relatively simple to use, still achieves high computational accuracy.

[0079] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for calculating sand peak transport time under real-time reservoir scheduling, characterized in that, Includes the following steps: Step 1: The sand hump transport time and the reservoir water flow particle transport time are fitted and calculated according to the measured data coefficient; Step 2: Calculate the water depth at the reservoir tail and in front of the dam under different inflow rates or scheduling conditions. and relation; Step 3: Determine the relationship between water level and reservoir capacity to express the linkage between different scheduling and reservoir capacity; Step 4: Substitute the water level in front of the dam and the inflow and outflow into the formula for calculating the sand peak transport time under the real-time reservoir scheduling, and you can quickly calculate the time it takes for the sand peak to transport from the tail of the reservoir to the front of the dam. The formula for calculating the peak transport time under real-time reservoir scheduling is as follows: ， In the formula, For the reservoir capacity, For inbound flow, This refers to the outbound flow rate.

2. The method for calculating the peak transport time of a reservoir under real-time scheduling as described in claim 1, characterized in that, The calculation of water depth at the reservoir tail and in front of the dam under different inflow rates or scheduling conditions. and The relationship is as follows: Using point plotting of inflow rate and tailwater depth The relationship diagram yields the water depth at the reservoir tail. Next, plot the water level and depth in front of the dam. The relationship diagram shows the water depth in front of the dam. .

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