Fast Pressure Fluctuation Prediction Method Based on RANS Model

Abstract

An embodiment of the present invention provides a fast pressure fluctuation prediction method based on the RANS model, which relates to the technical field of the operation and maintenance of stilling basins. The method includes: S1: Collect the operation condition data and design drawings of the stilling basin, and establish a three-dimensional model of the stilling basin structure; S2: Based on the operation condition data and the three-dimensional model, use the RANS model to carry out three-dimensional hydrodynamic numerical simulation to obtain the distribution data of pressure fluctuations in the stilling basin; S3: Based on the simulation results of the RANS model, calculate the key parameters required to predict the actual pressure fluctuations; S4: Based on the key parameters, calculate the root mean square value of the actual pressure fluctuations. This method can not only effectively reduce the cost and time of the prediction process, but also meet the requirements of accuracy and efficiency for pressure fluctuation analysis in engineering practice.

Description

Technical Field

[0001] The present invention relates to the technical field of the operation and maintenance of stilling basins, and more particularly, to a fast pressure fluctuation prediction method based on the RANS model. Background Art

[0002] In hydraulic engineering, a stilling basin is a common flood-discharging and energy-dissipating structure used to reduce the scouring of high-speed water flow on the downstream riverbed and structures. Accurately predicting the pressure fluctuations in the stilling basin is crucial for ensuring the safe operation of hydraulic structures and extending their service life. Traditional pressure fluctuation predictions mainly rely on numerical simulation methods (such as the large eddy simulation LES model), prototype observations, and model experiments. However, these methods are usually costly, require a large amount of computing resources and time, which limits their application in daily engineering practices.

[0003] In particular, although high-precision turbulent models such as detached eddy simulation (DES) and large eddy simulation (LES) can provide relatively accurate flow field details, their high demand for computing resources restricts their application in actual engineering projects. In addition, prototype observations and model experiments are not only costly but also often limited by experimental conditions and cannot fully reproduce actual working conditions, thus affecting the universality and accuracy of test results.

[0004] In view of this, developing a low-cost and high-efficiency prediction method has become an urgent need in the field of hydraulic engineering. Summary of the Invention

[0005] The object of the present invention includes providing a fast pressure fluctuation prediction method based on the RANS model, which can not only effectively reduce the cost and time of the prediction process but also meet the requirements of accuracy and efficiency for pressure fluctuation analysis in engineering practice.

[0006] The embodiments of the present invention can be implemented as follows:

[0007] The present invention provides a fast pressure fluctuation prediction method based on the RANS model, and the method includes:

[0008] S1: Collect the operation condition data and design drawings of the stilling basin, and establish a three-dimensional model of the stilling basin structure;

[0009] S2: Based on the operation condition data and the three-dimensional model, use the RANS model to conduct three-dimensional hydrodynamic numerical simulation to obtain the distribution data of pressure fluctuations in the stilling basin;

[0010] S3: Based on the simulation results of the RANS model, calculate the key parameters required to predict the actual pressure fluctuations;

[0011] S4: Based on the key parameters, calculate the root mean square value of the actual pressure fluctuations.

[0012] In an alternative embodiment, in S2, the distributed data includes three-dimensional distribution and data of the time-averaged pressure varying with time at any spatial point position.

[0013] In an alternative embodiment, S3 includes:

[0014] S31: Based on the simulation results of the RNAS model, calculate the boundary layer thickness δ distribution of the stilling basin floor, the local actual flow velocity u, the local friction velocity u τ , the turbulent kinetic energy k, and the hydraulic radius l; S32: Based on the key parameters calculated in S31, calculate the boundary layer Reynolds number Re δ , the relative wall distance z + , and the friction Reynolds number Re τ ;

[0015] S33: Based on the key parameters calculated in S32, calculate the coefficients α and β.

[0016] In an alternative embodiment, in S32, the calculation formulas for the three parameters are as follows:

[0017]

[0018]

[0019]

[0020] Where: μ is the friction coefficient between the stilling basin floor and the water body; υ is the kinematic viscosity of water at normal temperature; z is the actual height of this position from the floor.

[0021] In an alternative embodiment, in S33, the calculation formula for the coefficient α is as follows:

[0022]

[0023] In an alternative embodiment, in S33, the calculation formula for the coefficient β is as follows:

[0024]

[0025] In an alternative embodiment, S4 includes:

[0026] Based on the time-varying sequence data of the time-averaged pressure at any spatial position obtained from the RANS model simulation, calculate the variance of the time-averaged pressure at this position so as to calculate the root mean square value RMS(p) of the actual pressure fluctuation.

[0027] In an alternative embodiment, the formula for calculating the root mean square value RMS(p) of the actual pressure fluctuation is as follows:

[0028]

[0029] Where: ρ is the density of water at room temperature.

[0030] The beneficial effects of the fast pressure fluctuation prediction method based on the RANS model provided by the embodiments of the present invention include:

[0031] By adopting a numerical simulation method based on the Reynolds-averaged Navier-Stokes (RANS) model, this method can not only reduce the calculation cost, but also quickly and accurately predict the pressure fluctuation distribution near the bottom plate of flood discharge and energy dissipation structures such as stilling basins. Compared with the DES model and traditional experimental methods, the RANS model provides a more economical and practical solution, which helps to achieve fast and reliable pressure fluctuation analysis in daily engineering design and operation and maintenance. Description of the Drawings

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0033] Figure 1 It is a flowchart of the fast pressure fluctuation prediction method based on the RANS model provided by the embodiments of the present invention. Detailed Embodiments

[0034] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0035] Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents the selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0036] It should be noted that similar reference numerals and letters indicate similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0037] In the description of the present invention, it should be noted that if terms such as "upper", "lower", "inner", "outer", etc. are used to indicate the orientation or positional relationship, it is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the present invention is usually placed during use. This is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention.

[0038] In addition, if terms such as "first", "second", etc. are used only for distinguishing descriptions, they cannot be understood as indicating or implying relative importance.

[0039] It should be noted that, without conflict, the features in the embodiments of the present invention can be combined with each other.

[0040] By using a numerical simulation method based on the Reynolds Averaged Navier - Stokes (RANS) model to predict the pressure fluctuations in the stilling basin, not only can the calculation cost be reduced, but also the pressure fluctuation distribution near the bottom plate of flood - discharging and energy - dissipating structures such as the stilling basin can be predicted quickly and accurately. Compared with the DES model and traditional experimental methods, the RANS model provides a more economical and practical solution, which helps to achieve fast and reliable pressure fluctuation analysis in daily engineering design and operation and maintenance.

[0041] Therefore, please refer to Figure 1 , this embodiment provides a fast pressure fluctuation prediction method based on the RANS model (hereinafter referred to as: the method), and this method includes the following steps:

[0042] S1: Collect the operation condition data and design drawings of the stilling basin, and establish a three - dimensional model of the stilling basin structure.

[0043] Specifically, collect the operation condition data and design drawings of the stilling basin. Among them, the operation condition data mainly includes the upstream and downstream water levels of the stilling basin. Further, according to the design drawings, use software such as CATIA and Solidworks to establish a three - dimensional model of the stilling basin structure, and the overall and detailed dimensions of the constructed three - dimensional model are consistent with the actual engineering dimensions.

[0044] S2: Based on the operation condition data and the three - dimensional model, use the RANS model to carry out three - dimensional hydrodynamic numerical simulation to obtain the distribution data of the pressure fluctuations in the stilling basin.

[0045] Specifically, based on the operating condition data obtained from S1 and the established 3D model, a 3D hydrodynamic numerical simulation study in the stilling basin is carried out using the RANS model. This step of calculation can be completed using commercial software such as Fluent, Flow-3D, or open-source codes such as Openfoam. The RANS model adopts the Reynolds-averaged N-S equations, so the numerical simulation speed is relatively fast and the simulation can be completed in a relatively short time. Among them, the pressure fluctuation includes two parts: the time-averaged pressure and the pulsating pressure. The pressure fluctuation is generally measured by the root mean square value (RMS) of its time series. The result obtained by the RANS model simulation is the time-averaged flow field. Through this step, based on the numerical simulation results, the distribution data of the pressure fluctuation in the stilling basin can be obtained. The distribution data includes the 3D distribution and the data of the time-averaged pressure varying with time at any spatial point position.

[0046] S3: Based on the simulation results of the RANS model, calculate the key parameters required to predict the actual pressure fluctuation.

[0047] Specifically, based on the results obtained from the simulation calculation of the RANS model, some key parameters required to predict the actual pressure fluctuation can be calculated. The specific steps are as follows:

[0048] S31: Based on the simulation results of the RNAS model, calculate the distribution of the boundary layer thickness δ of the stilling basin bottom slab, the local actual flow velocity u, the local friction velocity u τ , the turbulent kinetic energy k, and the hydraulic radius l.

[0049] Among them, the distribution of the boundary layer thickness δ of the stilling basin bottom slab, the local actual flow velocity u, the local friction velocity u τ and the hydraulic radius l are all classical hydraulic parameters and can be directly calculated according to the simulation results of the RANS model. In addition, the distribution of the turbulent kinetic energy k can also be directly obtained from the simulation results of the RANS model.

[0050] S32: Based on the key parameters calculated in S31, calculate the boundary layer Reynolds number Re δ , the relative wall distance z + , and the friction Reynolds number Re τ . The calculation formulas for these three parameters are as follows:

[0051]

[0052]

[0053]

[0054] Where: μ is the friction coefficient between the stilling basin floor and the water body; υ is the kinematic viscosity of water at normal temperature, which can be obtained by looking up a table; z is the actual height of this position from the floor.

[0055] S33: Calculate coefficients α and β based on the key parameters calculated in S32.

[0056] Specifically, calculate coefficients α and β according to the above-obtained hydraulic parameters, and the calculation formulas are as follows:

[0057]

[0058]

[0059] S4: Calculate the root mean square value of the actual pressure fluctuation based on the key parameters.

[0060] Specifically, based on the time-averaged pressure at any position in space obtained by simulating based on the RANS model, the variance of the time-averaged pressure at this position can be calculated Thus, the root mean square value RMS(p) of the actual pressure fluctuation can be calculated using the following formula:

[0061]

[0062] Where: ρ is the density of water at normal temperature.

[0063] Thus, the purpose of calculating the root mean square value of the actual pressure fluctuation based on the simulation results of the RANS model is achieved.

[0064] The beneficial effects of the rapid pressure fluctuation prediction method based on the RANS model provided by the embodiments of the present invention include:

[0065] By adopting the numerical simulation method based on the Reynolds-averaged Navier-Stokes (RANS) model, this method can not only effectively reduce the cost and time of the prediction process, but also meet the requirements of accuracy and efficiency for pressure fluctuation analysis in engineering practice. Compared with the DES model and traditional experimental methods, the RANS model provides a more economical and practical solution, which helps to achieve rapid and reliable pressure fluctuation analysis in daily engineering design and operation and maintenance.

[0066] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A rapid pressure fluctuation prediction method based on RANS model, characterized in that: The method comprises: S1: Collect the operating data and design drawings of the stilling pool and establish a three-dimensional model of the stilling pool structure; S2: Based on the operating condition data and the three-dimensional model, a three-dimensional hydrodynamic numerical simulation is carried out using the RANS model to obtain the distribution data of pressure fluctuations in the stilling basin; S3: Based on the simulation results of the RANS model, calculate the key parameters required to predict the actual pressure fluctuations, including: S31: Based on the simulation results of the RNAS model, calculate the boundary layer thickness δ distribution of the stilling pool bottom plate, the local actual flow velocity u, and the local friction flow velocity , turbulent energy k and hydraulic radius l; S32: Calculate the boundary layer Reynolds number based on the key parameters calculated in S31 , relative wall distance , and the friction Reynolds number ; S33: Calculate coefficients α and β based on the key parameters calculated in S32; S4: Based on key parameters, calculate the root mean square value of actual pressure fluctuations, including: the time-averaged pressure at any position in space obtained based on RANS model simulation The variance of the time-averaged pressure at the location is calculated using the time-varying series data , thereby calculating the root mean square value RMS (p) of the actual pressure fluctuation.

2. The rapid pressure fluctuation prediction method based on the RANS model according to claim 1 is characterized in that: In S2, the distribution data includes three-dimensional distribution and data of time-averaged pressure at any spatial point changing with time.

3. The rapid pressure fluctuation prediction method based on the RANS model according to claim 1 is characterized in that: In S32, the calculation formulas of the three parameters are as follows: Where: μ is the friction coefficient between the stilling pool bottom plate and the water body; is the kinematic viscosity of water at room temperature; z is the actual height of this position from the bottom plate.

4. The rapid pressure fluctuation prediction method based on the RANS model according to claim 3 is characterized in that: In S33, the calculation formula of coefficient α is as follows: 。 5. The rapid pressure fluctuation prediction method based on the RANS model according to claim 3 is characterized in that: In S33, the coefficient β is calculated as follows: 。 6. The rapid pressure fluctuation prediction method based on the RANS model according to claim 1 is characterized in that: The formula for calculating the root mean square value RMS (p) of the actual pressure fluctuation is as follows: Where: ρ is the density of water at room temperature.

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