A method for calculating control parameters of a local time-averaged model based on the energy spectrum distribution of rotating turbulence and its application

Abstract

The present invention discloses a method for calculating control parameters of a local time-averaged model based on the distribution of rotating turbulent energy spectrum and its application method. The control parameter calculation method comprises the following steps: 1. The range of the turbulent energy spectrum that can be modeled or directly solved by the turbulence model coupling NS equation is: the wave number κ that can be solved by the RANS method l The wave number κ that can be solved by the DNS method η ; Step 2: Introduce Zeman number κ Ω , the solvable κ l ~κ η The energy spectrum between the two parts is divided into two parts; Step 3, according to the definition of turbulent kinetic energy and the energy spectrum distribution of rotating turbulence, combined with the position of the grid scale Δ in the energy spectrum, the turbulent kinetic energy k modeled in the PANS model is calculated integrally. u and the total turbulent kinetic energy k t ; Step 4: According to the control parameter f in the PANS model k The definition of turbulent kinetic energy k u and the total turbulent kinetic energy k t The expression of the corresponding control parameter is calculated. This calculation method takes into account the balance between the amount of calculation and the calculation accuracy in numerical simulation, and provides a powerful tool for efficiently solving the rotating flow field inside the hydraulic machinery.

Description

Technical Field

[0001] The present invention belongs to the field of turbulence numerical simulation, and relates to a calculation method, and in particular to a local time-averaged model control parameter calculation method based on rotating turbulence energy spectrum distribution and an application method thereof. Background Art

[0002] In the field of numerical simulation of turbulence, achieving a balance between computational effort and computational accuracy has always been a research hotspot. In engineering applications of complex rotating turbulence such as hydraulic machinery, efficient numerical calculation is particularly important. However, affected by factors such as strong rotation, large curvature and multiple walls, the internal rotating turbulence of hydraulic machinery such as water pumps exhibits characteristics such as rotation, transientity and pulsation. In the numerical simulation of this type of turbulence, the Reynolds time-averaged method (RANS) cannot accurately capture the flow characteristics such as the reverse transfer of rotating turbulent energy. Although methods such as direct numerical simulation (DNS) and large eddy simulation (LES) have higher computational accuracy, they have great difficulties in simulating hydraulic machinery with high Reynolds numbers and multiple walls.

[0003] In recent years, Girimaji et al. proposed a local time averaging (PANS) method, which can be used to control the parameter f k The change of control parameter f can achieve efficient solution of various flows. k It is defined as the ratio of the modeled turbulent kinetic energy to the total turbulent kinetic energy. The turbulent energy spectrum can well describe the distribution of turbulent kinetic energy at different scales. Therefore, f is calculated based on the turbulent energy spectrum. k The size and distribution of the wave number κ is the most reasonable way. At present, different scholars have derived PANS calculation methods based on the energy spectrum distribution of non-rotating turbulence, which are respectively denoted as PANS-ES1 and PANS-ES2. Studies have shown that reverse energy transfer occurs in rotating flow, so the energy spectrum distribution of rotating flow is different from that of non-rotating flow. Ω =(Ω 3 / ε) 1 / 2 Determines the turbulent length scale distribution and affects the energy spectrum transmission and energy spectrum form in the rotating flow, where κ Ω is the Zeman number, and Ω represents the rotation speed. There is no calculation method for rotating turbulence in the existing PANS method, but the difference in the energy spectrum distribution of rotating turbulence provides a new idea for solving rotating turbulence. Summary of the invention

[0004] In view of the above-mentioned defects and application requirements, the present invention proposes a method for calculating the dynamic control parameters of the PANS model based on the energy spectrum distribution of rotating turbulence, aiming to solve the problem that the existing PANS model cannot accurately reflect the flow characteristics of rotating turbulent flow, and provide a powerful tool for efficiently analyzing rotating turbulence in hydraulic machinery.

[0005] To achieve the above object, the present invention provides a method for calculating control parameters of a local time-averaged model based on the energy spectrum distribution of rotating turbulence, which has the following characteristics: comprising the following steps:

[0006] Step 1: Assuming that the numerical simulation method is used to solve the turbulence process, the range of the turbulence energy spectrum that can be modeled or directly solved by the turbulence model coupling NS (Navier-Stokes) equation is: the wave number κ that can be solved by the RANS method (Reynolds time-averaged method) l The wave number κ that can be solved by DNS method (direct numerical simulation) η ;

[0007] Step 2: Introduce Zeman number κ Ω , the solvable κ l ~κ η The energy spectrum between the two parts is divided into two parts, and the energy spectrum distribution of rotating turbulence is obtained;

[0008] Step 3: Based on the definition of turbulent kinetic energy and the energy spectrum distribution of rotating turbulence, combined with the position of the grid scale Δ in the energy spectrum, the turbulent kinetic energy k modeled in the PANS model is calculated integrally. u and the total turbulent kinetic energy k t ;

[0009] Step 4: According to the control parameter f in the PANS model k The definition of turbulent kinetic energy k u and the total turbulent kinetic energy k t The expression of , calculate the corresponding control parameter f k expression.

[0010] Furthermore, the present invention provides a method for calculating control parameters of a local time-averaged model based on the distribution of a rotating turbulent energy spectrum, which may also have the following characteristics: wherein, in step 1, in the turbulent energy spectrum, the wave number κ that can be solved by the RANS method is l The expression is:

[0011]

[0012] In the formula, l turb is the turbulence length scale that can be solved by the RANS method;

[0013]

[0014] In the formula, k T is the total turbulent kinetic energy in the RANS method, k T =k R +k U , k U is the modeled turbulent kinetic energy, k R is the turbulent kinetic energy solved directly, U i is the instantaneous speed, is the time-averaged velocity; ε is the turbulent kinetic energy dissipation rate;

[0015] In the turbulent energy spectrum, the wave number κ that can be solved by the DNS method is η The expression is:

[0016]

[0017] Where η is the length scale that can be solved by the DNS method, called the Kolmogorov length scale, and its expression is:

[0018]

[0019] Where ν is the dynamic viscosity of the fluid.

[0020] Furthermore, the present invention provides a method for calculating control parameters of a local time-averaged model based on the energy spectrum distribution of rotating turbulence, which may also have the following characteristics: wherein, in step 2, in the numerical simulation calculation, the calculation method of the grid scale Δ is:

[0021] Δ=(ΔxΔyΔz) 1 / 3 ;

[0022] Where Δx, Δy and Δz are the dimensions of the grid unit in the x, y and z directions respectively;

[0023] Wave number κ corresponding to the grid scale Δ for:

[0024]

[0025] Zeman number κ in rotating turbulence Ω The expression is:

[0026] κ Ω =(Ω 3 / ε) 1 / 2 ;

[0027] Where Ω is the rotation speed and ε is the turbulent kinetic energy dissipation rate;

[0028] The energy spectrum distribution of rotating turbulence is:

[0029] When Δ >κ Ω When the energy spectrum E(κ) and the wave number κ satisfy the relationship: E(κ)=αε 2 / 3 κ -5 / 3 , α is the first empirical coefficient of the energy spectrum;

[0030] When Δ <κ ΩWhen, the energy spectrum E(κ) and wave number κ satisfy the relationship: E(κ)=β(Ωε) 1 / 2 κ -2 , β is the second empirical coefficient of the energy spectrum.

[0031] Furthermore, the present invention provides a method for calculating control parameters of a local time-averaged model based on the distribution of rotating turbulent energy spectrum, which may also have the following characteristics: wherein, in step 3, it is assumed that in the PANS calculation, the grid scale is between the turbulence length scale and the Kolmogorov length scale;

[0032] The total turbulent kinetic energy k t Calculated by the following formula:

[0033]

[0034] Furthermore, the present invention provides a method for calculating control parameters of a local time-averaged model based on the energy spectrum distribution of rotating turbulence, which may also have the following characteristics: wherein, for a wave number of κ Δ ~κ η , the turbulent kinetic energy is solved by modeling;

[0035] When Δ <κ Ω When the modeled turbulent kinetic energy k u for:

[0036]

[0037] When Δ >κ Ω When the modeled turbulent kinetic energy k u for:

[0038]

[0039] Furthermore, the present invention provides a method for calculating control parameters of a local time-averaged model based on the energy spectrum distribution of rotating turbulence, which may also have the following characteristics: wherein, in step 4, the control parameter f k The definition of is: k =k u / k t .

[0040] Furthermore, the present invention provides a method for calculating control parameters of a local time-averaged model based on the energy spectrum distribution of rotating turbulence, which may also have the following characteristics: wherein the first empirical coefficient α of the energy spectrum and the second empirical coefficient β of the energy spectrum are both 1.8;

[0041] Control parameter f k The expression is:

[0042]

[0043] When Δ =κ Ω , the two expressions in the above segmentation can be unified.

[0044] The present invention also provides a computer-readable storage medium storing a computer program, which has the following characteristics: the computer program enables a computer to execute the method for calculating control parameters of a local time-averaged model based on the rotating turbulence energy spectrum distribution.

[0045] The present invention also provides an electronic device having the following characteristics: it includes a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, it implements the method for calculating control parameters of the local time-averaged model based on the rotating turbulence energy spectrum distribution.

[0046] The present invention also provides an application method of the local time-averaged model control parameter calculation method based on the rotating turbulence energy spectrum distribution in the numerical calculation of rotating turbulence, which has the following characteristics: when calculating the rotating turbulence application, firstly, the classic SST k-ω model is used as the base RANS model to derive its corresponding PANS model; then, according to the local time-averaged model control parameter calculation method based on the rotating turbulence energy spectrum distribution, the control parameter f derived based on the rotating turbulence energy spectrum is calculated. k The expression is obtained and brought into the PANS model expression of the SST k-ω model; then, the modified PANS model is coupled with the NS equations to form a nonlinear equation group, which can be used for the numerical model calculation of rotating turbulence after being discretized by the finite volume method.

[0047] The present invention also provides a computer-readable storage medium storing a computer program having the following characteristics: the computer program enables a computer to execute the application method of the local time-averaged model control parameter calculation method based on the rotating turbulence energy spectrum distribution in the numerical calculation of rotating turbulence.

[0048] The present invention also provides an electronic device having the following characteristics: it includes a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, it implements the method for applying the method for calculating control parameters of the local time-averaged model based on the energy spectrum distribution of rotating turbulence in the numerical calculation of rotating turbulence.

[0049] The beneficial effects of the present invention are as follows: the present invention provides a method for calculating control parameters of a local time-averaged model based on the energy spectrum distribution of rotating turbulence and its application method. According to the definition of turbulent kinetic energy, the definition of control parameters and the energy spectrum characteristics of rotating turbulence, a numerical solution method suitable for calculating rotating turbulence is obtained. The theoretical basis of this method is more complete and is more suitable for solving phenomena such as changes in turbulence intensity and reverse transfer of energy induced by the rotation effect. This method has a better ability to capture the rotation effect of rotating turbulence, and at the same time can achieve a balance between the amount of calculation and the accuracy of calculation when calculating rotating turbulence, providing a powerful tool for the efficient calculation of rotating turbulence in hydraulic machinery such as centrifugal pumps. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of the method for calculating the control parameters of the local time-averaged model based on the energy spectrum distribution of rotating turbulence;

[0051] Figure 2 is the energy spectrum distribution diagram of rotating turbulence;

[0052] Figure 3 It is a flowchart of the calculation method of the control parameters of the local time-averaged model based on the energy spectrum distribution of rotating turbulence;

[0053] Figure 4 This is a schematic diagram of the computational domain of the rotating channel flow example;

[0054] Figure 5 This is a schematic diagram of the calculation domain of the centrifugal pump example;

[0055] Figure 6 This is a comparison diagram of the average velocity of the flow direction near the suction surface of the rotating channel flow example;

[0056] Figure 7 It is a comparison chart of radial velocity at the monitoring position of the centrifugal pump example. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0058] like Figure 1 As shown, the present embodiment provides a method for calculating control parameters of a local time-averaged model based on a rotating turbulence energy spectrum distribution, comprising the following steps:

[0059] Step 1: Assuming that the numerical simulation method is used to solve the turbulence process, the range of the turbulence energy spectrum that can be modeled or directly solved by the turbulence model coupling NS (Navier-Stokes) equation is: the wave number κ that can be solved by the RANS method (Reynolds time-averaged method) l The wave number κ that can be solved by DNS method (direct numerical simulation) η .

[0060] Specifically, in the turbulent energy spectrum, the wave number κ that can be solved by the RANS method is l The expression is:

[0061]

[0062] In the formula, l turb is the turbulence length scale that can be solved by the RANS method;

[0063]

[0064] In the formula, k T is the total turbulent kinetic energy in the RANS method, k T =k R +k U , k U is the modeled turbulent kinetic energy, k R is the turbulent kinetic energy solved directly, U i is the instantaneous speed, is the time-averaged velocity; ε is the turbulent kinetic energy dissipation rate.

[0065] In the turbulent energy spectrum, the wave number κ that can be solved by the DNS method is η The expression is:

[0066]

[0067] Where η is the length scale that can be solved by the DNS method, called the Kolmogorov length scale, and its expression is:

[0068]

[0069] Where ν is the dynamic viscosity of the fluid.

[0070] Step 2: Introduce Zeman number κ Ω , the solvable κ l ~κ η The energy spectrum between the two parts is divided into two parts, and the energy spectrum distribution of rotating turbulence is obtained.

[0071] Specifically, in numerical simulation calculations, the calculation method of the grid scale Δ is:

[0072] Δ=(ΔxΔyΔz)1 / 3 ;

[0073] Where Δx, Δy, and Δz are the dimensions of the grid unit in the x, y, and z directions, respectively.

[0074] Wave number κ corresponding to the grid scale Δ for:

[0075]

[0076] Zeman number κ in rotating turbulence Ω The expression is:

[0077] κ Ω =(Ω 3 / ε) 1 / 2 ;

[0078] Where Ω is the rotation speed and ε is the turbulent kinetic energy dissipation rate.

[0079] The energy spectrum distribution of rotating turbulence is:

[0080] When Δ >κ Ω When the energy spectrum E(κ) and the wave number κ satisfy the relationship: E(κ)=αε 2 / 3 κ -5 / 3 , α is the first empirical coefficient of the energy spectrum;

[0081] When Δ <κ Ω When, the energy spectrum E(κ) and wave number κ satisfy the relationship: E(κ)=β(Ωε) 1 / 2 κ -2 , β is the second empirical coefficient of the energy spectrum.

[0082] Step 3: Based on the definition of turbulent kinetic energy and the energy spectrum distribution of rotating turbulence, combined with the position of the grid scale Δ in the energy spectrum, the turbulent kinetic energy k modeled in the PANS model is calculated integrally. u and the total turbulent kinetic energy k t .

[0083] Specifically, it is assumed that in the PANS calculation, the grid size is between the turbulence length scale and the Kolmogorov length scale.

[0084] The total turbulent kinetic energy k t Calculated by the following formula:

[0085]

[0086] like Figure 2 As shown, for smaller turbulence scales (i.e., larger wave numbers, the wave number is κ Δ ~κ η ), the turbulent kinetic energy is solved by modeling;

[0087] When Δ <κ Ω When the modeled turbulent kinetic energy k u for:

[0088]

[0089] When Δ >κ Ω When the modeled turbulent kinetic energy k u for:

[0090]

[0091] Step 4: According to the control parameter f in the PANS model k The definition of turbulent kinetic energy k u and the total turbulent kinetic energy k t , calculate the corresponding control parameter expression.

[0092] Control parameter f k The definition of is: k =k u / k t .

[0093] Specifically, the first empirical coefficient α of the energy spectrum and the second empirical coefficient β of the energy spectrum are both 1.8;

[0094] Control parameter f k The expression is:

[0095]

[0096] It is worth noting that when κ Δ =κ Ω , the two expressions in the above segmentation can be unified.

[0097] like Figure 3 As shown, this embodiment also provides an implementation of a method for calculating control parameters of a local time-averaged model based on the energy spectrum distribution of rotating turbulence, that is, an application method thereof in the numerical calculation of rotating turbulence:

[0098] Modification of the transport equation: First, according to the PANS theory, the corresponding PANS model is derived based on the SST k-ω model, and the f containing the rotation speed Ω is replaced by k The expression is introduced into the SST k-ωPANS transport equation. Among them, f k In the expression, ε=β1kω, β1 is the model coefficient, and ω is the specific dissipation rate of turbulent kinetic energy.

[0099] Application of the new method: The transport equations of the modified SST k-ωPANS model are coupled to the closed control equations of the NS equations, denoted as PANS-RCES. The equations are discretized by the finite volume method and used for numerical model calculations of rotating turbulence.

[0100] In a specific embodiment, Figure 4 and 5 The following are the schematic diagrams of the computational domain of the rotating channel flow example and the centrifugal pump example. Both examples are classic cases with rotating turbulence and can be used to verify the accuracy of the turbulence model calculation. In the rotating channel flow example, the Reynolds number Re = U m h / ν=7000, rotation number Ro=2ωh / U m =0.3, where h is half of the channel height, ν is the dynamic viscosity of the fluid, ω is the rotation speed of the channel flow, is the volume average velocity along the flow direction, the positive direction of the x-axis is the flow direction, and the entire flow is driven by the flow pressure gradient. In the centrifugal pump case, the impeller speed is 725r / min, the inlet diameter D1 = 71mm, the outlet radius R2 = 95mm, the number of blades is 6, the rated flow rate Q0 is 3.06L / s, and the Reynolds number defined by the impeller outlet diameter D2 and the impeller outlet circumferential velocity U2 is Re = U2D2 / υ = 1.4×10 6 The computational domain is divided into three parts: the inlet domain, the impeller domain and the outlet domain.

[0101] In this case, OpenFOAM is used to calculate the rotating channel flow example. The number of nodes in the regular hexahedron grid is 393,000. The pressure-velocity coupling adopts the PISO algorithm. The gradient term and the divergence term adopt the "Gauss linear" format, and the time term adopts the backward format. During the calculation process, the average CFL number is kept within 1. The calculation results of the PANS-RCES method proposed in this invention are consistent with the existing two dynamic f based on non-rotating energy spectrum. k The results of PANS-ES1, PANS-ES2 and DNS were compared. All three methods were coupled with the SST k-ωPANS model for calculation. In the centrifugal pump case, ANSYS CFX was used for calculation. The number of hexahedral grid nodes was 2.566 million. A total of 15 rotation cycles were calculated in the unsteady calculation, and the last 10 rotation cycles were time averaged. During the calculation process, the convection term used a high-order solution format, the diffusion term used a central difference format, and the transient term used a second-order backward Euler difference format. The time step is 1×10 -4 s, the convergence residual is 1×10 -5The calculation results of the PANS-RCES (abbreviated as PANS in the figure) method proposed in the present invention are compared with the commonly used SST k-ω model results (abbreviated as SST in the figure) and the PIV test results (abbreviated as PIV in the figure).

[0102] like Figure 6 As shown in the figure, near the suction surface where turbulence is suppressed in the rotating channel flow, all three methods can predict velocity distributions consistent with DNS, but the two SST k-ωPANS-ES1 and SST k-ωPANS-ES2 models based on the non-rotating energy spectrum have large deviations from the DNS results, while the SST k-ωPANS-RCES model results are in good agreement with DNS. Figure 7 As shown in the figure, in the radial velocity distribution at the monitoring position of the centrifugal pump, the SST k-ωPANS-RCES model obtains results that are more consistent with the experimental values ​​than the SST model.

[0103] In the embodiments disclosed in the present application, the computer storage medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. The computer storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. More specific examples of computer storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0104] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0105] The above are only preferred embodiments of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should be regarded as the protection scope of the present invention.

Claims

1. A method for calculating control parameters of a local time-averaged model based on the energy spectrum distribution of rotating turbulence, characterized in that: The following steps are involved: Step 1: Assuming that the numerical simulation method is used to solve the turbulence process, the range of the turbulence energy spectrum that can be modeled or directly solved by the turbulence model coupled with the NS equation is: the wave number κ that can be solved by the RANS method l The wave number κ that can be solved by DNS method η ; Step 2: Introduce Zeman number κ Ω , the solvable κ l ~κ η The energy spectrum between the two parts is divided into two parts, and the energy spectrum distribution of rotating turbulence is obtained; In numerical simulation, the calculation method of grid scale Δ is: Δ=(ΔxΔyΔz) 1 / 3 ; Where Δx, Δy and Δz are the dimensions of the grid unit in the x, y and z directions respectively; Wave number κ corresponding to the grid scale Δ for: Zeman number κ in rotating turbulence Ω The expression is: k Ω =(Ω 3 / e) 1 / 2 ; Where Ω is the rotation speed and ε is the turbulent kinetic energy dissipation rate; The energy spectrum distribution of rotating turbulence is: When Δ >κ Ω When the energy spectrum E(κ) and the wave number κ satisfy the relationship: E(κ)=αε 2 / 3 κ -5 / 3 , α is the first empirical coefficient of the energy spectrum; When Δ <κ Ω When, the energy spectrum E(κ) and wave number κ satisfy the relationship: E(κ)=β(Ωε) 1 / 2 κ -2 , β is the second empirical coefficient of the energy spectrum; Step 3: Based on the definition of turbulent kinetic energy and the energy spectrum distribution of rotating turbulence, combined with the position of the grid scale Δ in the energy spectrum, the turbulent kinetic energy k modeled in the PANS model is calculated integrally. u and the total turbulent kinetic energy k t ; Step 4: According to the control parameter f in the PANS model k The definition of turbulent kinetic energy k u and the total turbulent kinetic energy k t The expression of , calculate the corresponding control parameter f k expression.

2. The method for calculating control parameters of a local time-averaged model based on the rotating turbulence energy spectrum distribution according to claim 1 is characterized in that: in, In step 1, the wave number κ that can be solved by the RANS method in the turbulence energy spectrum is l The expression is: In the formula, l turb is the turbulence length scale that can be solved by the RANS method; In the formula, k T is the total turbulent kinetic energy in the RANS method, k T =k R +k U , k U is the modeled turbulent kinetic energy, k R is the turbulent kinetic energy solved directly, U i is the instantaneous speed, is the time-averaged velocity; ε is the turbulent kinetic energy dissipation rate; In the turbulent energy spectrum, the wave number κ that can be solved by the DNS method is η The expression is: Where η is the length scale that can be solved by the DNS method, called the Kolmogorov length scale, and its expression is: Where ν is the dynamic viscosity of the fluid.

3. The method for calculating control parameters of a local time-averaged model based on the rotating turbulence energy spectrum distribution according to claim 1 is characterized in that: in, In step 3, it is assumed that in the PANS calculation, the grid scale is between the turbulence length scale and the Kolmogorov length scale; The total turbulent kinetic energy k t Calculated by the following formula:

4. The method for calculating control parameters of a local time-averaged model based on the rotating turbulence energy spectrum distribution according to claim 3 is characterized in that: in, For wave number κ Δ ~κ η , the turbulent kinetic energy is solved by modeling; When Δ <κ Ω When the modeled turbulent kinetic energy k u for: When Δ >κ Ω When the modeled turbulent kinetic energy k u for:

5. The method for calculating control parameters of a local time-averaged model based on the rotating turbulence energy spectrum distribution according to claim 4 is characterized in that: in, In step 4, the control parameter f k The definition of is: k =k u / k t .

6. The method for calculating control parameters of a local time-averaged model based on the rotating turbulence energy spectrum distribution according to claim 5, characterized in that: in, The first empirical coefficient α of the energy spectrum and the second empirical coefficient β of the energy spectrum are both 1.8; Control parameter f k The expression is:

7. The method for applying the method for calculating control parameters of the local time-averaged model based on the rotating turbulence energy spectrum distribution according to any one of claims 1 to 6 in the numerical calculation of rotating turbulence, characterized in that: When calculating rotating turbulence applications, the classic SST k-ω model is first used as the base RANS model to derive its corresponding PANS model; Then, according to the local time-averaged model control parameter calculation method based on the rotating turbulence energy spectrum distribution, the control parameter f derived based on the rotating turbulence energy spectrum is calculated. k The expression is substituted into the PANS model expression of the SST k-ω model; Then, the modified PANS model is coupled with the NS equations to form a nonlinear equation set, which can be used for numerical model calculations of rotating turbulence after being discretized using the finite volume method.

8. A computer-readable storage medium storing a computer program, characterized in that: The computer program enables the computer to execute the method for calculating control parameters of a local time-averaged model based on the rotating turbulence energy spectrum distribution as described in any one of claims 1 to 6.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for calculating control parameters of a local time-averaged model based on the rotating turbulence energy spectrum distribution as described in any one of claims 1 to 6 is implemented.

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