Strong shear turbulence grid adaptive simulation method of coupling k-series model

Abstract

The invention discloses a strong shear turbulence grid adaptive simulation method coupled with a k-omega series model. The method mainly comprises the following steps of: 1, judging whether a shielding function is applied or not; step 2, identifying a self-adaptive length scale of a shear layer; 3, constructing a scale-related adjustment function based on the turbulence energy spectrum integral; 4, reconstructing the turbulence viscosity of the k-omega series model by using an adjustment function; and 5, performing turbulence simulation by using the reconstructed turbulence viscosity. According to the method, the self-adaptive sub-grid length scale of the shear layer is constructed and introduced into the initial region of the quasi-two-dimensional free shear layer, so that the sub-grid length scale of the initial region of the free shear layer is effectively reduced, and the problem that an existing RANS-LES hybrid model is highly dependent on grids is solved. The calculation accuracy is improved, meanwhile, the calculation consumption is greatly reduced, the turbulence simulation process is remarkably accelerated, and an effective numerical simulation method is provided for solving low-consumption and high-precision simulation of strong shear flow in the complex engineering flow problem.

Description

Technical Field

[0001] The present invention relates to the field of complex fluid mechanics calculations for aeroengines and gas turbines, and particularly to a strongly sheared turbulent grid adaptive simulation method that couples k-ω series models. Background Art

[0002] Turbulence phenomena widely exist in nature and engineering fields. Among them, strongly sheared flows are complex turbulent types that cannot be ignored in many engineering applications. Strongly sheared flows are usually accompanied by extremely large velocity gradients and the interaction between large-scale and small-scale turbulences, with flow characteristics such as non-equilibrium transport and anisotropy. Achieving accurate prediction of strongly sheared turbulence is a major challenge in current turbulence research. To improve the accuracy and reliability of engineering designs, there is an urgent need to develop a turbulence simulation method that can accurately capture the characteristics of strongly sheared flows, is efficient and scalable.

[0003] The commonly used turbulence simulation methods in existing engineering mainly solve the Reynolds-averaged N-S (RANS) equations. Although the computational cost is relatively small, the prediction accuracy for multi-scale, unsteady flows and complex flows with non-equilibrium transport and anisotropic turbulence is not good, making it difficult to achieve refined research on complex flow mechanisms, and severely restricting the improvement of the design level in fields such as fluid machinery. As a high-precision numerical simulation method, the large eddy simulation (LES) method has high accuracy, but its computational cost is much higher than the level that can be tolerated in engineering applications. Under the existing computing power, it is difficult for the LES method to be applied to the flow prediction in complex engineering fields at the engineering design level.

[0004] In the past two decades, the RANS-LES hybrid simulation method has balanced the computational accuracy and efficiency by using the RANS method in the near-wall region and the LES method in the mainstream region, providing a good solution to the problem of high computational cost for high-precision simulation of complex flows. However, the classical RANS-LES hybrid model has relatively strict requirements for grids and requires users to have rich experience in high-precision numerical simulation, so its prediction ability for complex engineering flows is limited. Currently, various new types of grid adaptive turbulence simulations have been proposed, which can effectively improve the prediction ability for complex flows, but the definition of their grid scales cannot adapt to complex geometric grids, and a more suitable grid length scale needs to be combined for strongly sheared layers.

[0005] To improve the accuracy of the RANS-LES hybrid simulation of strong-shear turbulence, the present invention proposes a shear-layer adaptive subgrid length scale. By introducing this scale in the initial region of the quasi-two-dimensional free shear layer, the subgrid length scale in the initial region of the free shear layer is effectively reduced. Without significantly increasing the computational cost, the accuracy and efficiency of the turbulence simulation in the shear layer are improved, thus providing a new approach to solve complex turbulent flow problems. Compared with the publicly disclosed invention patents CN115034162A and CN118133701A, the influence of strongly anisotropic grids on the development of the shear layer can be significantly reduced, the prediction accuracy of the grid-adaptive turbulence simulation method can be further improved, and the computational cost can be effectively reduced. Summary of the Invention

[0006] (I) Technical Problems to be Solved

[0007] The object of the present invention is to propose a grid-adaptive simulation method for strong-shear turbulence coupled with the k-ω series model. By constructing a shear-layer adaptive subgrid length scale and introducing it into the initial region of the quasi-two-dimensional free shear layer, the subgrid length scale in the initial region of the free shear layer is effectively reduced, and the problem of high grid dependence of the existing RANS-LES hybrid model is overcome. While improving the computational accuracy, the computational cost is significantly reduced, the turbulence simulation process is significantly accelerated, and an effective numerical simulation method is provided for the low-cost and high-precision simulation of strong-shear flows in complex engineering flow problems.

[0008] (II) Technical Solutions

[0009] To solve the above technical problems, the present invention provides a grid-adaptive simulation method for strong-shear turbulence coupled with the k-ω series model, including the following steps:

[0010] Step 1, determine whether to apply a shielding function;

[0011] Step 2, identify the shear-layer adaptive length scale;

[0012] Step 3, construct a scale-related adjustment function based on the turbulent energy spectrum integral;

[0013] Step 4, use the adjustment function to reconstruct the turbulent viscosity of the k-ω series model;

[0014] Step 5, perform turbulence simulation using the reconstructed turbulent viscosity;

[0015] ① The determination of whether to apply a shielding function in Step 1 includes:

[0016] Combined with the type of flow state being simulated, determine whether to adopt the shielding function F GAS , specifically, when the type of flow state is free shear flow, the shielding function is not adopted, and at this time the shielding function FGAS = 0; When the flow state type is near-wall flow, a shielding function is adopted, and the shielding function F GAS Selectively use the F1 shielding function, F2 shielding function from the DDES-SST model and the F from the DDES-SA model d Shielding function;

[0017] ② The identification of the shear layer adaptive length scale in step two includes:

[0018] Adopt the shielding function F described in step one GAS , determine the local grid length scale Δ * ; The local grid length scale Δ * Is given by the following formula:

[0019] Δ * = C GAS [(1 - F GAS )Δ SLA + F GAS Δ max

[0020]

[0021] Δ max = max(Δ x , Δ y , Δ z )

[0022]

[0023] Where, C GAS Is an empirical coefficient, taking 0.6; Δ x Is the length of the local hexahedral grid, Δ y Is the width of the local hexahedral grid, Δ z Is the height of the local hexahedral grid; Is the distance from the cell center to the i-th vertex, Is the unit normal vector of the vorticity vector, Is the position vector of the cell vertex, Is the position vector of the cell center;

[0024] Take values according to the following formula:

[0025]

[0026] Where, Is the strain rate tensor, Is the vorticity vector, ω 2 Is the square of the vorticity vector; θ takes 0.99, ​a1 = 0.15, a2 = 0.3; ν is the viscosity coefficient of the fluid, and v t is the turbulent viscosity given by the k-ω series model, and v t∞ is the far-field turbulent viscosity given by the k-ω series model;

[0027] <vtm>is the average value of the local VTM function value and the VTM function values of adjacent grids, F KH is based on the above-mentioned <vtm>Two-segment linear function of values;

[0028] ③ The scale-dependent regulation function constructed based on the integral of the turbulence energy spectrum described in step three includes:

[0029] According to the modeling method of the turbulent kinetic energy in the k-ω series model, the originally modeled turbulent kinetic energy k is obtained m , based on the local grid length scale Δ described in step two * , the actually modeled turbulent kinetic energy k is obtained by integrating based on the turbulence energy spectrum u ; The actually modeled turbulent kinetic energy k u is obtained by the following formula:

[0030]

[0031] where E(κ) is the Kolmogorov turbulence energy spectrum, κ is the turbulence wave number, C k is the Kolmogorov constant coefficient, taking 1.5, ε is the actual turbulence dissipation rate, κ c is the resolvable turbulence cutoff wave number, determined by the local grid length scale Δ described in step two * :

[0032]

[0033] where π is the pi, taking 3.14;

[0034] According to the actually modeled turbulent kinetic energy k u , the originally modeled turbulent kinetic energy k m and the shielding function F described in step one GAS , a dynamic scale-dependent regulation function D is constructed f ; Define the scale ratio as the ratio of the actually modeled turbulent kinetic energy k u and the originally modeled turbulent kinetic energy k m , and the dynamic scale-dependent regulation function D f is a function related to the scale ratio, obtained by the following formula:

[0035]

[0036] l GAS =(1 - F GAS )l u +F GAS l m

[0037]

[0038] where l u is the grid-related scale, l m The turbulent length scale given by the k-ω series model is obtained from the following formula:

[0039]

[0040] where ω m is the original modeled specific dissipation rate obtained from the k-ω series model; β * is a constant coefficient in the k-ω series model, taking 0.09;

[0041] ④ The reconstruction of the turbulent viscosity of the k-ω series model using the adjustment function described in step four includes:

[0042] Adopt the dynamically scale-dependent adjustment function D f described in step three to t regulate the turbulent viscosity ν sfs in the k-ω series model to obtain the reconstructed turbulent viscosity ν

[0043] ν sfs = D f ·ν t

[0044] ⑤ The turbulent simulation using the reconstructed turbulent viscosity described in step five includes:

[0045] Adopt the reconstructed turbulent viscosity ν sfs described in step four to calculate the Reynolds stress and update the transport equation of the k-ω series model; adopt the reconstructed turbulent viscosity ν sfs described in step four to t replace the turbulent viscosity ν

[0046] (III) Beneficial effects

[0047] A strong-shear turbulent grid adaptive simulation method coupling the k-ω series model provided by the present invention has the following beneficial effects: By identifying the local grid size, combining the shear layer adaptive sub-grid length scale to determine the local grid length scale, and then reconstructing the turbulent viscosity through the scale-dependent function constructed by the turbulent energy spectrum integral, grid adaptive simulation is realized, effectively improving the problems of large computational cost and poor accuracy in the simulation of the shear layer by the RANS-LES hybrid simulation method, and significantly accelerating the turbulent simulation process.

[0048] Compared with the prior arts CN 115186607A and CN 117763846A, by introducing the SLA length scale, the method of the present invention can significantly reduce the influence of the grid on the development of the shear layer, further improve the prediction accuracy of the grid adaptive turbulence simulation method, effectively reduce the computational cost, and provide an important means for solving the problem of strong shear multi-scale complex flow in an aero-engine.

[0049] The present invention has the characteristics of simple form and strong portability, can be well combined with various grid adaptive turbulence simulations, is convenient to be implanted into existing CFD codes for application and expansion, has broad academic and engineering application prospects, and provides an effective low-cost and high-precision numerical simulation method for predicting strong shear flows in solving complex engineering flow problems such as the internal flow of aero-engines. Brief Description of the Drawings

[0050] Figure 1 is a flow chart of a strong shear turbulence grid adaptive simulation method coupling the k-ω series model of the present invention;

[0051] Figure 2 is the D f distribution cloud map based on the shear layer adaptive sub-grid length scale when the strong shear turbulence grid adaptive simulation method coupling the k-ω series model of the present invention is applied to the specific example of a round tube jet;

[0052] Figure 3 is the turbulent vortex structure diagram of the round tube jet example calculated by using the original SST k-ω model;

[0053] Figure 4 is the turbulent vortex structure diagram of the round tube jet example calculated by using the strong shear turbulence grid adaptive simulation method coupling the k-ω series model of the present invention. Detailed Description of the Specific Embodiment

[0054] The following further details the specific embodiments of the present invention by taking the SST k-ω turbulence model in the k-ω series model as an example and the round tube jet flow as the calculation example in combination with the drawings and embodiments. The following embodiments are only used to illustrate the present invention, but not to limit the scope of the present invention.

[0055] The present invention provides a strong shear turbulence grid adaptive simulation method coupling the k-ω series model, including the following steps:

[0056] Step 1, determining whether to apply a shielding function;

[0057] In this step, in combination with the type of the simulated flow state, it is determined whether to adopt the shielding function F GAS , specifically, when the type of the flow state is free shear flow, the shielding function is not adopted, and at this time the shielding function F GAS = 0; When the flow state type is near-wall flow, a shielding function is adopted, and the shielding function F GAS Selectively use the F1 shielding function, the F2 shielding function from the DDES-SST model, and the F from the DDES-SA model d Shielding function.

[0058] In the circular pipe jet example of the embodiment of the present invention, a high-quality hexahedral mesh is used to spatially discretize the computational domain, and the total number of meshes is approximately 6.05 million.

[0059] In this embodiment, when using the F2 shielding function from the DDES-SST model, there is:

[0060] F GAS = F2

[0061] F2 = tanh(ψ 2 )

[0062]

[0063] where k m is the turbulent kinetic energy of the original modeling of the k-ω series model, and in this embodiment, it is the turbulent kinetic energy of the original modeling of the SST k-ω turbulence model; ω m is the original modeled specific dissipation rate obtained from the k-ω series model, and in this embodiment, it is the original modeled specific dissipation rate obtained from the SST k-ω turbulence model; ν is the viscosity coefficient of the fluid, d is the distance from the local grid to the wall, and β * is a constant coefficient in the SST k-ω turbulence model, taking 0.09.

[0064] Step 2, identify the shear layer adaptive length scale;

[0065] In this step, the shielding function F in Step 1 is adopted GAS , to determine the local grid length scale Δ * ; The local grid length scale Δ * is given by the following formula:

[0066] Δ * = C GAS [(1 - F GAS )Δ SLA + F GAS Δ max

[0067]

[0068] Δ max = max(Δ x , Δ y , Δ z ​)

[0069]

[0070] Among them, C GAS is an empirical coefficient, taking 0.6; Δ x is the length of the local hexahedral mesh, Δ y is the width of the local hexahedral mesh, Δ z is the height of the local hexahedral mesh; is the distance from the cell center to the i-th vertex, is the unit normal vector of the vorticity vector, is the position vector of the cell vertex, is the position vector of the cell center;

[0071] Take values according to the following formula:

[0072]

[0073] Among them, is the strain rate tensor, is the vorticity vector, ω 2 is the square of the vorticity vector; θ takes 0.99, a1 = 0.15, a2 = 0.3; ν is the viscosity coefficient of the fluid, v t is the turbulent viscosity given by the k-ω series model, and in this embodiment, it is the turbulent viscosity given by the SST k-ω turbulence model; v t∞ is the far-field turbulent viscosity given by the k-ω series model, and in this embodiment, it is the far-field turbulent viscosity given by the SST k-ω turbulence model;

[0074] The value range of the above VTM function is between 0 and 1. In the quasi-two-dimensional region at the initial stage of separation, the vorticity vector is also the eigenvector of the strain rate tensor , and at this time, the VTM function value tends to 0; in the fully developed three-dimensional turbulence, the direction of the vorticity vector has a weak correlation with the eigenvector of the strain rate tensor , and at this time, the VTM function value tends to 1.

[0075] <vtm>is the average of the local VTM function value and the VTM function values of adjacent grids, F KH is based on <vtm>Two-segment linear function of values. When <vtm>When it is less than a certain threshold, the VTM function value is small. When <vtm>When it increases gradually, the VTM function value rapidly increases to 1, thereby reducing the grid scale at the initial stage of separation. It can be seen from the sub-grid stress model that when the VTM function value increases to 1, the turbulent kinematic viscosity coefficient also decreases as expected.

[0076] Step 3: Construct a scale-dependent adjustment function based on the integration of the turbulent energy spectrum;

[0077] In this step, according to the way of modeling the turbulent kinetic energy in the k-ω series model, the originally modeled turbulent kinetic energy k m is obtained. In this embodiment, according to the way of modeling the turbulent kinetic energy in the SST k-ω turbulence model, the originally modeled turbulent kinetic energy k m is obtained.

[0078] According to the local grid length scale Δ * in Step 2, the actually modeled turbulent kinetic energy k u is obtained through integration based on the turbulent energy spectrum; the actually modeled turbulent kinetic energy k u is obtained by the following formula:

[0079]

[0080] where E(κ) is the Kolmogorov turbulent energy spectrum, κ is the turbulent wave number, C k is the Kolmogorov constant coefficient, taking 1.5, ε is the actual turbulent dissipation rate, κ c is the resolvable turbulent truncation wave number, which is determined by the local grid length scale Δ * in Step 2:

[0081]

[0082] where π is the pi, taking 3.14;

[0083] According to the actually modeled turbulent kinetic energy k u , the originally modeled turbulent kinetic energy k m and the shielding function F GAS in Step 1, construct a dynamic scale-dependent adjustment function D f , as shown in Figure 2 .

[0084] Define the scale ratio as the ratio of the actually modeled turbulent kinetic energy k u and the originally modeled turbulent kinetic energy k m . The dynamic scale-dependent adjustment function D f is a function related to the scale ratio and is obtained by the following formula:

[0085]

[0086] l GAS =(1 - F GAS )l u + F GAS l m

[0087]

[0088] wherein, l u is the grid - related scale, and l m is the turbulence length scale given by the k - ω series model, which is obtained from the following formula:

[0089]

[0090] wherein, ω m is the original modeled specific dissipation rate obtained from the k - ω series model, and in this embodiment, it is the original modeled specific dissipation rate obtained from the SST k - ω turbulence model; β * is a constant coefficient in the k - ω series model, taking 0.09.

[0091] Step Four, use the adjustment function to reconstruct the turbulence viscosity of the k - ω series model;

[0092] In this step, adopt the dynamic scale - related adjustment function D f to regulate the turbulence viscosity ν t in the k - ω series model, and obtain the reconstructed turbulence viscosity ν sfs , which is obtained from the following formula:

[0093] ν sfs = D f ·ν t

[0094] In this embodiment, taking the SST k - ω turbulence model as an example, regulate the turbulence viscosity ν t in the SST k - ω turbulence model, and obtain the reconstructed turbulence viscosity ν sfs as follows:

[0095]

[0096] wherein, a1 is a constant coefficient in the SST k - ω turbulence model, taking 0.31; S is the strain rate.

[0097] Step Five, use the reconstructed turbulence viscosity for turbulence simulation;

[0098] In this step, adopt the reconstructed turbulence viscosity ν sfs calculated in Step Four to calculate the Reynolds stress and update the transport equation of the k - ω series model. In this embodiment, taking the SST k - ω turbulence model in the k - ω series model as an example, adopt the reconstructed turbulence viscosity ν sfs Replace the turbulent viscosity ν in the SST k-ω turbulence model t , and the resulting new transport equation is shown as follows:

[0099]

[0100] Combine the obtained new transport equation with the SST k-ω turbulence model to obtain a strong shear turbulence grid adaptive simulation method for the coupled k-ω series model, and use it for the numerical simulation of the circular pipe jet example.

[0101] Adopt the fully implicit coupled solution technique for transient calculation, and the time step satisfies the CFL condition in engineering computational fluid dynamics. At the same time, the SST k-ω turbulence model in the k-ω series model is selected to perform numerical simulation on the circular pipe jet example of the embodiment of the present invention, and the numerical simulation results are compared with those of a strong shear turbulence grid adaptive simulation method of a coupled k-ω series model of the present invention.

[0102] Figure 3 Fig. is the turbulent vorticity structure diagram of the circular pipe jet example calculated by using the SST k-ω turbulence model in the k-ω series model, and the turbulent viscosity ratio is used for coloring.

[0103] Figure 4 Fig. is the turbulent vorticity structure diagram of the circular pipe jet example calculated by using a strong shear turbulence grid adaptive simulation method of a coupled k-ω series model of the present invention, and the turbulent viscosity ratio is used for coloring.

[0104] Figure 3 , Figure 4 The comparative analysis shows that the analytical ability of the turbulent vorticity structure obtained by using a strong shear turbulence grid adaptive simulation method of a coupled k-ω series model proposed by the present invention is stronger than that of the vorticity structure calculated by the SST k-ω turbulence model, and it can capture more abundant turbulent structures under the same number of grids, and can provide more accurate flow field details.

[0105] The above is only a preferred embodiment of the patent of the present invention, and it is not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

[0106] In summary, a strong shear turbulence grid adaptive simulation method of a coupled k-ω series model proposed by the present invention, by identifying the local shear layer adaptive sub-grid length scale and improving the local grid length scale Δ * Recognition, further reconstructing the turbulent viscosity by constructing a scale-related function through the integral of the turbulent energy spectrum, realizes a more efficient grid adaptive simulation, which not only greatly reduces the computational cost and significantly speeds up the turbulent simulation process, but also constructs a grid length scale more suitable for strong shear layers, providing an efficient and low-cost numerical simulation method for the high-precision simulation of strong shear flows in complex engineering flow problems.< / vtm> < / vtm> < / vtm> < / vtm> < / vtm> < / vtm>

Claims

1. A grid adaptive simulation method for strongly sheared turbulence coupling the k-ω series model, characterized in that, It includes the following steps: Step 1: Determine whether to apply the shielding function; Step 2: Identify the adaptive length scale of the shear layer; Step 3: Construct a scale-dependent adjustment function based on the turbulent energy spectrum integral; Step 4: Use the adjustment function to reconstruct the turbulent viscosity of the k-ω series model; Step 5: Use the reconstructed turbulent viscosity for turbulent simulation; ① The determination of whether to apply the shielding function described in Step 1 includes: Based on the type of flow state being simulated, determine whether to use the shielding function F GAS , specifically, when the type of flow state is free shear flow, the shielding function is not used, and at this time the shielding function F GAS = 0; when the type of flow state is near-wall flow, the shielding function is used, and the shielding function F GAS Selectively use the F1 shielding function, F2 shielding function from the DDES-SST model and the F d shielding function from the DDES-SA model; ② The identification of the adaptive length scale of the shear layer described in Step 2 includes: Adopt the shielding function F described in step one GAS , and determine the local grid length scale Δ * ; the local grid length scale Δ * is given by the following formula: Δ * = C GAS [(1 - F GAS )Δ SLA + F GAS Δ max ​ Δ max = max(Δ x , Δ y , Δ z ) Among them, C GAS is the empirical coefficient, taking 0.6; Δ x is the length of the local hexahedral mesh, Δ y is the width of the local hexahedral mesh, Δ z is the height of the local hexahedral mesh; is the distance from the cell center to the i-th vertex, is the unit normal vector of the vorticity vector, is the position vector of the cell vertex, is the position vector of the cell center; Take the value according to the following formula: Among them, is the strain rate tensor, is the vorticity vector, ω 2 is the square of the vorticity vector; θ takes 0.99, a1 = 0.15, a2 = 0.3; ν is the viscosity coefficient of the fluid, v t is the turbulent viscosity given by the k-ω series model, v t∞ is the far-field turbulent viscosity given by the k-ω series model; <vtm>is the average value of the local VTM function value and the VTM function values of adjacent grids, F KH is based on the said <vtm>A two-segment linear function of the value;< / vtm> < / vtm> ③ The construction of a scale-dependent adjustment function based on the turbulent energy spectrum integral described in Step 3 includes: According to the modeling method of turbulent kinetic energy in the k-ω series model, the originally modeled turbulent kinetic energy k is obtained m , according to the local grid length scale Δ described in step 2 * , based on the turbulent energy spectrum, the actually modeled turbulent kinetic energy k is obtained by integration u ; the actually modeled turbulent kinetic energy k u is obtained by the following formula: Among them, E(κ) is the Kolmogorov turbulence energy spectrum, κ is the turbulence wave number, and C k is the Kolmogorov constant coefficient, taking 1.5, ε is the actual turbulence dissipation rate, and κ c is the resolvable turbulence cut-off wave number, which is determined by the local grid length scale Δ * described in step two: where π is the ratio of a circle's circumference to its diameter, taking 3.14; According to the turbulent kinetic energy k to be actually modeled u , the originally modeled turbulent kinetic energy k m and the shielding function F described in step one GAS , construct a dynamically scale-dependent adjustment function D f ; define the scale ratio as the ratio of the turbulent kinetic energy k to be actually modeled u and the originally modeled turbulent kinetic energy k m , and the dynamically scale-dependent adjustment function D f is a function related to the scale ratio, obtained by the following formula: l GAS = (1 - F GAS )l u + F GAS l m where l u is the grid-related scale, and l m is the turbulence length scale given by the k-ω series model, which is obtained by the following formula: where, ω m is the original modeled specific dissipation rate obtained from the k-ω series model; β * is a constant coefficient in the k-ω series model, taking 0.09; ④ The reconstruction of the turbulent viscosity of the k-ω series model using the adjustment function described in Step 4 includes: Adopt the dynamic scale-related adjustment function D described in Step 3 f for the turbulent viscosity ν in the k-ω series model t to perform regulation and obtain the reconstructed turbulent viscosity ν sfs , which is obtained from the following formula: ν sfs = D f · ν t ⑤ The use of the reconstructed turbulent viscosity for turbulent simulation described in Step 5 includes: Adopt the reconstructed turbulent viscosity ν described in Step 4 sfs Calculate the Reynolds stress and update the transport equation of the k-ω series model; adopt the reconstructed turbulent viscosity ν described in Step 4 sfs Replace the turbulent viscosity ν in the k-ω series model t , and combine it with the k-ω series model to obtain the strong shear turbulent grid adaptive simulation method of the coupled k-ω series model

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