Strong shear turbulence grid adaptive simulation method coupled with RSM series model

Abstract

The invention discloses a strong shear turbulence grid adaptive simulation method coupled with an RSM 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 coupling RSM series model; 4, reconstructing a Reynolds stress tensor and turbulence viscosity by using an adjustment function; and 5, performing turbulence simulation by using the reconstructed Reynolds stress tensor and 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 effectively improved, the calculation consumption is 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 complex engineering flow.

Description

Technical Field

[0001] The present invention relates to the field of complex fluid mechanics calculations for aero-engines and gas turbines, and particularly to a strongly sheared turbulent grid adaptive simulation method coupled with the RSM series of 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, and have flow characteristics such as non-equilibrium transport and anisotropy. Achieving accurate prediction of strongly sheared turbulence is a major challenge in current turbulence research. In order 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 small, the prediction accuracy for multi-scale, unsteady flows and complex flows with non-equilibrium transport and anisotropic turbulences is not good, and it is difficult to achieve refined research on complex flow mechanisms, which severely restricts 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 the 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 computational 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 the grid 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] In order to improve the accuracy of the RANS-LES hybrid simulation of strong shear turbulence, the present invention proposes a shear layer adaptive sub-grid length scale. By introducing this scale in the initial region of the quasi-two-dimensional free shear layer, the sub-grid 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 CN115186608 A and CN 117763996 A, 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] (1) Technical Problem 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 RSM series model. By constructing a shear layer adaptive sub-grid length scale and introducing it into the initial region of the quasi-two-dimensional free shear layer, the sub-grid 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 greatly 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] (2) Technical Solution

[0009] To solve the above technical problem, the present invention provides a grid adaptive simulation method for strong shear turbulence coupled with the RSM 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-dependent adjustment function based on the turbulent energy spectrum integral and couple it with the RSM series model;

[0013] Step 4, use the adjustment function to reconstruct the Reynolds stress tensor and turbulent viscosity;

[0014] Step 5, perform turbulence simulation using the reconstructed Reynolds stress tensor and 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 flow state type 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, the shielding function is adopted, and the shielding function F GAS selectively uses the F1 shielding function and F2 shielding function from the DDES-SST model and the F d shielding function from the DDES-SA model;

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

[0018] Adopt the shielding function F GAS in Step 1 to 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] Among them, 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, and r is the position vector of the cell center;

[0024] Take values according to the following formula:

[0025]

[0026] 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 RSM series model, v t∞ is the far-field turbulent viscosity given by the RSM 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 said <vtm>Two-segment linear function of values;

[0028] ③ The scale-dependent adjustment function constructed based on the integral of the turbulent energy spectrum coupling the RSM series models described in step three includes:

[0029] The velocity fluctuations u' in all directions solved according to the RSM series models m , to obtain the original modeled turbulent kinetic energy k m , as shown in the following formula:

[0030]

[0031] According to the local grid length scale Δ described in step two * , the actual turbulent kinetic energy k to be modeled is obtained by integrating based on the turbulent energy spectrum u , the actual turbulent kinetic energy k to be modeled u is obtained from the following formula:

[0032]

[0033] 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 turbulence cut-off wave number, which is determined by the local grid length scale Δ described in step two * :

[0034]

[0035] where π is the pi, taking 3.14;

[0036] According to the actual turbulent kinetic energy k to be modeled u , the original modeled turbulent kinetic energy k m and the shielding function F described in step one GAS , construct the dynamic scale-dependent adjustment function D f ; define the scale ratio as the ratio of the actual turbulent kinetic energy k to be modeled u and the original 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 from the following formula:

[0037]

[0038] l GAS =(1 - F GAS )l u + F GAS l m

[0039]

[0040] Among them, l u is the grid-related scale, and l m is the turbulence length scale given by the RSM series of models;

[0041] ④ The reconstruction of the Reynolds stress tensor and the turbulent viscosity using the adjustment function described in step four includes:

[0042] Adopt the dynamically scale-related adjustment function D f described in step three to regulate the Reynolds stress tensor in the RSM model to obtain the reconstructed Reynolds stress tensor given by the following formula:

[0043]

[0044] Adopt the dynamically scale-related adjustment function D f described in step three to t regulate the turbulent viscosity ν sfs in the RSM model to obtain the reconstructed turbulent viscosity ν sfs ν f = D t · ν sfs

[0046] ⑤ The turbulent simulation using the reconstructed Reynolds stress tensor and turbulent viscosity described in step five includes:

[0047] Adopt the reconstructed Reynolds stress tensor described in step four and the reconstructed turbulent viscosity ν sfs to update the transport equation of the RSM series of models; adopt the reconstructed Reynolds stress tensor described in step four and the reconstructed turbulent viscosity ν t to replace the Reynolds stress tensor and the turbulent viscosity ν f in the original transport equation of the RSM series of models, and combine with the RSM series of models to obtain the strong shear turbulent grid adaptive simulation method of the coupled RSM series of models.

[0048] (III) Beneficial effects

[0049] The strong-shear turbulent grid adaptive simulation method coupling the RSM series models provided by the present invention has the following beneficial effects: By identifying the local grid size, combining with the shear layer adaptive sub-grid length scale to determine the local grid length scale, and then reconstructing the turbulent viscosity through the integral of the turbulent energy spectrum to construct a scale-dependent function, 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.

[0050] Compared with the prior arts CN 115186608 A and CN 117763996 A, the method of the present invention can significantly reduce the influence of the grid on the development of the shear layer by introducing the SLA length scale, further improve the prediction accuracy of the grid adaptive turbulent 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.

[0051] The present invention has the characteristics of simple form and strong portability, can be well combined with various grid adaptive turbulent simulations, is convenient to be implanted into the existing CFD code 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. Description of the Drawings

[0052] Figure 1 is a flow chart of the strong-shear turbulent grid adaptive simulation method coupling the RSM series models of the present invention;

[0053] Figure 2 is the D f distribution cloud map based on the shear layer adaptive sub-grid length scale when the strong-shear turbulent grid adaptive simulation method coupling the RSM series models of the present invention is applied to the specific example of a round pipe jet;

[0054] Figure 3 is the turbulent vorticity structure diagram of the round pipe jet example calculated by using the SSG-RSM model;

[0055] Figure 4 is the turbulent vorticity structure diagram of the round pipe jet example calculated by using the strong-shear turbulent grid adaptive simulation method coupling the RSM series models of the present invention. Detailed Embodiments

[0056] The following further details the specific embodiments of the present invention with reference to the drawings and embodiments, taking the SSG-RSM model in the RSM series models as an example and the round pipe jet flow as the calculation example. The following embodiments are only used to illustrate the present invention, but not to limit the scope of the present invention.

[0057] The present invention provides a method for adaptively simulating strong shear turbulence by coupling the RSM series of models, comprising the following steps:

[0058] Step 1: Determine whether to apply a shielding function;

[0059] In this step, in combination 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 F GAS = 0; when the type of flow state is near-wall flow, the shielding function is adopted, and the shielding function F GAS Selectively use the F1 shielding function and F2 shielding function from the DDES-SST model and the F d shielding function from the DDES-SA model.

[0060] 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.

[0061] In this embodiment, when using the F d function from the DDES-SA model, there is:

[0062] F GAS = F d

[0063]

[0064]

[0065] where U ij is the velocity gradient tensor, ν t is the turbulent viscosity given by the SSG-RSM model, ν is the viscosity coefficient of the fluid, d is the distance from the local mesh to the wall, κ is the von Kármán constant, with a value of 0.41; C d1 , C d2 are empirical coefficients, taking 20 and 3 respectively.

[0066] Step 2: Identify the adaptive length scale of the shear layer;

[0067] In this step, adopt the shielding function F GAS in Step 1 to determine the local mesh length scale Δ * ; the local mesh length scale Δ * is given by the following formula:

[0068] Δ * = C GAS [(1 - F GAS )Δ SLA +F GAS Δ max

[0069]

[0070] Δ max =max(Δ x ,Δ y ,Δ z )

[0071]

[0072] where 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;

[0073] takes values according to the following formula:

[0074]

[0075] 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, v t is the turbulent viscosity given by the RSM series model, which is the turbulent viscosity given by the SSG-RSM model in this embodiment; v t∞ is the far-field turbulent viscosity given by the RSM series model, which is the far-field turbulent viscosity given by the SSG-RSM model in this embodiment.

[0076] 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.

[0077] <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 the value. 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 increases rapidly to 1, thereby reducing the grid scale at the initial stage of separation. According to the sub-grid stress model, when the VTM function value increases to 1, the turbulent kinematic viscosity coefficient also decreases as expected.

[0078] Step 3: Construct a scale-dependent adjustment function based on the integral of the turbulent energy spectrum and coupling with the RSM series model;

[0079] In this step, according to the velocity fluctuations u' in all directions solved by the RSM series model m , the original modeled turbulent kinetic energy k m is obtained, as shown in the following formula:

[0080]

[0081] According to the local grid length scale Δ in Step 2 * , the actual turbulent kinetic energy k to be modeled is obtained by integrating based on the turbulent energy spectrum u , and the actual turbulent kinetic energy k to be modeled u is obtained from the following formula:

[0082]

[0083] 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 * :

[0084]

[0085] where π is the pi, taking 3.14;

[0086] According to the actual turbulent kinetic energy k to be modeled u , the original modeled turbulent kinetic energy k m and the shielding function F described in Step 1 GAS , construct a dynamic scale-dependent adjustment function D f ; define the scale ratio as the ratio of the actual turbulent kinetic energy k to be modeled u and the original modeled turbulent kinetic energy k m , and the dynamic scale-dependent adjustment function D f is a function related to the scale ratio, and is obtained from the following formula:

[0087]

[0088] l GAS = (1 - F GAS )l u +F GAS l m

[0089]

[0090] wherein, l u is the grid-related scale, and l m is the turbulence length scale given by the RSM series models, and in this embodiment, it is obtained from the turbulence length scale of the SSG-RSM model, as shown in the following formula:

[0091]

[0092] wherein, ε m is the dissipation rate of the original modeling.

[0093] Step Four: Use the adjustment function to reconstruct the Reynolds stress tensor and the turbulent viscosity;

[0094] In this step, adopt the dynamic scale-related adjustment function D f in Step Three to regulate the Reynolds stress tensor in the RSM model to obtain the reconstructed Reynolds stress tensor In this embodiment, adopt the dynamic scale-related adjustment function D f in Step Three to regulate the Reynolds stress tensor in the SSG-RSM model to obtain the reconstructed Reynolds stress tensor which is obtained from the following formula:

[0095]

[0096] Adopt the dynamic scale-related adjustment function D f in Step Three to regulate the turbulent viscosity ν t in the RSM model to obtain the reconstructed turbulent viscosity ν sfs . In this embodiment, adopt the dynamic scale-related adjustment function D f in Step Three to regulate the turbulent viscosity ν t in the SSG-RSM model to obtain the reconstructed turbulent viscosity ν sfs which is obtained from the following formula:

[0097]

[0098] wherein, C μ is a constant coefficient in the SSG-RSM model, taking 0.09.

[0099] Step Five: Use the reconstructed Reynolds stress tensor and turbulent viscosity to conduct turbulent simulation;

[0100] In this step, the reconstructed Reynolds stress tensor in Step 4 and the reconstructed turbulent viscosity ν sfs are used to update the transport equation of the RSM series models. The reconstructed Reynolds stress tensor in Step 4 and the reconstructed turbulent viscosity ν sfs are used to replace the Reynolds stress tensor and the turbulent viscosity ν t in the original transport equation of the RSM series models, and combined with the RSM series models to obtain the strong shear turbulence grid adaptive simulation method of the coupled RSM series models.

[0101] In this embodiment, the reconstructed Reynolds stress tensor in Step 4 and the reconstructed turbulent viscosity ν sfs are used to update the transport equation in the SSG-RSM model. The reconstructed Reynolds stress tensor in Step 4 and the reconstructed turbulent viscosity ν sfs are used to replace the Reynolds stress tensor and the turbulent viscosity ν t in the original transport equation of the SSG-RSM model, and the new transport equation obtained is shown as follows:

[0102]

[0103] where φ ij is the pressure-strain correlation term of the SSG model, specifically:

[0104] φ ij = φ ij,1 + φ ij,2

[0105]

[0106] where a ij in the formula is the anisotropic tensor, Ω ij is the vorticity tensor, is the generation term of the Reynolds stress transport equation, is the generation term of the dissipation rate transport equation, and is calculated by the following formula:

[0107]

[0108] The model coefficient C s1 involved is 1.7, C s2 is -1.05, C r1 is 0.9, C r2 is 0.8, C r3 is 0.65, C r4 is 0.625, C r5 is 0.2, C ε1 is 1.45, C ε2 is 1.83.

[0109] The obtained new transport equation is combined with the SSG-RSM model to obtain the strong-shear turbulent grid adaptive simulation method of the coupled RSM series models, and it is used for the numerical simulation of the round pipe jet flow example.

[0110] The transient calculation is carried out by adopting the fully implicit coupled solution technique, and the time step satisfies the CFL condition in engineering computational fluid dynamics. At the same time, the SSG-RSM model in the RSM series models is selected to carry out the numerical simulation of the round pipe jet flow example of the embodiment of the present invention, and the numerical simulation results are compared with those of a strong-shear turbulent grid adaptive simulation method of a coupled RSM series model of the present invention.

[0111] Figure 3 It is the turbulent vortex structure diagram of the round pipe jet flow example calculated by using the SSG-RSM model in the RSM series models, and the turbulent viscosity ratio is used for coloring.

[0112] Figure 4 It is the turbulent vortex structure diagram of the round pipe jet flow example calculated by using a strong-shear turbulent grid adaptive simulation method of a coupled RSM series model of the present invention, and the turbulent viscosity ratio is used for coloring.

[0113] Figure 3 、 Figure 4 The comparative analysis shows that the analytical ability of the turbulent vortex structure obtained by calculating with a strong-shear turbulent grid adaptive simulation method of a coupled RSM series model proposed by the present invention is stronger than that of the vortex structure obtained by calculating with the SSG-RSM model. Under the same number of grids, it can capture richer turbulent structures and provide more accurate flow field details.

[0114] The above is only the preferred embodiment of the present invention patent, and it does not limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0115] To sum up, a strong-shear turbulent grid adaptive simulation method of a coupled RSM series model proposed by the present invention, by identifying the local shear layer adaptive sub-grid length scale, improves the identification of the local grid length scale Δ * Furthermore, the turbulent viscosity is reconstructed by constructing a scale-related function through the integral of the turbulent energy spectrum, realizing a more efficient grid adaptive simulation. It 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 the strong shear layer, 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 strong shear turbulence coupling with the RSM series model, characterized in that It includes the following steps: Step 1: Determine whether to apply the shielding function; Step 2: Identify the shear layer adaptive length scale; Step 3: Construct a scale-dependent adjustment function based on the turbulent energy spectrum integral coupling the RSM series model; Step 4: Use the adjustment function to reconstruct the Reynolds stress tensor and turbulent viscosity; Step 5: Perform turbulent simulation using the reconstructed Reynolds stress tensor and turbulent viscosity; ① The determination of whether to apply the shielding function described in Step 1 includes: Determine whether to use the shielding function F in combination with the simulated flow state type GAS , specifically, when the flow state type is free shear flow, the shielding function is not used, and at this time the shielding function F GAS = 0; when the flow state type is near-wall flow, the shielding function is used, and the shielding function F GAS Optionally use the F1 shielding function and F2 shielding function from the DDES-SST model and the F d shielding function from the DDES-SA model; ② The identification of the shear layer adaptive length scale described in Step 2 includes: Adopt the shielding function F described in Step 1 GAS , to 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; I i 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, and r 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 RSM series model, v t∞ is the far-field turbulent viscosity given by the RSM 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 coupling the RSM series model described in Step 3 includes: The velocity fluctuations u' in all directions solved according to the RSM series model m , and the original modeled turbulent kinetic energy k m is obtained as shown in the following equation: According to the local grid length scale Δ described in step two * , the turbulent kinetic energy k that should be actually modeled is obtained by integration based on the turbulent energy spectrum u , the turbulent kinetic energy k that should be actually modeled 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 cutoff wave number, which is determined by the local grid length scale Δ * described in step two: where π is the pi, 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 dynamic scale-related 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 dynamic scale-related adjustment function D f is a function related to the scale ratio and is 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 RSM series of models; ④ The reconstruction of the Reynolds stress tensor and turbulent viscosity using the adjustment function described in Step 4 includes: Adopt the dynamic scale-related adjustment function D described in Step 3 f For the Reynolds stress tensor in the RSM model Perform regulation to obtain the reconstructed Reynolds stress tensor Obtained from the following formula: Adopt the dynamic scale-related adjustment function D described in Step 3 f For the turbulent viscosity ν in the RSM model t Perform regulation to obtain the reconstructed turbulent viscosity ν sfs , which is obtained from the following formula: ν sfs = D f ·ν t ⑤ The performance of turbulent simulation using the reconstructed Reynolds stress tensor and turbulent viscosity described in Step 5 includes: Adopt the reconstructed Reynolds stress tensor described in step four and the reconstructed turbulent viscosity ν sfs to update the transport equation of the RSM series models; adopt the reconstructed Reynolds stress tensor described in step four and the reconstructed turbulent viscosity ν sfs to replace the Reynolds stress tensor and the turbulent viscosity ν t in the original transport equation of the RSM series models, and combine with the RSM series models to obtain the strong shear turbulence grid adaptive simulation method of the coupled RSM series models.

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