Microstructure resistance reduction simulation method and system based on turbulence model correction and storage medium
By modifying the wall boundary conditions of the Reynolds-averaged turbulence model and utilizing dimensionless microstructure parameters, the problem of the imbalance between efficiency and accuracy in the evaluation of microstructure drag reduction performance was solved, and efficient and accurate microstructure drag reduction simulation was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CENT SOUTH UNIV
- Filing Date
- 2025-12-31
- Publication Date
- 2026-04-14
AI Technical Summary
Existing methods for evaluating the drag reduction performance of microstructures cannot achieve a balance between computational efficiency and accuracy, which limits their engineering applications.
By establishing a correction function SR, the wall boundary conditions of the turbulent dissipation rate ω are corrected based on the Reynolds-averaged turbulence model. Combined with dimensionless microstructure parameters, efficient simulation calculation of the drag reduction effect of microstructure is achieved, avoiding high-cost geometric modeling and ultra-fine mesh generation.
While maintaining computational efficiency, the accuracy and consistency of predictions for the drag reduction effect of microstructures are improved, and stable simulation results are output.
Smart Images
Figure CN121859779A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the fields of fluid mechanics and biomimetic drag reduction technology, specifically to a microstructure drag reduction simulation method, system, and storage medium based on turbulence model modification. Background Technology
[0002] Surface microstructure drag reduction technology is an important achievement combining bionics and fluid mechanics. Specific surface microstructures include V-shaped, rectangular, and U-shaped grooves. Currently, evaluating the drag reduction performance of microstructures mainly relies on two types of methods: one is high-fidelity direct numerical simulation (DNS), which, although accurate, requires extremely fine geometric modeling and cross-scale mesh generation of the microstructure, consuming huge computational resources and making it difficult to use for engineering design and rapid optimization; the second is engineering turbulence models based on Reynolds-averaged RANS, which have high computational efficiency. However, traditional RANS models cannot accurately reflect the near-wall turbulent pseudo-sequence structure changes caused by microstructures, and their predictive ability for drag reduction effects is insufficient.
[0003] In summary, existing technologies either sacrifice accuracy for speed, failing to guide optimization, or incur extremely high computational costs in pursuit of accuracy, hindering their engineering applications. Therefore, a novel simulation method is urgently needed that can maintain computational efficiency while accurately predicting the drag reduction effect of microstructures. Summary of the Invention
[0004] The technical problem to be solved by the present invention is that existing methods for evaluating the drag reduction performance of microstructures cannot balance efficiency and accuracy. The present invention provides a high-efficiency and highly accurate microstructure drag reduction simulation method, system and storage medium based on turbulence model correction.
[0005] To achieve the above objectives, the first aspect of this application provides a microstructure drag reduction simulation method based on turbulence model modification, comprising: S1: Obtain the geometric parameters of the microstructure to be simulated, and obtain multiple sets of velocity profile offsets. and the offset of each velocity profile The corresponding dimensionless microstructure parameters; S2, based on multiple sets of velocity profile offsets And its corresponding dimensionless microstructure parameters establish a correction function SR, which is used to quantitatively characterize the influence of microstructure on near-wall turbulent dissipation characteristics; S3. Select the Reynolds-mean-turbulence model, and modify the wall boundary conditions of the turbulence dissipation rate ω in the selected Reynolds-mean-turbulence model based on the correction function SR to obtain the modified Reynolds-mean-turbulence model. S4. In the computational fluid dynamics software, select the wall region in the smooth wall geometry model that needs to simulate the microstructure effect, input the geometric parameters of the microstructure, and use the modified Reynolds-averaged turbulence model to perform simulation calculations on the wall region, and output simulation results that reflect the drag reduction effect of the microstructure. The simulation results include at least the wall friction drag and drag coefficient.
[0006] In this embodiment of the application, the dimensionless microstructure parameters in S1 include at least: dimensionless height parameters and dimensionless spacing parameters, and satisfy the following relationship; , ; in, For friction speed, The viscosity coefficient is... h The height of the microstructure is a geometric parameter of the microstructure. s The microstructure spacing is a geometric parameter of the microstructure. For highly dimensionless parameters, The distance is a dimensionless parameter.
[0007] In this embodiment of the application, S2 includes: S21, Define the SR function to satisfy the following relation:
[0008] in, n r The shape parameter is a power of the height term; n s The shape parameter is a power of the spacing term; For peak position parameters, For the peak position parameters of the height and spacing terms, C 2 is the regularization parameter; C 1e For the exponential scaling parameter of the height term, C 2e This is the exponential scaling parameter for the spacing term; S22, each set of dimensionless structural parameters Substituting into the SR function and solving, we get And calculate the error function, the formula is: ; in, Offset of each velocity profile The number; For the first The solution obtained by the group The predicted value, This is the error value. for Group velocity profile offset ; N Velocity profile offset The total number of groups; S23, Adjust the parameters of the SR function. The preset number of adjustments is used, and the error value is obtained by executing S22 each time. ;in Take a positive integer, and ; S24, the minimum error value obtained after multiple adjustments. The corresponding parameters are used as fixed parameters to obtain the correction function SR.
[0009] In this embodiment of the application, S3 includes: Select k The ωSST turbulence model is used as the Reynolds-averaged turbulence model, and the correction function SR is used with k The wall treatment is related to the turbulent dissipation rate ω in the ωSST turbulence model; the boundary conditions for the turbulent dissipation rate ω at the wall of the wall region with microstructures satisfy the following relationship: ; in, For friction speed, This refers to kinematic viscosity.
[0010] In this embodiment of the application, the microstructure is a V-shaped groove, a rectangular groove, or a U-shaped groove.
[0011] In this embodiment of the application, a control region is selected in the smooth wall geometry model in S4. The control region is positioned opposite to the wall region. The control region is simulated using an initial Reynolds-mean-turbulence model.
[0012] A second aspect of this application provides a microstructure drag reduction simulation system, comprising: The data processing module acquires the geometric parameters of the microstructure to be simulated and obtains multiple sets of velocity profile offsets. and the offset of each velocity profile The corresponding dimensionless microstructure parameters; The correction function building module is based on multiple sets of velocity profile offsets. And its corresponding dimensionless microstructure parameters establish a correction function SR, which is used to quantitatively characterize the influence of microstructure on near-wall turbulent dissipation characteristics; The model building module selects the Reynolds-mean-turbulence model and modifies the wall boundary conditions of the turbulence dissipation rate ω in the selected Reynolds-mean-turbulence model based on the correction function SR, thereby obtaining the modified Reynolds-mean-turbulence model. The simulation calculation module, in the computational fluid dynamics software, selects the wall region in the smooth wall geometry model that needs to simulate the microstructure effect, inputs the geometric parameters of the microstructure, and uses the modified Reynolds-averaged turbulence model to perform simulation calculations on the wall region, outputting simulation results that reflect the drag reduction effect of the microstructure. The simulation results include at least the wall friction drag and drag coefficient.
[0013] A third aspect of this application provides a storage medium storing a program that, when executed by a processor, implements the aforementioned microstructure drag reduction simulation method based on turbulence model correction.
[0014] The above technical solution achieves the following technical effects: This application establishes a correction function SR to equate the influence of microstructures to the coefficient correction of the ω-wall boundary condition, enabling the near-wall turbulent dissipation changes caused by microstructures to be quantitatively introduced within the Reynolds-averaged turbulence framework. This avoids the high-cost geometric modeling and ultra-fine mesh generation required for real microstructures, thus improving the ability to... The predictions of wall friction resistance and drag coefficient are consistent, thus providing stable and low-cost simulation results of microstructure drag reduction effect. Attached Figure Description
[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0016] Figure 1 A schematic flowchart of a method according to an embodiment of this application is shown. Figure 2 A schematic diagram of the microstructure in an embodiment of this application is shown. Detailed Implementation
[0017] To facilitate understanding of the present invention, the present invention will be described more fully and in detail below with reference to the accompanying drawings and preferred embodiments, but the scope of protection of the present invention is not limited to the following specific embodiments.
[0018] Unless otherwise defined, all technical terms used herein have the same meaning as commonly understood by those skilled in the art. The technical terms used herein are for the purpose of describing particular embodiments only and are not intended to limit the scope of the invention.
[0019] Unless otherwise specified, all raw materials, reagents, instruments and equipment used in this invention can be purchased from the market or prepared by existing methods.
[0020] Figure 1 The schematic diagram illustrates a process flow of an embodiment of this application. In one embodiment of this application, a microstructure drag reduction simulation method based on turbulence model correction is provided, comprising the following steps: S1, obtain the geometric parameters of the microstructure to be simulated, and obtain multiple sets of velocity profile offsets. and the offset of each velocity profile The corresponding dimensionless microstructure parameters will be used to offset the velocity profile. Multiple datasets are formed, each corresponding one-to-one with the dimensionless microstructure parameters, to establish the SR correction function; S2, based on multiple sets of velocity profile offsets And the corresponding offset of each velocity profile The dimensionless microstructure parameters are used to establish a correction function SR; the correction function SR can take the dimensionless microstructure parameters as input and output correction coefficients that characterize the influence of microstructure on near-wall turbulent dissipation characteristics, thus reflecting the effect of different microstructures on velocity profile shift and turbulent dissipation changes in an equivalent sense. S3. Select a Reynolds-mean-flow turbulence model as the basic turbulence model. Under smooth wall conditions, this type of model has a preset turbulence dissipation rate ω wall boundary condition or an empirical treatment method near the wall. In this step, based on the correction function SR obtained in step S2, the wall boundary condition of the turbulence dissipation rate ω in the selected Reynolds-mean-flow turbulence model is modified to obtain the modified Reynolds-mean-flow turbulence model. S4. In the computational fluid dynamics software, select the wall region in the smooth wall geometry model that needs to simulate the microstructure effect, input the geometric parameters of the microstructure, and use the modified Reynolds-averaged turbulence model to perform simulation calculations on the wall region. Output the simulation results that reflect the drag reduction effect of the microstructure. The simulation results should include at least the wall friction drag and drag coefficient.
[0021] In a specific embodiment, the microstructure of S1 can be in the form of microgrooves, i.e., V-shaped grooves, rectangular grooves, and U-shaped grooves; geometric parameters can include the height and spacing of the microstructures, etc.; and multiple sets of velocity profile offsets are obtained based on the DNS method (or experiments). Velocity profile offset Each set of dimensionless microstructure parameters corresponds to a set of geometric parameters of the microstructure.
[0022] In a specific example, a geometric model containing only smooth walls is established based on the target engineering object (e.g., the external flow field of a train). A corresponding computational mesh is generated. The smooth wall geometric model is divided into two relatively independent regions: one region for simulating the microstructure and the other for reference. The reference region uses an uncorrected Reynolds-averaged turbulence model to calculate the baseline flow field. The wall region for simulating the microstructure, without establishing the microstructure's solid geometry, incorporates the geometric parameters of the microstructure. h , s Calculated dimensionless microstructure parameters The correction coefficients are obtained by solving the correction function SR. Then, the ω wall boundary conditions are corrected by the correction coefficients obtained by the correction function SR. The control region and the wall region used to simulate the microstructure adopt the same inlet conditions, outlet conditions, fluid property parameters, mesh resolution and solver settings to complete one calculation and obtain the control results and correction results in the same flow field. The simulation results (control results and correction results) include at least the wall friction drag and drag coefficient.
[0023] By equating the influence of microstructures to the coefficient correction of the ω-wall boundary conditions, the near-wall turbulent dissipation changes caused by microstructures can be quantitatively introduced within the Reynolds-mean-turbulence framework, avoiding costly geometric modeling and ultra-fine mesh generation of real microstructures. Furthermore, a control region is set within the geometric model, enabling direct comparison between the control and correction results under the same operating conditions, reducing errors caused by differences in external conditions; and improving the ability to... The prediction consistency of wall friction resistance and drag coefficient is improved, thus providing a more stable output of the simulation results of microstructure drag reduction effect.
[0024] In this embodiment of the application, the dimensionless microstructure parameters include at least: a dimensionless height parameter and a dimensionless spacing parameter, and satisfy the following relationship; , ; in, h The height of the microstructure is a geometric parameter of the microstructure. s The microstructure spacing is a geometric parameter of the microstructure. For highly dimensionless parameters, The distance is a dimensionless parameter.
[0025] It should also be noted that the fluid density parameter ρ and viscosity coefficient are obtained through... By combining prior calculations, the wall shear stress at the wall region where the microstructure is to be installed is obtained. Then, the friction speed is calculated based on the fluid density and wall shear stress. .
[0026] In this embodiment of the application, S2 specifically includes: S21, Define the SR function to satisfy the following relation:
[0027] in, n r The shape parameter is a power of the height term; n s The shape parameter is a power of the spacing term; For peak position parameters, For the peak position parameters of the height and spacing terms, C 2 is the regularization parameter; C 1e For the exponential scaling parameter of the height term, C 2e This is the exponential scaling parameter for the spacing term; S22, each set of dimensionless structural parameters Substituting into the SR function and solving, we get And calculate the error function, the formula is: ; in, Offset of each velocity profile The number; For the first The group was obtained by solving the SR function. The predicted value, This is the error value. for Group velocity profile offset N is the velocity profile offset. The total number of groups; S23, Adjust the parameters of the SR function multiple times. And each adjustment executes S22 to obtain the error value. ;in Take a positive integer, and It should also be noted that the specific number of adjustments is preset, and during the adjustment process, different parameter values are selected at intervals within the corresponding preset ranges of the aforementioned parameters.
[0028] S24, the minimum error value obtained after multiple adjustments. The corresponding parameters are used as fixed parameters to obtain the correction function SR.
[0029] In this embodiment of the application, S3 specifically includes: Select K The ωSST turbulence model is used as the Reynolds-averaged turbulence model, and the correction function SR constructed in step S2 is compared with K. The turbulent dissipation rate ω in the ωSST turbulence model is related to the wall treatment; in this embodiment, for wall regions with microstructures, an equivalent boundary condition based on the correction function SR is used to replace or modify the original ω wall treatment, thereby effectively introducing the influence of the microstructure on near-wall turbulent dissipation into K. In the ωSST model.
[0030] In practice, after determining the wall region corresponding to the microstructure and obtaining the dimensionless microstructure parameters of that region... , Then, the boundary conditions for the turbulent dissipation rate ω at the wall surface of the wall region with the microstructure are modified using the correction function SR to satisfy the following relationship: ; in, For dimensionless microstructure parameters A determined correction function value is obtained when the geometric dimensions corresponding to the geometric parameters of the absence of microstructure or microstructure tend to zero. The boundary condition can approach 1, at which point it degenerates into a standard k-ωSST turbulence model approaching a smooth wall; however, when the geometric parameters of the microstructure take different non-zero values... As an amplifying or weakening factor, By applying weights, the magnitude of the turbulent dissipation rate ω at the wall is altered, ensuring that the obtained near-wall velocity profile and turbulent viscosity distribution reflect the velocity profile offset caused by the microstructure. And the changing trend of turbulent dissipation.
[0031] In specific embodiments, such as Figure 2 As shown, the microstructure is any one of V-shaped groove, rectangular groove, and U-shaped groove, where (a) is a V-shaped groove, (b) is a rectangular groove, and (c) is a U-shaped groove.
[0032] In a specific operational example of this application, the open-source computational fluid dynamics software OpenFOAM is used as the platform to implement k based on the correction function SR. The ωSST turbulence model is modified, and the drag reduction effect of groove-like microstructures is equivalently simulated on a smooth wall geometry model.
[0033] First, find K in the OpenFOAM source code. The base class equations file for the ωSST turbulence model. For example, in a typical version, it can be found at: In the directory src / TurbulenceModels / turbulenceModels / Base / kOmegaSST / , locate the two files kOmegaSST.C and kOmegaSST.H. kOmegaSST.C is the main implementation file, and kOmegaSST.H is the header file. In this embodiment, only the correction function SR and related calculation logic of this application need to be added to kOmegaSST.C; the header file can remain unchanged.
[0034] Locate the solution for the turbulent dissipation rate ω in the kOmegaSST.C file. Specifically, enter the `correct()` function of the class. Within this function, you can see the discrete forms of the k-equation and the ω-equation, as well as the wall treatment portion. In this embodiment, in the code segment constructing the ω-equation, especially those related to wall boundary conditions, a call to the correction function SR is inserted, replacing the original fixed-form ω-wall treatment for smooth walls with an equivalent treatment based on SR.
[0035] The correction function SR and dimensionless microstructure parameters are calculated in the ω equation. SR can be expressed as an empirical function obtained by pre-fitting or calibrating according to step S2, or by interpolation using a lookup table. Taking the empirical function form as an example, its analytical expression is: ; in, The shape parameter is a power of the height term; The shape parameter is a power of the spacing term; For peak position parameters, For the peak position parameters of the height and spacing terms, For regularization parameters; For the exponential scaling parameter of the height term, This is the exponential scaling parameter for the spacing term; Each parameter is determined through the calibration process of S2, and the preferred specific parameters are as follows: =9, =2, =13.76, =1.0×10 -4 , =1.0×10 9 , =0.1, =0.029.
[0036] Calculate the dimensionless height parameter separately. and dimensionless parameters of spacing Specific calculation formula: ; ; in,h The height of the microstructure is a geometric parameter of the microstructure, in meters. s The microstructure spacing is a geometric parameter of the microstructure, in meters. For highly dimensionless parameters, The distance is a dimensionless parameter. Friction velocity, in m / s; The viscosity coefficient is approximately 1.5 × 10⁻⁵ m. 2 / s.
[0037] The modified OpenFOAM code was recompiled to generate a new k-ωSST turbulence model containing the correction function SR. When using this model for flow field simulation, only the smooth wall geometry needs to be constructed during the geometric modeling stage, without explicitly modeling the solid geometry of microstructures such as grooves in the computational domain. During the boundary condition setting stage, corresponding to the locations where microstructures are laid on the actual wall, wall partitions are defined on the smooth wall, and the geometric parameters of the microstructures in each partition are input. h , s etc., automatically calculated by the solver. , and call This allows for the dynamic correction of the ω-wall boundary conditions of the partition during the numerical solution process.
[0038] Through the above steps, without changing the overall solution framework, we can achieve the solution for the initial k. The embedded correction of the ωSST turbulence model allows the drag reduction effect of the groove microstructure to be reflected in the simulation results such as wall friction drag and drag coefficient even when the groove microstructure is not displayed in the geometric model. This serves as a specific operational example of the method in this application in engineering software.
[0039] In one embodiment, the present invention also provides a microstructure drag reduction simulation system, comprising: The data processing module acquires the geometric parameters of the microstructure to be simulated and obtains multiple sets of velocity profile offsets. and the offset of each velocity profile The corresponding dimensionless microstructure parameters; The correction function building module is based on multiple sets of velocity profile offsets. And its corresponding dimensionless microstructure parameters establish a correction function SR, which is used to quantitatively characterize the influence of microstructure on near-wall turbulent dissipation characteristics; The model building module selects the Reynolds-mean-turbulence model and modifies the wall boundary conditions of the turbulence dissipation rate ω in the selected Reynolds-mean-turbulence model based on the correction function SR, thereby obtaining the modified Reynolds-mean-turbulence model. The simulation calculation module, in the computational fluid dynamics software, selects the wall region in the smooth wall geometry model that needs to simulate the microstructure effect, inputs the geometric parameters of the microstructure, and uses the modified Reynolds-averaged turbulence model to perform simulation calculations on the wall region, outputting simulation results that reflect the drag reduction effect of the microstructure. The simulation results include at least the wall friction drag and drag coefficient.
[0040] The system includes a processor and a memory. All the above modules are stored in the memory as program units, and the processor executes the above program modules stored in the memory to implement the corresponding functions.
[0041] This application also provides a storage medium storing a program that, when executed by a processor, implements the above-described microstructure drag reduction simulation method based on turbulence model correction.
[0042] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0043] The above are merely preferred embodiments of the present invention. It should be noted that the present invention is not limited to the above embodiments. For those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should also be considered within the scope of protection of the present invention.
Claims
1. A microstructure drag reduction simulation method based on turbulence model correction, characterized in that, include: S1: Obtain the geometric parameters of the microstructure to be simulated, and obtain multiple sets of velocity profile offsets. and the offset of each velocity profile The corresponding dimensionless microstructure parameters; S2, based on multiple sets of velocity profile offsets And its corresponding dimensionless microstructure parameters establish a correction function SR, which is used to quantitatively characterize the influence of microstructure on near-wall turbulent dissipation characteristics; S3. Select the Reynolds-mean-turbulence model, and modify the wall boundary conditions of the turbulence dissipation rate ω in the selected Reynolds-mean-turbulence model based on the correction function SR to obtain the modified Reynolds-mean-turbulence model. S4. In the computational fluid dynamics software, select the wall region in the smooth wall geometry model that needs to simulate the microstructure effect, input the geometric parameters of the microstructure, and use the modified Reynolds-averaged turbulence model to perform simulation calculations on the wall region, and output simulation results that reflect the drag reduction effect of the microstructure. The simulation results include at least the wall friction drag and drag coefficient.
2. The microstructure drag reduction simulation method based on turbulence model correction according to claim 1, characterized in that, The dimensionless microstructure parameters in S1 include at least: dimensionless height parameters and dimensionless spacing parameters, and satisfy the following relationship; ， ； in, For friction speed, The viscosity coefficient is... h The height of the microstructure is a geometric parameter of the microstructure. s The microstructure spacing is a geometric parameter of the microstructure. For highly dimensionless parameters, The distance is a dimensionless parameter.
3. The microstructure drag reduction simulation method based on turbulence model correction according to claim 2, characterized in that, S2 include: S21, Define the SR function to satisfy the following relation: in, The shape parameter is a power of the height term; The shape parameter is a power of the spacing term; For peak position parameters, For the peak position parameters of the height and spacing terms, For regularization parameters; For the exponential scaling parameter of the height term, This is the exponential scaling parameter for the spacing term; S22, each set of dimensionless structural parameters Substituting into the SR function and solving, we get And calculate the error function, the formula is: ； in, Offset of each velocity profile The number; For the first The solution obtained by the group The predicted value, This is the error value. For the first Group velocity profile offset ; N Velocity profile offset The total number of groups; S23, Adjust the parameters of the SR function. The preset number of adjustments is used, and after each adjustment, step S22 is executed to obtain the error value. ;in Take a positive integer, and ; S24, the minimum error value obtained after multiple adjustments. The corresponding parameters are used as fixed parameters to obtain the correction function SR.
4. The microstructure drag reduction simulation method based on turbulence model correction according to claim 3, characterized in that, S3 include: Select k The ωSST turbulence model is used as the Reynolds-averaged turbulence model, and the correction function SR is used with k The wall treatment is related to the turbulent dissipation rate ω in the ωSST turbulence model; the boundary conditions for the turbulent dissipation rate ω at the wall of the wall region with microstructures satisfy the following relationship: ； in, For friction speed, This refers to kinematic viscosity.
5. The microstructure drag reduction simulation method based on turbulence model correction according to claim 1, characterized in that, The microstructure is a V-shaped groove, a rectangular groove, or a U-shaped groove.
6. The microstructure drag reduction simulation method based on turbulence model correction according to claim 1, characterized in that, It also includes selecting a control region in the smooth wall geometry model in S4, the control region being positioned opposite the wall region, and the control region being simulated using an initial Reynolds-averaged turbulence model.
7. A microstructure drag reduction simulation system, characterized in that, include: The data processing module acquires the geometric parameters of the microstructure to be simulated and obtains multiple sets of velocity profile offsets. and the offset of each velocity profile The corresponding dimensionless microstructure parameters; The correction function building module is based on multiple sets of velocity profile offsets. And its corresponding dimensionless microstructure parameters establish a correction function SR, which is used to quantitatively characterize the influence of microstructure on near-wall turbulent dissipation characteristics; The model building module selects the Reynolds-mean-turbulence model and modifies the wall boundary conditions of the turbulence dissipation rate ω in the selected Reynolds-mean-turbulence model based on the correction function SR, thereby obtaining the modified Reynolds-mean-turbulence model. The simulation calculation module, in the computational fluid dynamics software, selects the wall region in the smooth wall geometry model that needs to simulate the microstructure effect, inputs the geometric parameters of the microstructure, and uses the modified Reynolds-averaged turbulence model to perform simulation calculations on the wall region, outputting simulation results that reflect the drag reduction effect of the microstructure. The simulation results include at least the wall friction drag and drag coefficient.
8. A storage medium having a program stored thereon, which, when executed by a processor, implements the microstructure drag reduction simulation method based on turbulence model correction as described in any one of claims 1 to 6.