A porous medium resistance coefficient calculation method, system, device and medium
By constructing a cylindrical array model of the resistance coefficient of porous media and using a high-resolution numerical method, the problems of high model requirements and low calculation accuracy in existing technologies are solved, realizing a simple and efficient calculation of the resistance coefficient of porous media and obtaining more accurate results.
Patent Information
- Application Number
- CN202310873163.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-07-14
- Publication Date
- 2026-05-15
- Estimated Expiration
- 2043-07-14
AI Technical Summary
Existing technologies require high-precision models, have low computational accuracy, and are complex when calculating the drag coefficient of porous media, making it difficult to solve complex flow problems.
A porous medium geometric model was constructed using a cylindrical array. The flow computational domain was determined and a structural mesh was generated. A high-resolution numerical method was used for computational fluid dynamics simulation. A flow characteristic database was established and integral statistics were performed to construct the drag coefficient equation.
It provides a simple and efficient method to obtain high-precision and refined calculation results, which is applicable to more complex flow problems and yields more accurate results.
Smart Images
Figure CN116956771B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of drag coefficient technology, and in particular to a method, system, computer device, and computer storage medium for calculating the drag coefficient of porous media. Background Technology
[0002] Methods for calculating the drag coefficient of porous media mainly fall into three categories: analytical methods, empirical formula methods, and numerical simulation methods. Analytical methods establish mathematical models based on the physical properties and flow laws of porous media and derive analytical solutions. Empirical formula methods obtain a large amount of data through experimental measurements and fit the experimental data into empirical formulas; commonly used empirical formulas include the Forchheimer formula and the Ergun formula. Numerical simulation methods simulate the flow in porous media using computational fluid dynamics, involving issues such as mesh generation of the flow region and parallel computation of the flow field; commonly used numerical calculation methods include the finite element method and the finite difference method.
[0003] Analytical methods require sophisticated models of the microscopic geometry, porosity, permeability, and other physical properties of porous media, and are currently limited to simple problems and simple flows. Empirical formula methods are often constrained by limited experimental conditions and cannot obtain high-precision and refined experimental results. Traditional numerical simulation methods require mesh generation of the flow region, but real porous media problems face challenges such as complex three-dimensional pore geometry and the difficulty in generating body-fitted meshes. Summary of the Invention
[0004] This application provides a method, system, computer equipment, and storage medium for calculating the resistance coefficient of porous media, in order to solve the technical problems of high model requirements, low calculation accuracy, and complex calculation in the prior art. It provides a simple, efficient, and accurate calculation method to obtain high-precision and refined calculation results.
[0005] To address the aforementioned technical problems, in a first aspect, this application provides a method for calculating the resistance coefficient of porous media, the method comprising:
[0006] A porous medium geometric model is constructed using a cylindrical array, the flow computation domain of the porous medium geometric model is determined, and the flow computation domain is divided into structural meshes.
[0007] Computational fluid dynamics simulations were performed on the porous medium geometric model using high-resolution numerical methods to obtain a flow characteristic database of the porous medium geometric model.
[0008] Integral statistics are performed on the flow characteristic database to obtain the drag coefficient dataset of the porous medium geometric model;
[0009] The drag coefficient equation of the porous medium geometric model is constructed based on the drag coefficient dataset.
[0010] Preferably, constructing the drag coefficient equation for the porous medium geometric model based on the drag coefficient dataset includes:
[0011] Based on the drag coefficient dataset, a functional equation is established for the drag coefficient and the incoming Reynolds number of the porous medium geometric model. The coefficients of the functional equation are functions of the solidity of the porous medium geometric model.
[0012] Based on the drag coefficient dataset, a polynomial function fitting method is used to determine the functional relationship between the coefficients of the function equation and the solidity of the porous medium geometric model.
[0013] Preferably, the functional equation of the drag coefficient and the incoming Reynolds number of the porous medium geometric model is as follows:
[0014]
[0015] Among them, C D The coefficient of drag is represented by Re, where Re represents the incoming Reynolds number, and K0, K1, and K2 represent the coefficients of the functional equation.
[0016] Preferably, the coefficients of the functional equation have a functional relationship with the solidity of the porous medium geometric model as follows:
[0017]
[0018]
[0019]
[0020] Wherein, φ represents the solidity of the porous medium geometric model.
[0021] Preferably, the method of constructing a porous medium geometric model using a cylindrical array includes:
[0022] The geometry of the porous medium is characterized by an N×N uniformly arranged cylindrical array;
[0023] Determine the solids ratio range of the porous media geometric model;
[0024] The spacing between adjacent cylinders is determined based on the solidity range.
[0025] The porous medium geometric model is constructed based on the spacing between the adjacent cylinders.
[0026] Preferably, determining the solids ratio range of the porous media geometric model includes:
[0027] Set the integral outer boundary of the porous medium geometric model so that the integral outer boundary is internally tangent to the outermost cylinder of the porous medium geometric model, and obtain the first overall drag coefficient by integral statistics;
[0028] The outer boundary of the integral is moved inward so that it intersects with the center of the outermost cylinder of the porous medium geometric model, and the second overall drag coefficient is obtained by integral statistics.
[0029] Calculate the error between the first overall drag coefficient and the second overall drag coefficient. If the error is less than a predetermined percentage, it is defined as a sparse porous medium.
[0030] The solids ratio range of the porous medium geometric model is determined based on the error range of the sparse porous medium.
[0031] Preferably, the step of performing computational fluid dynamics simulation on the porous medium geometric model using a high-resolution numerical method to obtain a flow characteristic database of the porous medium geometric model includes:
[0032] Establish the governing equations for the geometric model of the porous medium;
[0033] The control equations are spatially discretized using a high-resolution second-order upwind scheme to verify and confirm the structure-grid independence.
[0034] The range of incoming Reynolds number is determined, and multiple parallel numerical simulations are performed on the porous medium geometric model to obtain a flow characteristic database of the porous medium geometric model. The flow characteristic database includes: fluid velocity, fluid density, and static pressure and drag of the porous medium geometric model.
[0035] Secondly, this application also provides a system for calculating the drag coefficient of porous media, the system comprising: a model building module: used to construct a porous media geometric model using a cylindrical array, determine the flow calculation domain of the porous media geometric model, and perform structural meshing on the flow calculation domain;
[0036] First calculation module: used to perform computational fluid dynamics simulation on the porous medium geometric model using high-resolution numerical methods, and obtain a flow characteristic database of the porous medium geometric model;
[0037] The second calculation module is used to perform integral statistics on the flow characteristic database to obtain the drag coefficient dataset of the porous medium geometric model.
[0038] Model equation construction module: used to construct the drag coefficient equation of the porous medium geometric model based on the drag coefficient dataset.
[0039] Thirdly, this application also provides a computer device, which includes a memory, a processor, and a transceiver connected to each other via a bus; the memory is used to store a set of computer program instructions and data, and to transmit the stored data to the processor, and the processor executes the program instructions stored in the memory to perform the method described above.
[0040] Fourthly, this application also provides a computer-readable storage medium storing a computer program that, when executed, implements the method described above.
[0041] This application provides a method, system, computer device, and computer storage medium for calculating the drag coefficient of porous media. The method constructs a porous media geometric model using a cylindrical array, determines the flow computational domain of the porous media geometric model, and performs structured mesh generation on the flow computational domain. A high-resolution numerical method is used to perform computational fluid dynamics simulation on the porous media geometric model to obtain a flow characteristic database of the porous media geometric model; integral statistics are performed on the flow characteristic database to obtain a drag coefficient dataset of the porous media geometric model; and the drag coefficient equation of the porous media geometric model is constructed based on the drag coefficient dataset. The porous media drag coefficient calculation method provided in this application does not require complex modeling, has a wide range of applications, can capture finer flow details, solve more complex flow problems, and is simple, efficient, and provides more accurate calculation results. Attached Figure Description
[0042] Figure 1 This is a flowchart of a preferred embodiment of the method for calculating the resistance coefficient of porous media provided in this application;
[0043] Figure 2 This is a flowchart of a method for constructing a porous medium geometric model using a cylindrical array, provided in a preferred embodiment of this application.
[0044] Figure 3 This is a preferred embodiment of the N×N cylindrical array diagram provided in this application;
[0045] Figure 4 This is a flowchart of a method for determining the solids ratio range of a porous media geometric model according to a preferred embodiment of this application;
[0046] Figure 5 This is a schematic diagram of the flow computation domain of a porous media geometric model provided in a preferred embodiment of this application;
[0047] Figure 6 This is a schematic diagram of the mesh generation of the flow computation domain of a porous media geometric model provided in a preferred embodiment of this application;
[0048] Figure 7 This is a flowchart of a preferred embodiment of the method for calculating the flow characteristic database of a porous media geometric model provided in this application;
[0049] Figure 8 This is a flowchart of a preferred embodiment of the method for constructing the drag coefficient equation of a porous medium geometric model provided in this application;
[0050] Figure 9 This is a schematic diagram of a porous media resistance coefficient calculation system provided in a preferred embodiment of this application;
[0051] Figure 10 This is a schematic diagram of another porous media resistance coefficient calculation system provided in a preferred embodiment of this application;
[0052] Figure 11 This is a system error cloud diagram for calculating the resistance coefficient of porous media provided in a preferred embodiment of this application;
[0053] Figure 12 This is a schematic diagram of a computer device provided in a preferred embodiment of this application. Detailed Implementation
[0054] The embodiments of this application are described in detail below with reference to the accompanying drawings. The embodiments are provided for illustrative purposes only and should not be construed as limiting the scope of this application. The accompanying drawings are for reference and illustration only and do not constitute a limitation on the scope of patent protection of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.
[0055] Please see Figure 1 To address the technical problems of high model requirements, low calculation accuracy, and complex calculations in existing technologies, this application provides a method for calculating the resistance coefficient of porous media, including the following steps;
[0056] S1. A porous medium geometric model is constructed using a cylindrical array, the flow computation domain of the porous medium geometric model is determined, and the flow computation domain is divided into structural meshes.
[0057] S2. Computational fluid dynamics simulation is performed on the porous medium geometric model using high-resolution numerical methods to obtain a flow characteristic database of the porous medium geometric model.
[0058] S3. Perform integral statistics on the flow characteristic database to obtain the drag coefficient dataset of the porous medium geometric model.
[0059] S4. Construct the drag coefficient equation of the porous medium geometric model based on the drag coefficient dataset.
[0060] The technical solution provided in this application mainly includes two parts: the first part is the "model end", which mainly establishes the model equation about the drag coefficient of porous media through high-resolution computational fluid dynamics; the second part is the "user end", where the user only needs to input the incoming Reynolds number and the solids content of the porous media, and then input them into the "model end" to directly obtain the drag coefficient value.
[0061] The construction process of the "model end" includes all the steps of the porous medium resistance coefficient calculation method provided in the embodiments of this application. Specifically, a porous medium geometric model is first constructed using a cylindrical array. In the embodiments of this invention, the construction of the porous medium geometric model using a cylindrical array is as follows: Figure 2 As shown, it includes the following steps:
[0062] S101. The geometry of the porous medium is characterized by an N×N uniformly arranged cylindrical array.
[0063] S102. Determine the solids ratio range of the porous medium geometric model.
[0064] S103. Determine the spacing between adjacent cylinders based on the solidity range.
[0065] S104. Construct the porous medium geometric model based on the spacing between the adjacent cylinders.
[0066] Specifically, such as Figure 3 As shown, an N×N uniformly arranged array of cylinders is used to characterize the geometry of the porous medium. In this embodiment, N is taken as 10 for illustration. The diameter of each cylinder is the same, denoted by d. The solids ratio of this porous medium geometric model is expressed as... Different solid fractions can be obtained by changing the spacing between adjacent cylinders. Here, D represents the characteristic length of the porous medium geometry model.
[0067] = (N-1)s+Nd, when N is 10, D = 9s+10d.
[0068] In this embodiment, the next step is to determine the solidity range of the porous medium geometric model, and then determine the spacing between adjacent cylinders. Once the spacing between adjacent cylinders is determined, the porous medium geometric model can be constructed based on the spacing between adjacent cylinders.
[0069] In this embodiment of the application, the solids ratio range of the porous media geometric model is determined, such as... Figure 4 As shown, it includes the following steps:
[0070] S1021. Set the integral outer boundary of the porous medium geometric model so that the integral outer boundary is internally tangent to the outermost cylinder of the porous medium geometric model, and obtain the first overall drag coefficient by integral statistics.
[0071] S1022. Move the outer boundary of the integral inward so that the outer boundary of the integral intersects with the center of the outermost cylinder of the porous medium geometric model, and obtain the second overall drag coefficient by integral statistics.
[0072] S1023. Calculate the error between the first overall resistance coefficient and the second overall resistance coefficient. If the error is less than a predetermined percentage, it is defined as a sparse porous medium.
[0073] S1024. Determine the solids ratio range of the porous medium geometric model based on the error range of the sparse porous medium.
[0074] From the detailed flow information of the porous media geometric model, it can be seen that for a densely packed array of cylinders, the solids ratio is relatively large, the internal flow channels are narrow, and the flow velocity is low. However, the external flow velocity of the porous media geometric model is large and plays a dominant role, resulting in a large flow field gradient near the outermost cylinder of the porous media geometric model. The integral statistics of the drag coefficient will have large differences due to small changes in the outer boundary of the integral, and cannot accurately reflect the universal statistical law.
[0075] To avoid the aforementioned problems, this application employs modal decomposition and macroscopic statistical analysis methods to control the density of the cylindrical array, thereby determining the solidity of the porous media geometric model. First, an integral outer boundary of the porous media geometric model is set such that it is internally tangent to the outermost cylinder of the porous media geometric model, as shown below. Figure 3 As shown, the integration interval is made to exactly enclose all cylinders, and the first overall drag coefficient is obtained through integration statistics. Next, the outer boundary of the integration is moved inward so that it intersects the center of the outermost cylinder in the porous medium geometric model, as shown... Figure 3 The dashed box shown represents the outer boundary of the integral after inward shift. The second overall drag coefficient is obtained through integration statistics. The error between the first and second overall drag coefficients is calculated. In this embodiment, an error greater than 10% is defined as compact, an error less than 0.2% is defined as sparse, and an error in between is defined as transitional.
[0076] In this embodiment, a porous medium geometric model with sparse modalities is selected as the porous medium geometric model to be constructed. In this case, the solids ratio range of the porous medium geometric model can be determined according to the error range requirements of the sparse porous medium. In this embodiment, the solids ratio range of the porous medium geometric model is selected as: Φ = 9.48e -5 ~4.3e -9At this point, all modes are sparse, and the statistics of the porous media geometric model are not affected by the integration boundary, resulting in more accurate results. Since N and the cylinder diameter d of the porous media geometric model are known quantities, the solids ratio range and formula of the porous media geometric model can be used to determine the results. This allows us to determine the range of the spacing s between adjacent cylinders. Once the range of s is determined, the geometric model of the porous medium can be constructed.
[0077] In this embodiment, a cylindrical array of sparse modes with minimal influence of the integral statistic boundary is used as the porous medium model, which can reduce the influence of the outer boundary of the integral on the integral statistic and make the constructed model more universal.
[0078] After constructing the porous medium geometric model, it is necessary to determine the flow computation domain of the porous medium geometric model. In the embodiments of this application, such as... Figure 5 The diagram shows the flow computation domain of the porous medium geometric model, where ① represents the location of the cylindrical array.
[0079] To improve computational accuracy, it is necessary to perform structured mesh generation on the flow computation domain of the porous medium geometric model, such as... Figure 6 The diagram shows a schematic of the mesh generation for the flow computational domain of a porous media geometric model.
[0080] In this embodiment, after meshing the flow computation domain of the porous medium geometric model, a high-resolution numerical method is used to perform computational fluid dynamics simulation on the porous medium geometric model to obtain a flow characteristic database of the porous medium geometric model, such as... Figure 7 As shown, it includes the following steps:
[0081] S201. Establish the governing equations for the geometric model of the porous medium.
[0082] S202. The control equations are spatially discretized using a high-resolution second-order upwind scheme to verify and confirm the structure-grid independence.
[0083] S203. Determine the range of incoming Reynolds number and perform multiple parallel numerical simulations on the porous medium geometric model to obtain a flow characteristic database of the porous medium geometric model. The flow characteristic database includes: fluid velocity, fluid density, and static pressure and resistance of the porous medium geometric model.
[0084] First, the model control equations are established. In this embodiment, the control equations used are as follows:
[0085] Continuity equation:
[0086] Momentum equation:
[0087] Where u = (u, v) represents the fluid velocity vector, p is the static pressure of the porous medium geometric model, ρ represents the fluid density, and μ is the viscosity coefficient.
[0088] In this embodiment, to improve the accuracy of the calculation of the drag coefficient of porous media, the flow computation domain of the porous media geometric model is divided into a structured mesh. To improve computational efficiency while maintaining accuracy, the mesh of the flow computation domain needs to be verified and confirmed for independence to determine its size. In this embodiment, a high-resolution second-order upwind scheme is used to spatially discretize the governing equations to verify and confirm the independence of the structured mesh.
[0089] The second-order upwind scheme is a type of higher-order upwind scheme. Its purpose is to ensure at least second-order accuracy under unconditional stability. The specific calculation process is existing technology and will not be specifically limited in the embodiments of this application.
[0090] Next, the range of incoming Reynolds number for the porous media geometry model is determined. In this embodiment, the range of incoming Reynolds number is Re = 1 to 50. Based on the determined range of solids content for the porous media geometry model, multiple large-scale parallel numerical simulations are performed to obtain a refined flow characteristic database of the porous media geometry model. This flow characteristic database includes fluid velocity, fluid density, and the static pressure and drag of the porous media geometry model.
[0091] In this embodiment, after establishing the governing equations of the porous medium geometric model, a high-resolution second-order upwind scheme is used to spatially discretize the governing equations to verify and confirm the independence of the structure mesh and determine the mesh size. This improves computational efficiency while ensuring the fineness of the flow characteristic database of the obtained porous medium geometric model.
[0092] In this embodiment of the application, after obtaining a refined flow characteristic database, integral statistics are performed on the flow characteristic database to obtain the drag coefficient dataset of the porous medium geometric model. The drag coefficient calculation formula is as follows:
[0093]
[0094] Where F represents the geometric resistance of the porous medium, ρ represents the fluid density, and U ∞ The velocity of the incoming flow is represented by , and D represents the characteristic length of the porous medium geometric model.
[0095] By using the drag coefficient calculation formula and a refined flow characteristic database, a complete dataset of drag coefficients for porous media geometric models is obtained. This data is more accurate and complete, ensuring the accuracy of the drag coefficient calculation for porous media.
[0096] In this embodiment of the application, after obtaining the drag coefficient dataset of the porous media geometric model, it is necessary to construct the drag coefficient function equation of the porous media geometric model. The drag coefficient equation of the porous media geometric model is constructed based on the drag coefficient dataset, as follows: Figure 8 As shown, it includes the following steps:
[0097] S401. Based on the drag coefficient dataset, establish a functional equation for the drag coefficient and the incoming Reynolds number of the porous medium geometric model, wherein the coefficients of the functional equation are functions of the solidity of the porous medium geometric model.
[0098] S402. Based on the drag coefficient dataset, a polynomial function fitting method is used to determine the functional relationship between the coefficients of the function equation and the solidity of the porous medium geometric model.
[0099] In this embodiment of the application, based on the drag coefficient dataset, a functional equation is established for the drag coefficient and the incoming Reynolds number of the porous medium geometric model. The expression of the functional equation is as follows:
[0100]
[0101] Among them, C D Re represents the drag coefficient of the porous media geometric model, Re represents the incoming Reynolds number, K0, K1 and K2 represent the coefficients of the functional equation, and K0, K1 and K2 are functions of the solidity of the porous media geometric model.
[0102] In this embodiment of the application, based on the drag coefficient dataset, a polynomial function fitting method is used to establish the functional relationship between K0, K1, and K2 and the solidity of the porous medium geometric model, expressed as:
[0103]
[0104]
[0105]
[0106] Wherein, φ represents the solidity of the porous medium geometric model.
[0107] By combining the functional equations relating the drag coefficient to the incoming Reynolds number of the porous medium geometric model, and the functional relationship between the coefficients of these equations and the solids content of the porous medium geometric model, the equational expression for the drag coefficient of the porous medium geometric model in relation to its solids content and incoming Reynolds number can be determined. In practical applications, given the incoming Reynolds number and the solids content of the porous medium, the drag coefficient can be obtained. The calculation is simple, requires no complex and detailed modeling and data analysis, and is computationally efficient.
[0108] Compared to existing methods for calculating the drag coefficient of porous media, this application uses high-resolution computational fluid dynamics methods to perform refined flow simulations on porous media models, offering the following advantages:
[0109] 1. Compared with analytical methods, it has a wider range of applications and can solve more complex flow problems.
[0110] 2. Compared with empirical formula methods, it is not limited by experimental conditions, can obtain more refined flow details, can carry out small-scale flow mode identification, and macroscopic aerodynamic characteristic statistics, and obtain a more accurate drag coefficient dataset.
[0111] 3. Compared with traditional numerical simulation, the method provided in this application represents the geometry of porous media through cylindrical arrays, which can not only meet the description requirements of the physical properties of real porous media problems, but also avoid the difficulty of generating computational mesh due to the complexity of three-dimensional geometry.
[0112] 4. High-quality structured mesh generation is adopted, and combined with high-resolution computational fluid dynamics methods, the calculation results are more accurate.
[0113] 5. By establishing model equations for the macroscopic drag coefficient, solidity, and Reynolds number, the calculation method becomes simpler and more efficient.
[0114] In summary, addressing the technical problems of high model requirements, low computational accuracy, and computational complexity in existing technologies, this application provides a method for calculating the drag coefficient of porous media. The method employs a cylindrical array to construct a porous media geometric model, determines the flow computational domain of the porous media geometric model, and performs structural meshing on the flow computational domain. High-resolution numerical methods are used to conduct computational fluid dynamics simulations on the porous media geometric model to obtain a flow characteristic database of the porous media geometric model; integral statistics are performed on the flow characteristic database to obtain a drag coefficient dataset of the porous media geometric model; and the drag coefficient equation of the porous media geometric model is constructed based on the drag coefficient dataset. The porous media drag coefficient calculation method provided in this application does not require complex modeling, has a wide range of applications, can capture finer flow details, solve more complex flow problems, and is simple, efficient, and provides more accurate calculation results.
[0115] Accordingly, such as Figure 9 As shown, based on the method for calculating the resistance coefficient of porous media, this embodiment of the invention also provides a system for calculating the resistance coefficient of porous media, the system comprising:
[0116] Model building module 1: used to construct a porous medium geometric model using a cylindrical array, determine the flow computation domain of the porous medium geometric model, and perform structural meshing on the flow computation domain;
[0117] First calculation module 2: used to perform computational fluid dynamics simulation on the porous medium geometric model using high-resolution numerical methods, and obtain a flow characteristic database of the porous medium geometric model;
[0118] The second calculation module 3 is used to perform integral statistics on the flow characteristic database to obtain the drag coefficient dataset of the porous medium geometric model;
[0119] Model equation construction module 4: used to construct the drag coefficient equation of the porous medium geometric model based on the drag coefficient dataset.
[0120] The porous media drag coefficient calculation system provided in this application only requires inputting two parameters into the system: the incoming Reynolds number Re and the porous media solids content φ. After substituting these parameters into the system's model equations, the drag coefficient value can be directly obtained. This porous media drag coefficient calculation system does not require the construction of complex parameter models; the calculation method is simple, efficient, and yields more accurate results.
[0121] In the embodiments of this application, such as Figure 10 As shown, it also includes an error verification module 5, which is used to obtain the predicted value of the porous medium drag coefficient based on the input solids ratio and the incoming Reynolds number, compare and analyze the predicted value of the porous medium drag coefficient with the corresponding value of the drag coefficient dataset of the porous medium geometric model, and obtain the error value of the porous medium geometric model. If the error value is less than a threshold, the porous medium geometric model is applied to the calculation of the porous medium drag coefficient; if the error value is not less than the threshold, the drag coefficient equation of the porous medium geometric model is optimized.
[0122] In this embodiment of the application, after the system model is established, the solids ratio φ and the incoming Reynolds number Re are input to obtain the predicted value C of the drag coefficient. Df The predicted value C of the drag coefficient Df The corresponding value C of the drag coefficient dataset of the porous media geometric model obtained from the second calculation module 3. D A comparative analysis was conducted to evaluate the model's prediction error; the threshold for this application was set at 3%. (C) D The corresponding values in the dataset are used as the accurate values, and the model error is used as the model error. like Figure 11 The diagram shows an error cloud, with a global maximum error of only 2%, indicating that the porous media resistance coefficient calculation system provided in this application has high calculation accuracy.
[0123] Specific limitations regarding the porous media resistance coefficient calculation system can be found in the above-described limitations of the porous media resistance coefficient calculation method, and will not be repeated here. Those skilled in the art will recognize that the various modules and steps described in conjunction with the embodiments disclosed in this application can be implemented in hardware, software, or a combination of both. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.
[0124] like Figure 12 As shown in the figure, an embodiment of the present invention provides a computer device including a memory, a processor, and a transceiver, which are connected to each other via a bus; the memory is used to store a set of computer program instructions and data, and transmits the stored data to the processor; the processor executes the program instructions stored in the memory to perform the steps of the above-described method for calculating the resistance coefficient of porous media.
[0125] The memory may include volatile memory or non-volatile memory, or both; the processor may be a central processing unit, a microprocessor, an application-specific integrated circuit, a programmable logic device, or a combination thereof. By way of example, but not limitation, the programmable logic device described above may be a complex programmable logic device, a field-programmable gate array, a general-purpose array logic, or any combination thereof.
[0126] In addition, memory can be a physically independent unit or integrated with the processor.
[0127] Those skilled in the art will understand that Figure 12 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have the same component arrangement.
[0128] In one embodiment, a computer-readable storage medium is provided for storing one or more computer programs, the one or more computer programs including program code, which, when run on a computer, is used to perform the above-described method for calculating the resistance coefficient of porous media.
[0129] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., SSD), etc.
[0130] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed, it can include the processes of the embodiments of the above methods.
[0131] The method, system, computer equipment, and computer storage medium for calculating the drag coefficient of porous media provided in this application address the technical problems of high model requirements, low calculation accuracy, and complex calculations in existing technologies. A porous media geometric model is constructed using a cylindrical array, the flow computational domain of the porous media geometric model is determined, and a structured mesh is generated for the flow computational domain. A high-resolution numerical method is used to perform computational fluid dynamics simulation on the porous media geometric model to obtain a flow characteristic database of the porous media geometric model. Integral statistics are performed on the flow characteristic database to obtain a drag coefficient dataset of the porous media geometric model, and the drag coefficient equation of the porous media geometric model is constructed based on the drag coefficient dataset. The porous media drag coefficient calculation method provided in this application does not require complex modeling, has a wide range of applications, can capture finer flow details, solve more complex flow problems, and is simple, efficient, and provides more accurate calculation results.
[0132] The embodiments described above are merely preferred embodiments of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various improvements and substitutions without departing from the technical principles of this invention, and these improvements and substitutions should also be considered within the scope of protection of this application. Therefore, the scope of protection of this patent application should be determined by the scope of the claims.
Claims
1. A method for calculating the resistance coefficient of porous media, characterized in that, The method includes: A porous medium geometric model is constructed using a cylindrical array, the flow computation domain of the porous medium geometric model is determined, and the flow computation domain is divided into structural meshes. Computational fluid dynamics simulations were performed on the porous medium geometric model using high-resolution numerical methods to obtain a flow characteristic database of the porous medium geometric model. Integral statistics are performed on the flow characteristic database to obtain the drag coefficient dataset of the porous medium geometric model; Construct the drag coefficient equation for the porous medium geometric model based on the drag coefficient dataset; The step of constructing the drag coefficient equation for the porous medium geometric model based on the drag coefficient dataset includes: Based on the drag coefficient dataset, a functional equation is established for the drag coefficient and the incoming Reynolds number of the porous medium geometric model. The coefficients of the functional equation are functions of the solidity of the porous medium geometric model. Based on the drag coefficient dataset, a polynomial function fitting method is used to determine the functional relationship between the coefficients of the function equation and the solidity of the porous medium geometric model. The functional equation for the drag coefficient and the incoming Reynolds number of the porous medium geometric model is as follows: ； in, This represents the drag coefficient of the porous medium geometric model. Indicates the incoming Reynolds number, , and Represents the coefficients of a functional equation; The coefficients of the functional equation are related to the solidity of the porous medium geometric model as follows: ； ； ； Among them, the This represents the solidity of the porous medium geometric model.
2. The method for calculating the resistance coefficient of porous media as described in claim 1, characterized in that, The method of constructing a porous medium geometric model using a cylindrical array includes: The geometry of the porous medium is characterized by an N×N uniformly arranged cylindrical array; Determine the solids ratio range of the porous media geometric model; The spacing between adjacent cylinders is determined based on the solidity range. The porous medium geometric model is constructed based on the spacing between the adjacent cylinders.
3. The method for calculating the resistance coefficient of porous media as described in claim 2, characterized in that, Determining the solids ratio range of the porous media geometric model includes: Set the integral outer boundary of the porous medium geometric model so that the integral outer boundary is internally tangent to the outermost cylinder of the porous medium geometric model, and obtain the first overall drag coefficient by integral statistics; The outer boundary of the integral is moved inward so that it intersects with the center of the outermost cylinder of the porous medium geometric model, and the second overall drag coefficient is obtained by integral statistics. Calculate the error between the first overall drag coefficient and the second overall drag coefficient. If the error is less than a predetermined percentage, it is defined as a sparse porous medium. The solids ratio range of the porous medium geometric model is determined based on the error range of the sparse porous medium.
4. The method for calculating the resistance coefficient of porous media as described in claim 3, characterized in that, The process employs high-resolution numerical methods to perform computational fluid dynamics simulations on the porous medium geometric model, resulting in a flow characteristic database of the porous medium geometric model, including: Establish the governing equations for the geometric model of the porous medium; The control equations are spatially discretized using a high-resolution second-order upwind scheme to verify and confirm the structure-grid independence. The range of incoming Reynolds number is determined, and multiple parallel numerical simulations are performed on the porous medium geometric model to obtain a flow characteristic database of the porous medium geometric model. The flow characteristic database includes: fluid velocity, fluid density, and static pressure and drag of the porous medium geometric model.
5. A system for calculating the resistance coefficient of porous media, characterized in that, The system includes: Model building module: used to construct a porous medium geometric model using a cylindrical array, determine the flow computation domain of the porous medium geometric model, and perform structural meshing on the flow computation domain; First calculation module: used to perform computational fluid dynamics simulation on the porous medium geometric model using high-resolution numerical methods, and obtain a flow characteristic database of the porous medium geometric model; The second calculation module is used to perform integral statistics on the flow characteristic database to obtain the drag coefficient dataset of the porous medium geometric model. Model equation construction module: used to construct the drag coefficient equation of the porous medium geometric model based on the drag coefficient dataset; The step of constructing the drag coefficient equation for the porous medium geometric model based on the drag coefficient dataset includes: Based on the drag coefficient dataset, a functional equation is established for the drag coefficient and the incoming Reynolds number of the porous medium geometric model. The coefficients of the functional equation are functions of the solidity of the porous medium geometric model. Based on the drag coefficient dataset, a polynomial function fitting method is used to determine the functional relationship between the coefficients of the function equation and the solidity of the porous medium geometric model. The functional equation for the drag coefficient and the incoming Reynolds number of the porous medium geometric model is as follows: ； in, This represents the drag coefficient of the porous medium geometric model. Indicates the incoming Reynolds number, , and Represents the coefficients of a functional equation; The coefficients of the functional equation are related to the solidity of the porous medium geometric model as follows: ； ； ； Among them, the This represents the solidity of the porous medium geometric model.
6. A computer device, characterized in that: The computer device includes a memory, a processor, and a transceiver connected to each other via a bus; the memory stores a set of computer program instructions and data, and transmits the stored data to the processor, which executes the program instructions stored in the memory to perform the method as described in any one of claims 1 to 4.
7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program that, when executed, implements the method as described in any one of claims 1 to 4.