Porous medium heat and mass transport cross-scale calculation method, system and equipment and medium
Through the cross-scale calculation method, porous media is divided into macro units and iteratively solved using machine learning models, which solves the contradiction between accuracy and efficiency in thermal mass transport calculation of porous media, and realizes high-precision, high-speed computing and low memory usage.
Patent Information
- Application Number
- CN202510613656.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-13
- Publication Date
- 2025-08-12
AI Technical Summary
The existing porous media thermal mass transport simulation methods have contradictions in calculation accuracy and efficiency. The macroscopic scale simulation results are uncertain, and the pore scale simulation calculation is large and the efficiency is low, making it difficult to meet the scale span and accuracy requirements for practical applications.
The cross-scale calculation method is used to divide the porous structure into macro units, solve and iterate through machine learning agent models, combine the coupled calculation of macroscopic and pore-scale physics, and update the macro transport parameters until converge.
It realizes high-precision and high-speed thermal mass transport calculation of porous media, reduces the memory usage of computing hardware, and has a wide range of applications.
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Figure CN120470973A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical field of heat and mass transport in porous media, and in particular to a cross-scale calculation method, system, equipment and medium for heat and mass transport in porous media. Background Art
[0002] Heat and mass transport processes in porous media are widespread in nature and engineering, with important applications in fluid transport within biological tissues, energy resource development and utilization, and aerospace thermal protection. Accurately describing these processes is crucial for optimizing the design and precise control of practical applications. Currently, the field of heat and mass transport in porous media generally employs two types of simulation methods: macroscale simulation and pore-scale simulation.
[0003] Macroscale simulations characterize the effects of intrinsic heat and mass transport processes between pores using macroscopic transport parameters and investigate these processes using volume averaging. Compared to pore-scale simulations, macroscale simulations are computationally efficient and require less effort. However, due to the complex heterogeneous solid skeleton structure and multiphase fluid interface interactions in real porous media, macroscale simulation methods lack a basis for setting macroscopic transport parameters, resulting in high uncertainty in simulation results and difficulty meeting the accuracy requirements of practical applications.
[0004] Pore-scale simulation, which divides the solid skeleton structure and studies the intrinsic heat and mass transport processes of fluids between pores, is a high-precision simulation method. However, due to the need for detailed meshing of microscopic pores, pore-scale simulation is computationally intensive and inefficient. With current computer hardware, pore-scale simulations can typically only achieve a scale span of three orders of magnitude in one spatial dimension. However, in practical applications, the spatial scale span of heat and mass transport processes in porous media can reach six to twelve orders of magnitude in one dimension, making this high-precision pore-scale simulation method difficult to meet the scale span requirements of practical applications. Summary of the Invention
[0005] Based on this, it is necessary to provide a cross-scale calculation method, system, equipment and medium for heat and mass transport in porous media to address the above technical problems. It has the advantages of high calculation accuracy and fast calculation speed for the heat and mass transport process in porous media, and low memory usage, so the requirements for computing hardware are relatively low and it has a wide range of applications.
[0006] In a first aspect, the present application provides a cross-scale calculation method for heat and mass transport in porous media, comprising:
[0007] Determine the porous structure, heat and mass transport physical processes and solution conditions of porous media;
[0008] Dividing the porous structure into a plurality of macro units, assigning initial values to macro transport parameters, and repeatedly performing the following steps until the macro transport parameters converge;
[0009] Solve macro-scale physical fields;
[0010] Determining a pore-scale boundary condition of each macro-unit according to the macro-scale physical field, and obtaining a pore-scale physical field based on the pore-scale boundary condition and porous structure of each macro-unit;
[0011] The macroscopic transport parameters are updated according to the pore-scale physical field.
[0012] In some examples, dividing the porous structure into a plurality of macro units includes:
[0013] The porous structure is divided into macro units at equal intervals and uniformly, so as to divide the porous structure into a plurality of macro units.
[0014] In some examples, the macroscale includes a volume-averaged scale or a Darcy scale, and the macroscale physical field includes part or all of a saturation field, a volume-averaged velocity field, a volume-averaged pressure field, and a volume-averaged temperature field;
[0015] The solving of the macro-scale physical field includes: inputting the macro-transport parameters and the fixed solution conditions into a macro-scale percolation solver or a pre-trained macro-scale physical field solving machine learning agent model, and obtaining the solution result of the macro-scale physical field output by the macro-scale percolation solver or the macro-scale physical field solving machine learning agent model.
[0016] In some examples, determining the pore-scale boundary condition of each of the macro-units based on the macro-scale physical field includes:
[0017] According to the macro-scale physical field, the pore-scale boundary conditions of the macro unit are interpolated.
[0018] In some examples, obtaining a pore-scale physical field based on the pore-scale boundary conditions and porous structure of each of the macro units includes:
[0019] Each of the macro units is traversed, and a pre-trained pore-scale physical field solving machine learning proxy model is called in each macro unit to obtain the pore-scale physical field through the pore-scale physical field solving machine learning proxy model.
[0020] In some examples, updating the macroscopic transport parameter based on the pore-scale physical field includes:
[0021] Obtain the macroscopic transport parameters calculated in this round that are equivalent to the pore-scale physical field, and update the macroscopic transport parameters according to the macroscopic transport parameters calculated in this round.
[0022] In some examples, this also includes:
[0023] Obtain the deviation between the macroscopic transport parameters calculated in this round and those calculated in the previous round;
[0024] If the deviation value is smaller than a predetermined threshold, it is determined that the macroscopic transport parameter converges.
[0025] In a second aspect, a cross-scale calculation system for heat and mass transport in porous media is provided, comprising:
[0026] Determination module, used to determine the porous structure of porous media, physical processes of heat and mass transport, and solution conditions;
[0027] A division module, used for dividing the porous structure into a plurality of macro units;
[0028] A calculation module is used to assign initial values to macroscopic transport parameters and repeatedly perform the following steps until the macroscopic transport parameters converge: solving the macroscopic scale physical field; determining the pore-scale boundary conditions of each macroscopic unit according to the macroscopic scale physical field, and obtaining the pore-scale physical field based on the pore-scale boundary conditions and porous structure of each macroscopic unit; and updating the macroscopic transport parameters according to the pore-scale physical field.
[0029] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for cross-scale calculation of heat and mass transport in porous media according to the first aspect and any possible implementation of the first aspect are implemented.
[0030] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the program is executed by a processor, the steps of the cross-scale calculation method of heat and mass transport in porous media of the above-mentioned first aspect and any possible implementation of the first aspect are implemented.
[0031] According to the embodiment of the present application, after determining the porous structure, heat and mass transport physical process, and solution conditions of the porous medium, the porous structure is divided into multiple macro units, and the macro transport parameters are assigned initial values. Then, the macro scale physical field is solved, the pore scale boundary conditions of each macro unit are determined, the pore scale physical field is obtained, and the macro transport parameters are updated according to the pore scale physical field. When the macro transport parameters converge, the iteration is stopped, thereby completing the cross-scale calculation of heat and mass transport in porous media. The method has the advantages of high calculation accuracy and fast calculation speed for the heat and mass transport process of porous media, low memory usage, relatively low requirements for computing hardware, and a wide range of applications. BRIEF DESCRIPTION OF THE DRAWINGS
[0032] Other features, objects and advantages of the present application will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:
[0033] Figure 1 This is a flow chart of the cross-scale calculation method for heat and mass transport in porous media provided in an embodiment of the present application;
[0034] Figure 2 A schematic diagram of a cross-scale calculation method for heat and mass transport in porous media provided in an embodiment of the present application;
[0035] Figure 3 A flow chart of a cross-scale calculation method for heat and mass transport in porous media provided in another embodiment of the present application;
[0036] Figure 4 This is a structural block diagram of the cross-scale calculation system for heat and mass transport in porous media provided in an embodiment of the present application;
[0037] Figure 5 This is a structural block diagram of the computer device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0038] The present application will be further described in detail below with reference to the embodiments and accompanying drawings. It should be understood that the specific embodiments described herein are intended only to illustrate the relevant application and are not intended to limit the application. It should also be noted that, for ease of description, only the portions relevant to the application are shown in the accompanying drawings.
[0039] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.
[0040] The following describes in detail the cross-scale calculation method, system, equipment and medium for heat and mass transport in porous media according to the embodiments of the present application in conjunction with the accompanying drawings.
[0041] The cross-scale calculation method, system, equipment and medium of porous media heat and mass transport in this application solve the problem of incompatibility between accuracy and efficiency of existing porous media heat and mass transport calculation methods, and can achieve high-precision prediction of heat and mass transport processes at actual application scales.
[0042] Figure 1 FIG. 1 is a flow chart of a cross-scale calculation method for heat and mass transport in porous media according to an embodiment of the present application. Figure 1 As shown, the cross-scale calculation method for heat and mass transport in porous media according to an embodiment of the present application includes the following steps:
[0043] S101: Determine the porous structure, heat and mass transport physical processes, and solution conditions of porous media.
[0044] That is: determine the porous structure of the porous medium to be calculated, the physical process of heat and mass transport in the porous medium, and the solution conditions.
[0045] In a specific example, the physical processes typically include a series of physical processes related to heat and mass transport in porous media, including but not limited to: coupled heat and flow processes, single-phase and multiphase flow processes, pure heat conduction processes, steady-state processes, and transient processes. Furthermore, based on these physical processes, the effects of processes such as fluid-solid coupling, phase change, chemical reaction, and multi-component transport can be included.
[0046] In one embodiment of the present application, the solution conditions include but are not limited to initial value conditions and boundary conditions.
[0047] S102: Divide the porous structure into a plurality of macro units, assign initial values to the macro transport parameters, and repeat the following steps until the macro transport parameters converge.
[0048] Divide the porous structure into macro units, for example, divide the three-dimensional porous structure into N x ×N y ×N z Macro units, wherein the division method can be to divide the porous structure into macro units with equal intervals and uniformity, so as to divide the porous structure into multiple macro units. Wherein, x, y and z represent the x direction, y direction and z direction in three-dimensional coordinates respectively.
[0049] Assigning initial values to macroscopic transport parameters means assigning iterative initial values to macroscopic transport parameters, wherein the macroscopic transport parameters include but are not limited to absolute permeability, relative permeability, macroscopic capillary pressure, equivalent thermal conductivity, etc.
[0050] S103: Solve the macro-scale physical field.
[0051] In one embodiment of the present application, the macroscale includes a volume-average scale or a Darcy scale, and the macroscale physical field includes part or all of a saturation field, a volume-average velocity field, a volume-average pressure field, and a volume-average temperature field.
[0052] Solving the macro-scale physical field, for example, is to input the macro-transport parameters and the fixed solution conditions into a pre-trained macro-scale physical field solving machine learning agent model, and obtain the solution result of the macro-scale physical field output by the macro-scale physical field solving machine learning agent model. In other words, the solution of the macro-scale physical field can be obtained by using the developed and trained macro-scale physical field solving machine learning agent model. Of course, the solution of the macro-scale physical field can also be solved by using existing numerical simulation methods. For example, the macro-transport parameters and the fixed solution conditions can also be input into the macro-scale percolation solver, and the solution result of the macro-scale physical field output by the macro-scale percolation solver is obtained.
[0053] S104: Determine the pore-scale boundary conditions of each macro-unit according to the macro-scale physical field, and obtain the pore-scale physical field based on the pore-scale boundary conditions and porous structure of each macro-unit.
[0054] In one embodiment of the present application, determining the pore-scale boundary conditions of each of the macro-units according to the macro-scale physical field includes: interpolating the pore-scale boundary conditions of the macro-units according to the macro-scale physical field.
[0055] This involves performing a downscaling design and then determining the pore-scale boundary conditions for each macro-unit. For example, pore-scale boundary conditions can be interpolated from macro-scale physical fields. In this example, these can be calculated using a pore-scale machine learning proxy model for adjacent units, thereby improving the accuracy of the downscaling design.
[0056] Based on the pore-scale boundary conditions and porous structure of each of the macro units, a pore-scale physical field is obtained, including: traversing each of the macro units, calling a pre-trained pore-scale physical field solving machine learning agent model in each macro unit, and obtaining the pore-scale physical field through the pore-scale physical field solving machine learning agent model.
[0057] Specifically, the system loops through each macrocell and invokes a trained pore-scale physics solver, or machine learning proxy model, within each macrocell to obtain the pore-scale physics field. The inputs to this pore-scale physics solver include the porous structure within the macrocell, boundary conditions for heat and mass transport, and fluid properties. The output of this pore-scale physics solver is the pore-scale physics field corresponding to the heat and mass transport process.
[0058] In one embodiment of the present application, the pore-scale physical field includes but is not limited to: phase field, velocity field, pressure field, temperature field, and component concentration field.
[0059] S105: Update the macroscopic transport parameters according to the pore-scale physical field.
[0060] Updating the macroscopic transport parameters may include, for example, obtaining macroscopic transport parameters calculated in this round that are equivalent to the pore-scale physical field, and updating the macroscopic transport parameters based on the macroscopic transport parameters calculated in this round. In other words, upscaling calculations are performed to obtain macroscopic transport parameters equivalent to the pore-scale physical field.
[0061] In the above description, steps S103 to S105 are executed in an iterative loop. The iteration ends when the macroscopic transport parameters converge. Therefore, the deviation between the macroscopic transport parameters calculated in the current round and those in the previous round can be used to determine convergence. If the deviation is less than a predetermined threshold, the macroscopic transport parameters are considered converged. Specifically, whether the macroscopic transport parameters have converged is determined as follows: if the deviation between the macroscopic transport parameters calculated in the current round and those in the previous round is less than a predetermined threshold, then the cross-scale calculations for that time step have converged. Otherwise, the process returns to step S103 for the next iterative calculation, continuing until the macroscopic transport parameters converge. The computational process from steps S103 to S105 involves bidirectional information transfer between the global and local scales, characterizing the interaction between scales. Through coupled iterative calculations at the pore and macroscale levels, the equivalence of macroscale and pore-scale heat and mass transport processes is ensured.
[0062] According to the cross-scale calculation method for heat and mass transport in porous media of the embodiment of the present application, after determining the porous structure, heat and mass transport physical process and solution conditions of the porous medium, the porous structure is divided into multiple macro units, and the macro transport parameters are assigned initial values. Then, the macro scale physical field is solved, the pore scale boundary conditions of each macro unit are determined, the pore scale physical field is obtained, and the macro transport parameters are updated according to the pore scale physical field. When the macro transport parameters converge, the iteration is stopped, thereby completing the cross-scale calculation of heat and mass transport in porous media. The method has the advantages of high calculation accuracy and fast calculation speed for the heat and mass transport process of porous media, low memory usage, relatively low requirements for computing hardware, and a wide range of applications.
[0063] In a specific application, such as Figure 2 As shown, it shows the schematic diagram of the cross-scale calculation method of porous medium heat and mass transport in the embodiment of the present application, combined with Figure 3 The figure shows a schematic flow diagram of a cross-scale calculation method for heat and mass transport in porous media in a specific application. Figure 2 and Figure 3As shown in the figure, namely: determine the porous structure, physical process and boundary conditions to be calculated; divide the porous structure into macro units; assign iterative initial values to the macro transport parameters; solve the macro-scale physical field; downscale the design to determine the pore-scale boundary conditions of each macro unit; call the machine learning agent model to obtain the pore-scale physical field; upscale the calculation to obtain the macro transport parameters equivalent to the pore-scale physical field; judge whether the macro transport parameters converge. If so, end the iterative calculation, otherwise return to the step of solving the macro-scale physical field.
[0064] like Figure 3 As shown in the figure, for transient problems, it also includes the step of advancing to the next time step and returning to the macro-scale physical field solution step to perform coupled iterative calculations within the time step, and so on until the required time step is calculated.
[0065] The cross-scale calculation method for porous media heat and mass transport in the embodiments of this application achieves high computational accuracy. Through cross-scale coupled calculations, macroscopic transport process predictions are achieved, directly reflecting the influence of the intrinsic heat and mass transport processes of the fluid between pores. The computational accuracy exceeds that of existing macroscopic simulation methods based on empirical correlations. It also achieves high computational efficiency and low memory usage. By dividing large-scale porous structures into macroscopic units and coupling pore-scale and macroscopic calculations, the original large-scale fully coupled equations are equivalent to solving problems within multiple small macroscopic units, significantly reducing memory usage and improving the calculation speed of porous media heat and mass transport processes.
[0066] Figure 4 FIG. 1 is a block diagram of a cross-scale calculation system for heat and mass transport in porous media according to an embodiment of the present application. Figure 4 As shown, the porous media heat and mass transport cross-scale calculation system according to an embodiment of the present application includes: a determination module 410, a division module 420 and a calculation module 430, wherein:
[0067] Determination module 410, for determining the porous structure of the porous medium, the physical process of heat and mass transport, and the solution conditions;
[0068] A division module 420 is used to divide the porous structure into a plurality of macro units;
[0069] The calculation module 430 is used to assign initial values to the macroscopic transport parameters and repeatedly perform the following steps until the macroscopic transport parameters converge: solving the macroscopic scale physical field; determining the pore-scale boundary conditions of each macroscopic unit according to the macroscopic scale physical field, and obtaining the pore-scale physical field based on the pore-scale boundary conditions and porous structure of each macroscopic unit; and updating the macroscopic transport parameters according to the pore-scale physical field.
[0070] The determination module 410 determines the porous structure of the porous medium to be calculated, the physical process of heat and mass transport in the porous medium, and the solution conditions.
[0071] In a specific example, the physical processes typically include a series of physical processes related to heat and mass transport in porous media, including but not limited to: coupled heat and flow processes, single-phase and multiphase flow processes, pure heat conduction processes, steady-state processes, and transient processes. Furthermore, based on these physical processes, the effects of processes such as fluid-solid coupling, phase change, chemical reaction, and multi-component transport can be included.
[0072] In one embodiment of the present application, the solution conditions include but are not limited to initial value conditions and boundary conditions.
[0073] The division module 420 divides the porous structure into macro units, for example, dividing the three-dimensional porous structure into N x ×N y ×N z Macro units, wherein the division method can be to divide the porous structure into macro units with equal intervals and uniformity, so as to divide the porous structure into multiple macro units. Wherein, x, y and z represent the x direction, y direction and z direction in three-dimensional coordinates respectively.
[0074] The calculation module 430 assigns initial values to the macroscopic transport parameters, that is, assigns iterative initial values to the macroscopic transport parameters. The macroscopic transport parameters include but are not limited to absolute permeability, relative permeability, macroscopic capillary pressure, equivalent thermal conductivity, etc.
[0075] Solve macroscopic physics.
[0076] In one embodiment of the present application, the macroscale includes a volume-average scale or a Darcy scale, and the macroscale physical field includes part or all of a saturation field, a volume-average velocity field, a volume-average pressure field, and a volume-average temperature field.
[0077] Solving the macro-scale physical field, for example, is to input the macro-transport parameters and the fixed solution conditions into a pre-trained macro-scale physical field solving machine learning agent model, and obtain the solution result of the macro-scale physical field output by the macro-scale physical field solving machine learning agent model. In other words, the solution of the macro-scale physical field can be obtained by using the developed and trained macro-scale physical field solving machine learning agent model. Of course, the solution of the macro-scale physical field can also be solved by using existing numerical simulation methods. For example, the macro-transport parameters and the fixed solution conditions can also be input into the macro-scale percolation solver, and the solution result of the macro-scale physical field output by the macro-scale percolation solver is obtained.
[0078] In one embodiment of the present application, determining the pore-scale boundary conditions of each of the macro-units according to the macro-scale physical field includes: interpolating the pore-scale boundary conditions of the macro-units according to the macro-scale physical field.
[0079] This involves performing a downscaling design and then determining the pore-scale boundary conditions for each macro-unit. For example, pore-scale boundary conditions can be interpolated from macro-scale physical fields. In this example, these can be calculated using a pore-scale machine learning proxy model for adjacent units, thereby improving the accuracy of the downscaling design.
[0080] Based on the pore-scale boundary conditions and porous structure of each of the macro units, a pore-scale physical field is obtained, including: traversing each of the macro units, calling a pre-trained pore-scale physical field solving machine learning agent model in each macro unit, and obtaining the pore-scale physical field through the pore-scale physical field solving machine learning agent model.
[0081] Specifically, the system loops through each macrocell and invokes a trained pore-scale physics solver, or machine learning proxy model, within each macrocell to obtain the pore-scale physics field. The inputs to this pore-scale physics solver include the porous structure within the macrocell, boundary conditions for heat and mass transport, and fluid properties. The output of this pore-scale physics solver is the pore-scale physics field corresponding to the heat and mass transport process.
[0082] In one embodiment of the present application, the pore-scale physical field includes but is not limited to: phase field, velocity field, pressure field, temperature field, and component concentration field.
[0083] Updating the macroscopic transport parameters may include, for example, obtaining macroscopic transport parameters calculated in this round that are equivalent to the pore-scale physical field, and updating the macroscopic transport parameters based on the macroscopic transport parameters calculated in this round. In other words, upscaling calculations are performed to obtain macroscopic transport parameters equivalent to the pore-scale physical field.
[0084] In the above description, the steps of solving the macroscale physical field and updating the macroscopic transport parameters based on the pore-scale physical field are performed in an iterative loop. The iterative loop ends when the macroscopic transport parameters converge. Therefore, the deviation between the macroscopic transport parameters calculated in the current round and those in the previous round can be used to determine convergence. If the deviation is less than a predetermined threshold, the macroscopic transport parameters are considered converged. Specifically, whether the macroscopic transport parameters have converged is determined as follows: if the deviation between the macroscopic transport parameters calculated in the current round and those in the previous round is less than a predetermined threshold, then the cross-scale calculations for that time step have converged. Otherwise, the process returns to solving the macroscale physical field and performs the next iterative calculation until the macroscopic transport parameters converge. The iterative calculation process from solving the macroscale physical field to updating the macroscopic transport parameters based on the pore-scale physical field involves bidirectional information transfer between the global and local scales, characterizing the interaction between scales. Through coupled iterative calculations at the pore-scale and macroscale levels, the equivalence of macroscale and pore-scale heat and mass transport processes is ensured.
[0085] According to the porous media heat and mass transport cross-scale calculation system of the embodiment of the present application, after determining the porous structure, heat and mass transport physical process and solution conditions of the porous medium, the porous structure is divided into multiple macro units, and the macro transport parameters are assigned initial values. Then, the macro scale physical field is solved, the pore scale boundary conditions of each macro unit are determined, the pore scale physical field is obtained, and the macro transport parameters are updated according to the pore scale physical field. When the macro transport parameters converge, the iteration is stopped, thereby completing the porous media heat and mass transport cross-scale calculation. It has the advantages of high calculation accuracy and fast calculation speed for the porous media heat and mass transport process, low memory usage, relatively low requirements for computing hardware, and a wide range of applications.
[0086] The specific limitations of the porous media heat and mass transport cross-scale calculation system can be found in the limitations of the porous media heat and mass transport cross-scale calculation method described above and will not be repeated here. The various modules of the porous media heat and mass transport cross-scale calculation system can be implemented in whole or in part through software, hardware, or a combination thereof. The modules can be embedded in or independent of a processor in a computer device in the form of hardware, or stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the modules.
[0087] In one embodiment, a computer device is provided. Figure 5This is a block diagram of the computer device provided in the embodiments of the present application. The computer device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, it implements the aforementioned embodiment of the cross-scale calculation method for heat and mass transport in porous media. For example, the computer device performs the following operations: determining the porous structure of the porous media, the physical processes of heat and mass transport, and the solution conditions;
[0088] Dividing the porous structure into a plurality of macro units, assigning initial values to macro transport parameters, and repeatedly performing the following steps until the macro transport parameters converge;
[0089] Solve macro-scale physical fields;
[0090] Determining a pore-scale boundary condition of each macro-unit according to the macro-scale physical field, and obtaining a pore-scale physical field based on the pore-scale boundary condition and porous structure of each macro-unit;
[0091] The macroscopic transport parameters are updated according to the pore-scale physical field.
[0092] The present application also provides a computer-readable storage medium storing a computer program. When a processor executes the computer program, the aforementioned method for calculating cross-scale heat and mass transport in porous media is implemented. For example, the method may include determining the porous structure of the porous media, the physical process of heat and mass transport, and the solution conditions.
[0093] Dividing the porous structure into a plurality of macro units, assigning initial values to macro transport parameters, and repeatedly performing the following steps until the macro transport parameters converge;
[0094] Solve macro-scale physical fields;
[0095] Determining a pore-scale boundary condition of each macro-unit according to the macro-scale physical field, and obtaining a pore-scale physical field based on the pore-scale boundary condition and porous structure of each macro-unit;
[0096] The macroscopic transport parameters are updated according to the pore-scale physical field.
[0097] The present application embodiment provides a computer program product, which includes instructions. When the instructions are executed, the method described in the embodiment of the present application is executed. For example, you can execute Figure 1 The various steps of the cross-scale calculation method of heat and mass transport in porous media shown, such as performing: determining the porous structure of the porous media, the physical process of heat and mass transport and the solution conditions;
[0098] Dividing the porous structure into a plurality of macro units, assigning initial values to macro transport parameters, and repeatedly performing the following steps until the macro transport parameters converge;
[0099] Solve macro-scale physical fields;
[0100] Determining a pore-scale boundary condition of each macro-unit according to the macro-scale physical field, and obtaining a pore-scale physical field based on the pore-scale boundary condition and porous structure of each macro-unit;
[0101] The macroscopic transport parameters are updated according to the pore-scale physical field.
[0102] Those skilled in the art will appreciate that all or part of the processes in the methods for implementing the above embodiments can be accomplished by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include processes of the embodiments of the above methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).
[0103] The technical features of the above embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0104] The above embodiments merely illustrate several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, all of which fall within the scope of protection of the present application. Therefore, the scope of protection of the present patent application shall be determined by the appended claims.
Claims
1. A cross-scale calculation method for heat and mass transport in porous media, characterized by: include: Determine the porous structure, heat and mass transport physical processes and solution conditions of porous media; Dividing the porous structure into a plurality of macro units, assigning initial values to macro transport parameters, and repeatedly performing the following steps until the macro transport parameters converge; Solve macro-scale physical fields; Determining a pore-scale boundary condition of each macro-unit according to the macro-scale physical field, and obtaining a pore-scale physical field based on the pore-scale boundary condition and porous structure of each macro-unit; The macroscopic transport parameters are updated according to the pore-scale physical field.
2. The cross-scale calculation method for heat and mass transport in porous media according to claim 1, characterized in that: The step of dividing the porous structure into a plurality of macro units comprises: The porous structure is divided into macro units at equal intervals and uniformly, so as to divide the porous structure into a plurality of macro units.
3. The cross-scale calculation method for heat and mass transport in porous media according to claim 1, characterized in that: The macroscopic scale includes a volume average scale or a Darcy scale, and the macroscopic scale physical field includes part or all of a saturation field, a volume average velocity field, a volume average pressure field, and a volume average temperature field; The solving of the macro-scale physical field includes: inputting the macro-transport parameters and the fixed solution conditions into a macro-scale percolation solver or a pre-trained macro-scale physical field solving machine learning agent model, and obtaining the solution result of the macro-scale physical field output by the macro-scale percolation solver or the macro-scale physical field solving machine learning agent model.
4. The cross-scale calculation method for heat and mass transport in porous media according to claim 1, characterized in that: Determining the pore-scale boundary conditions of each macro unit according to the macro-scale physical field includes: According to the macro-scale physical field, the pore-scale boundary conditions of the macro unit are interpolated.
5. The cross-scale calculation method for heat and mass transport in porous media according to claim 1, characterized in that: The step of obtaining a pore-scale physical field based on the pore-scale boundary conditions and porous structure of each macro unit comprises: Each of the macro units is traversed, and a pre-trained pore-scale physical field solving machine learning proxy model is called in each macro unit to obtain the pore-scale physical field through the pore-scale physical field solving machine learning proxy model.
6. The cross-scale calculation method for heat and mass transport in porous media according to any one of claims 1 to 5, characterized in that: The updating of the macroscopic transport parameters according to the pore-scale physical field comprises: Obtain the macroscopic transport parameters calculated in this round that are equivalent to the pore-scale physical field, and update the macroscopic transport parameters according to the macroscopic transport parameters calculated in this round.
7. The cross-scale calculation method for heat and mass transport in porous media according to claim 1, characterized in that: Also includes: Obtain the deviation between the macroscopic transport parameters calculated in this round and those calculated in the previous round; If the deviation value is smaller than a predetermined threshold, it is determined that the macroscopic transport parameter converges.
8. A cross-scale calculation system for heat and mass transport in porous media, characterized by: include: Determination module, used to determine the porous structure of porous media, physical processes of heat and mass transport, and solution conditions; A division module, used for dividing the porous structure into a plurality of macro units; A calculation module is used to assign initial values to macroscopic transport parameters and repeatedly perform the following steps until the macroscopic transport parameters converge: solving the macroscopic scale physical field; determining the pore-scale boundary conditions of each macroscopic unit according to the macroscopic scale physical field, and obtaining the pore-scale physical field based on the pore-scale boundary conditions and porous structure of each macroscopic unit; and updating the macroscopic transport parameters according to the pore-scale physical field.
9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the cross-scale calculation method for heat and mass transport in porous media according to any one of claims 1 to 7 is implemented.
10. A computer-readable storage medium comprising a memory and a computer program stored in the memory and executable on a processor, characterized in that: When the program is executed by a processor, the cross-scale calculation method for heat and mass transport in porous media according to any one of claims 1 to 7 is implemented.