Simulation analysis method and device of porous structure, storage medium and computer equipment

Abstract

The embodiment of the invention provides a simulation analysis method and device of a porous structure, a storage medium and computer equipment. The method comprises the following steps: carrying out image processing on an obtained original porous structure image to generate a porous structure image; performing three-dimensional reconstruction on the porous structure image to generate a structure model; performing grid processing on the structure model to generate a grid model; performing multi-physical field simulation according to the grid model to generate simulation data, the simulation data including pressure gradient, effective conductivity, heat flux density and permeability; and calculating the simulation data based on a genetic algorithm to generate a simulation analysis result. In the technical scheme provided by the embodiment of the invention, the simulation data is generated by performing multi-physical field simulation on the grid model, and the simulation analysis result is generated by calculating the simulation data based on the genetic algorithm, so that the simulation precision and the reliability of the simulation analysis result of the porous structure are improved.

Description

Technical Field

[0001] The present invention relates to the field of simulation technology, and in particular to a simulation analysis method, device, storage medium and computer device for a porous structure. Background Art

[0002] Due to its good mechanical strength, electrical conductivity and corrosion resistance, porous titanium felt is widely used as a current collector or support in fuel cells, electrolyzers and electrochemical reactors. The interior of the porous titanium felt is an irregular and interlaced fiber structure with a high porosity and strong fluidity, which has a decisive influence on the performance of the device. The modeling methods in the related art usually adopt regular and simplified structures, which cannot accurately describe the true microstructure of the porous titanium felt, resulting in a large gap between the simulation results and the experiments, low simulation accuracy and low reliability of the simulation analysis results. Summary of the Invention

[0003] In view of this, embodiments of the present invention provide a simulation analysis method, device, storage medium and computer device for a porous structure, so as to improve the simulation accuracy and the reliability of the simulation analysis results of the porous structure.

[0004] On the one hand, an embodiment of the present invention provides a simulation analysis method for a porous structure, including: Performing image processing on the obtained original porous structure image to generate a porous structure image; Performing three-dimensional reconstruction on the porous structure image to generate a structure model; Performing mesh processing on the structure model to generate a mesh model; Performing multi-physics field simulation according to the mesh model to generate simulation data, where the simulation data includes pressure gradient, effective conductivity, heat flux density and permeability; Calculating the simulation data based on a genetic algorithm to generate a simulation analysis result.

[0005] Optionally, the multi-physics field simulation includes gas flow simulation, the simulation data includes pressure gradient, and performing multi-physics field simulation according to the mesh model to generate simulation data includes: Generating a porosity according to the pore volume and the total material volume of the obtained porous structure; Generating an effective viscosity of the medium in the porous structure according to the porosity and the obtained gas dynamic viscosity and average particle diameter; Generating the pressure gradient according to the effective viscosity and the obtained gas velocity field.

[0006] Optionally, the multi-physics field simulation includes conductivity simulation, the simulation data includes effective conductivity, and performing multi-physics field simulation according to the mesh model to generate simulation data includes: The effective conductivity is generated based on the porosity and the conductivity of the obtained porous structure material itself.

[0007] Optionally, the multi-physics simulation includes a permeability simulation, and the simulation data includes permeability. The multi-physics simulation based on the grid model to generate simulation data includes: The permeability is generated based on the porosity, the particle diameter, and the specific surface area of the obtained porous structure.

[0008] Optionally, the multi-physics simulation includes a heat conduction simulation, and the simulation data includes heat flux density. The multi-physics simulation based on the grid model to generate simulation data includes: The heat flux density is generated based on the thermal conductivity and the temperature gradient of the obtained porous structure.

[0009] Optionally, calculating the simulation data based on the genetic algorithm to generate a simulation analysis result includes: Calculating the pressure gradient, the effective conductivity, the heat flux density, the permeability, the set target pressure gradient, target effective conductivity, target heat flux density, and target permeability based on the genetic algorithm to generate a simulation analysis result.

[0010] Optionally, calculating the pressure gradient, the effective conductivity, the heat flux density, the permeability, the set target pressure gradient, target effective conductivity, target heat flux density, and target permeability based on the genetic algorithm to generate a simulation analysis result includes: Constructing a fitness function according to the pressure gradient, the effective conductivity, the heat flux density, the permeability, the set target pressure gradient, target effective conductivity, target heat flux density, target permeability, and the weight coefficient; Calculating the fitness function according to the genetic algorithm to generate a fitness function value; When the fitness function value is less than or equal to a set threshold, the performance of the porous structure is evaluated as good; or, When the fitness function value is greater than the set threshold, adjusting the structural parameters of the structure model, and continuing to execute the step of performing grid processing on the structure model to generate a grid model; Wherein, the structural parameters include one or any combination of the particle diameter of the porous structure, the gas dynamic viscosity, the specific surface area, the porosity, and / or the heat flux density.

[0011] On the other hand, an embodiment of the present invention provides a simulation analysis device for a porous structure, including: A first generation module, configured to perform image processing on the obtained original porous structure image to generate a porous structure image; A second generation module for performing three-dimensional reconstruction on the porous structure image to generate a structure model; A third generation module for performing mesh processing on the structure model to generate a mesh model; A fourth generation module for performing multi-physical field simulation based on the mesh model to generate simulation data, where the simulation data includes pressure gradient, effective conductivity, heat flux density, and permeability; A fifth generation module for calculating the simulation data based on a genetic algorithm to generate a simulation analysis result.

[0012] On the other hand, an embodiment of the present invention provides a storage medium, where the storage medium includes a stored program, and when the program runs, it controls the device where the storage medium is located to execute the above-mentioned simulation analysis method for the porous structure.

[0013] On the other hand, an embodiment of the present invention provides a computer device, including a memory and a processor, where the memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions. When the program instructions are loaded and executed by the processor, the steps of the above-mentioned simulation analysis method for the porous structure are implemented.

[0014] In the technical solution provided by the embodiment of the present invention, image processing is performed on the acquired original porous structure image to generate a porous structure image; three-dimensional reconstruction is performed on the porous structure image to generate a structure model; mesh processing is performed on the structure model to generate a mesh model; multi-physical field simulation is performed based on the mesh model to generate simulation data, where the simulation data includes pressure gradient, effective conductivity, heat flux density, and permeability; calculation is performed on the simulation data based on a genetic algorithm to generate a simulation analysis result. In the technical solution provided by the embodiment of the present invention, by performing multi-physical field simulation on the mesh model to generate simulation data and calculating the simulation data based on a genetic algorithm to generate a simulation analysis result, the simulation accuracy and the reliability of the simulation analysis result of the porous structure are improved. Description of the Drawings

[0015] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the drawings required to be used in the embodiment will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative labor.

[0016] Figure 1 It is a flowchart of a simulation analysis method for a porous structure provided by an embodiment of the present invention; Figure 2 It is a schematic diagram of an original porous structure image provided by an embodiment of the present invention; Figure 3 Schematic diagram of a structural model provided by an embodiment of the present invention; Figure 4 Schematic diagram of a grid model provided by an embodiment of the present invention; Figure 5 Flow chart of multi-physics field simulation based on a grid model to generate simulation data provided by an embodiment of the present invention; Figure 6 Schematic diagram of a pressure gradient provided by an embodiment of the present invention; Figure 7 Schematic diagram of a simulation analysis device for a porous structure provided by an embodiment of the present invention; Figure 8 Schematic diagram of a computer device provided by an embodiment of the present invention. Detailed implementation manners

[0017] For a better understanding of the technical solutions of the present invention, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0018] It should be clear that the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0019] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms of "a", "the" and "said" used in the embodiments of the present invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0020] It should be understood that the term " / and" used herein is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, a and / or b may represent: a exists alone, a and b exist simultaneously, and b exists alone. In addition, the character " / " herein generally represents an "or" relationship between the front and rear associated objects.

[0021] In related technologies, common modeling methods use idealized or regular structures (such as honeycombs, sphere packings, etc.) to replace the real structure of the porous structure, resulting in inaccurate reflection of pore distribution, fiber orientation, current path, etc. And there is a lack of a unified modeling process and it cannot be applied to multiple simulation platforms. There is a lack of a method for adjusting the structural parameters of the porous structure, which cannot support the structural optimization design, and the modeling flexibility is poor. Due to inaccurate structural expression, the calculated results of the physical properties of the porous structure are quite different from the experimental values, affecting the engineering guiding value, resulting in low simulation accuracy and low reliability of the simulation analysis results.

[0022] To solve the technical problems in the related art, an embodiment of the present invention provides a simulation analysis method for a porous structure. Figure 1 The flowchart of a simulation analysis method for a porous structure provided by an embodiment of the present invention is as Figure 1 shown, and the method includes: Step 102, perform image processing on the obtained original porous structure image to generate a porous structure image.

[0023] In the embodiment of the present invention, each step is executed by a computer device. For example, the computer device includes a computer, a tablet computer, a server, or a simulation device.

[0024] In the embodiment of the present invention, the porous structure includes a porous titanium felt, and in the embodiment of the present invention, the titanium felt for the anode of a proton exchange membrane fuel cell is taken as an example for description.

[0025] In the embodiment of the present invention, a scanning electron microscope (SEM) can be used to obtain a microscopic image of the porous structure (porous titanium felt), that is, the original porous structure image. Figure 2 The schematic diagram of an original porous structure image provided by an embodiment of the present invention.

[0026] Specifically, perform image processing on the obtained original porous structure image. The image processing includes image enhancement, filtering, and binarization processing to generate a porous structure image.

[0027] Step 104, perform three-dimensional reconstruction on the porous structure image to generate a structure model.

[0028] In the embodiment of the present invention, three-dimensional reconstruction software (such as Avizo) can be used to perform three-dimensional reconstruction on the porous structure image to generate a structure model. This structure model is the true structure model of the porous structure. Figure 3 The schematic diagram of a structure model provided by an embodiment of the present invention.

[0029] Step 106, perform mesh processing on the structure model to generate a mesh model.

[0030] In the embodiment of the present invention, preprocessing such as repairing the structure model can be performed to eliminate unclear structures such as burrs in the structure model and output it in the STL format or other formats compatible with simulation software.

[0031] In the embodiment of the present invention, the structure model can be imported into mesh modeling software (such as ANSYS), and unstructured tetrahedral meshes are used for mesh processing according to the structural complexity, and local encryption is performed to generate a mesh model. Figure 4Schematic diagram of a grid model provided by an embodiment of the present invention.

[0032] Among them, due to the irregularity and very complex pore structure of the porous structure, using unstructured tetrahedral meshes has the advantages of being easy to generate automatically and adapting to complex curved surfaces. Local refinement means using smaller and denser meshes in certain key areas (rather than the whole) of the structural model to improve the simulation accuracy, keep the overall number of meshes small, and save computing resources.

[0033] Step 108: Perform multi-physics field simulation based on the grid model to generate simulation data, where the simulation data includes pressure gradient, effective conductivity, heat flux density, and permeability.

[0034] In the embodiment of the present invention, the multi-physics field simulation includes: gas flow simulation, conductivity simulation, heat conduction simulation, and permeability simulation.

[0035] Gas flow simulation: Use a gas flow model based on Darcy's law to calculate the flow behavior of gas in the porous structure (porous titanium felt) through the pressure gradient. Under low Reynolds number flow conditions, it can accurately predict the velocity field and pressure field of gas flow, ensuring that the simulation data is consistent with the actual gas flow situation.

[0036] Conductivity simulation: Use the Effective Medium Theory (EMT) to simulate the conductivity in the porous structure (porous titanium felt), consider the relationship between different porosities and material conductivities, and can accurately calculate the conductance characteristics of the porous structure (porous titanium felt) under the action of an electric field, supporting the performance evaluation in electrochemical applications.

[0037] Heat conduction simulation: Simulate the heat conduction behavior in the porous structure (porous titanium felt) material through Fourier's law, and calculate the distribution of heat flux density and temperature gradient. In multi-physics field simulation, accurate modeling of heat conduction is crucial for optimizing the temperature control performance of devices, especially in thermal management of fuel cells and battery systems.

[0038] Permeability simulation: Use models such as the Kozeny-Carman equation to evaluate the permeability of the porous structure (porous titanium felt) and reveal the flow efficiency of gas or fluid in the porous medium. This simulation data can provide an important theoretical basis for the structural optimization of the porous structure (porous titanium felt) to ensure its efficient performance in practical applications.

[0039] Figure 5 Flowchart of multi-physics field simulation based on a grid model to generate simulation data provided by an embodiment of the present invention, as Figure 5 shown, step 108 includes: Step 1082: Generate a porosity based on the pore volume and total material volume of the porous structure obtained.

[0040] In the embodiments of the present invention, the modeling of gas flow in the medium of the porous structure generally adopts Darcy's law (for low Reynolds number flows), and for complex porous structures, it may be necessary to be corrected by the Brinkman model.

[0041] In the embodiments of the present invention, the pore volume and total material volume of the porous structure can be obtained from the grid model.

[0042] Specifically, through the formula calculate the pore volume and total material volume of the porous structure to generate a porosity. Wherein, is the porosity, is the pore volume, is the total material volume.

[0043] Step 1084: Generate the effective viscosity of the medium in the porous structure based on the porosity and the obtained gas dynamic viscosity and average particle diameter.

[0044] In the embodiments of the present invention, the gas dynamic viscosity and average particle diameter can be obtained from the grid model.

[0045] Specifically, through the formula calculate the porosity, gas dynamic viscosity, and average particle diameter to generate the effective viscosity of the medium in the porous structure. Wherein, is the porosity, is the gas dynamic viscosity (Pa·s), is the average particle diameter, is the effective viscosity of the medium in the porous structure.

[0046] Step 1086: Generate a pressure gradient based on the effective viscosity and the obtained gas velocity field.

[0047] In the embodiments of the present invention, the gas velocity field can be obtained from the grid model.

[0048] Specifically, through the formula calculate the effective viscosity and gas velocity field to generate a pressure gradient. Wherein, is the effective viscosity, is the gas velocity field (m / s), is the pressure gradient (Pa / m), Figure 6 FIG. is a schematic diagram of a pressure gradient provided by an embodiment of the present invention. The smaller the pressure gradient, the smaller the flow resistance.

[0049] Step 1088: Generate the effective conductivity based on the porosity and the conductivity of the porous structure material itself.

[0050] In the embodiments of the present invention, in the calculation of the effective conductivity, an effective conductivity model (based on the conductivity, porosity, and structure of different materials) is usually used, and a random network model or effective medium theory can be adopted to characterize it.

[0051] In the embodiments of the present invention, the conductivity of the porous structure material itself can be obtained from the grid model.

[0052] Specifically, through the formula calculate the porosity and the conductivity of the porous structure material itself to generate the effective conductivity. Among them, is the effective conductivity (S / m), is the conductivity of the porous structure material itself (S / m), is the porosity. The larger the effective conductivity, the better the electron / ion conduction performance of the porous structure.

[0053] Step 1090: Generate the permeability based on the porosity, the particle diameter, and the specific surface area of the obtained porous structure.

[0054] In the embodiments of the present invention, the particle diameter and the specific surface area of the porous structure can be obtained from the grid model.

[0055] Specifically, there is a certain relationship between the permeability and the porosity, which is usually expressed by the Kozeny-Carman equation, that is, the formula can be used to calculate the porosity, the particle diameter of the porous structure, and the specific surface area to generate the permeability. Among them, is the permeability (m²), is the particle diameter (m), is the porosity, is the specific surface area (m² / m³). The larger the permeability, the better the penetration effect.

[0056] Step 1092: Generate the heat flux density based on the thermal conductivity and the temperature gradient of the obtained porous structure.

[0057] In the embodiments of the present invention, the thermal conductivity and the temperature gradient of the porous structure can be obtained from the grid model.

[0058] Specifically, the energy transfer in the heat conduction process follows Fourier's law, and the formula is used to calculate the thermal conductivity and the temperature gradient of the porous structure to generate the heat flux density. Among them, is the heat flux density (W / m²), is the thermal conductivity (W / m·K), is the temperature gradient (K / m). The greater the heat flux density means the higher efficiency of the material's heat dissipation ability.

[0059] Step 110: Calculate the simulation data based on the genetic algorithm to generate the simulation analysis result.

[0060] In the embodiment of the present invention, by establishing a performance-structure parameter database and combining with the genetic algorithm, the fiber structure parameters are adjusted in reverse to generate a new generation of structure samples for quickly evaluating and selecting the best design scheme.

[0061] In the embodiment of the present invention, the pressure gradient, effective conductivity, heat flux density, permeability, set target pressure gradient, target effective conductivity, target heat flux density, and target permeability can be calculated based on the genetic algorithm to generate the simulation analysis result.

[0062] Specifically, step 110 may include: Step S1: Construct a fitness function according to the pressure gradient, effective conductivity, heat flux density, permeability, set target pressure gradient, target effective conductivity, target heat flux density, target permeability, and weight coefficients.

[0063] In the embodiment of the present invention, when performing structure optimization, a fitness function can be used to evaluate the performance of different structures. Assuming the goal is to optimize the balance of permeability and conductivity, the fitness function can be defined as: , where, is the pressure gradient, is the target pressure gradient, is the effective conductivity, is the target effective conductivity, is the heat flux density, is the target heat flux density, is the permeability, is the target permeability, is the weight coefficient, and the value range of each weight coefficient is 0.2~0.5, and .

[0064] In the embodiment of the present invention, the target pressure gradient, target effective conductivity, target heat flux density, and target permeability can be set according to the actual situation. For example, the value range of the target pressure gradient is 1000~10000 Pa / m, the target effective conductivity is 1000 S / m, the value range of the target heat flux density is 5000~20000 W / m 2 , the value range of the target permeability is 10 -13 ~10 -11 m 2 .

[0065] Step S2: Calculate the fitness function according to the genetic algorithm to generate fitness function values.

[0066] In the embodiment of the present invention, the genetic algorithm generates a batch of candidate structures in each generation, calculates the fitness function value for each structure, preferentially retains the individuals with low fitness function values (better target performance), generates new candidate structures, and finally finds the optimal or near-optimal porous structure through iterative evolution, making the fitness function value the lowest.

[0067] Step S3: When the fitness function value is less than or equal to the set threshold, evaluate the performance of the porous structure as good.

[0068] In the embodiment of the present invention, the set threshold can be set according to the actual situation. For example, the set threshold is 0.3.

[0069] Step S4: When the fitness function value is greater than the set threshold, adjust the structural parameters of the structure model and continue to execute step 106. Among them, the structural parameters include one or any combination of the particle diameter, gas dynamic viscosity, specific surface area, porosity, and / or heat flux density of the porous structure.

[0070] In the embodiment of the present invention, the above optimization is for the fluid structure. In fact, the porous structure also needs to consider its structural properties such as supportability and strength. Therefore, it is necessary to comprehensively consider how to adjust the structural parameters of the structure model.

[0071] In the technical solution provided by the embodiment of the present invention, taking the porous titanium felt for the anode of a proton exchange membrane fuel cell as an example, the random fiber generation method is adopted, the fiber diameter is set to 5 μm, the length is 400 μm, and the orientation is random, to generate a three-dimensional structure model with a porosity of about 75%. It is imported into the fluid simulation software (FLUENT), the inlet flow rate is set to 0.5 m / s and the outlet is at atmospheric pressure, and the average pressure drop and permeability are calculated. The porous structure is imported into the electric field module, a voltage difference of 2V is applied to the upper and lower surfaces, and the equivalent volume conductivity is calculated. By adjusting the fiber density and orientation angle, the optimal balance point between permeability and conductivity is obtained.

[0072] In the technical solution provided by the embodiment of the present invention, image processing is performed on the acquired original porous structure image to generate a porous structure image; three-dimensional reconstruction is performed on the porous structure image to generate a structure model; mesh processing is performed on the structure model to generate a mesh model; multi-physics field simulation is performed based on the mesh model to generate simulation data, where the simulation data includes pressure gradient, effective conductivity, heat flux density, and permeability; calculation is performed on the simulation data based on the genetic algorithm to generate a simulation analysis result. In the technical solution provided by the embodiment of the present invention, by performing multi-physics field simulation on the mesh model to generate simulation data and calculating the simulation data based on the genetic algorithm to generate a simulation analysis result, the simulation accuracy and the reliability of the simulation analysis result of the porous structure are improved.

[0073] In the technical solution provided by the embodiment of the present invention, a modeling method for a porous structure that supports the reconstruction of a real original porous structure image and the generation of controllable parameters is established, forming a general process that can be widely adapted to a variety of multi-physics field simulation platforms, realizing an accurate mapping between the micro-structure and performance, improving the reliability of simulation prediction, supporting reverse optimization design of the structure, and providing support for improving the performance of devices.

[0074] In the technical solution provided by the embodiment of the present invention, a method for structure modeling that supports both real structures and controllable parameters is provided, taking into account simulation accuracy and flexibility. The constructed three-dimensional structure model can truly reflect the micro-characteristics of the porous titanium felt, improving the reliability of simulation analysis, having strong multi-physics field adaptation ability, being applicable to structure-performance correlation analysis and reverse optimization design, and can be widely used in the research of structure optimization, performance prediction, and fault diagnosis of titanium felt-related electrochemical systems.

[0075] An embodiment of the present invention provides a simulation analysis device for a porous structure. Figure 7 As shown in the schematic diagram of a simulation analysis device for a porous structure provided by an embodiment of the present invention, Figure 7 the device includes: a first generation module 11, a second generation module 12, a third generation module 13, a fourth generation module 14, and a fifth generation module 15.

[0076] The first generation module 11 is used to perform image processing on the acquired original porous structure image to generate a porous structure image.

[0077] The second generation module 12 is used to perform three-dimensional reconstruction on the porous structure image to generate a structure model.

[0078] The third generation module 13 is used to perform mesh processing on the structure model to generate a mesh model.

[0079] The fourth generation module 14 is used to perform multi-physics field simulation based on the mesh model to generate simulation data, where the simulation data includes pressure gradient, effective conductivity, heat flux density, and permeability.

[0080] The fifth generation module 15 is used to calculate the simulation data based on a genetic algorithm and generate a simulation analysis result.

[0081] In an embodiment of the present invention, the multi-physical field simulation includes a gas flow simulation, and the simulation data includes a pressure gradient. The fourth generation module 14 is specifically configured to: Generate a porosity according to the pore volume and the total material volume of the obtained porous structure; Generate an effective viscosity of the medium in the porous structure according to the porosity, the obtained gas dynamic viscosity, and the average particle diameter; Generate the pressure gradient according to the effective viscosity and the obtained gas velocity field.

[0082] In an embodiment of the present invention, the multi-physical field simulation includes a conductivity simulation, and the simulation data includes an effective conductivity. The fourth generation module 14 is specifically configured to: generate the effective conductivity according to the porosity and the conductivity of the porous structure material itself obtained.

[0083] In an embodiment of the present invention, the multi-physical field simulation includes a permeability simulation, and the simulation data includes a permeability. The fourth generation module 14 is specifically configured to: generate the permeability according to the porosity, the obtained particle diameter, and the specific surface area of the porous structure.

[0084] In an embodiment of the present invention, the multi-physical field simulation includes a heat conduction simulation, and the simulation data includes a heat flux density. The fourth generation module 14 is specifically configured to: generate the heat flux density according to the obtained thermal conductivity and temperature gradient of the porous structure.

[0085] In an embodiment of the present invention, the fifth generation module 15 is specifically configured to: calculate the pressure gradient, the effective conductivity, the heat flux density, the permeability, the set target pressure gradient, target effective conductivity, target heat flux density, and target permeability based on a genetic algorithm to generate a simulation analysis result.

[0086] In an embodiment of the present invention, the fifth generation module 15 is specifically configured to: Construct a fitness function according to the pressure gradient, the effective conductivity, the heat flux density, the permeability, the set target pressure gradient, target effective conductivity, target heat flux density, target permeability, and a weight coefficient; Calculate the fitness function according to the genetic algorithm to generate a fitness function value; When the fitness function value is less than or equal to a set threshold, evaluate the performance of the porous structure as good; or, When the fitness function value is greater than the set threshold, adjust the structural parameters of the structural model, and continue to execute the step of performing mesh processing on the structural model to generate a mesh model; Among them, the structural parameters include one or any combination of the particle diameter of the porous structure, the gas dynamic viscosity, the specific surface area, the porosity, and / or the heat flux density.

[0087] In the technical solution provided by the embodiment of the present invention, image processing is performed on the obtained original porous structure image to generate a porous structure image; three-dimensional reconstruction is performed on the porous structure image to generate a structural model; mesh processing is performed on the structural model to generate a mesh model; multi-physical field simulation is performed according to the mesh model to generate simulation data, where the simulation data includes a pressure gradient, an effective conductivity, a heat flux density, and a permeability; based on a genetic algorithm, calculations are performed on the simulation data to generate a simulation analysis result. In the technical solution provided by the embodiment of the present invention, by performing multi-physical field simulation on the mesh model to generate simulation data and performing calculations on the simulation data based on a genetic algorithm to generate a simulation analysis result, the simulation accuracy and the reliability of the simulation analysis result of the porous structure are improved.

[0088] The simulation analysis device for a porous structure provided by the embodiment of the present invention can be used to implement the above Figure 1 simulation analysis method for a porous structure. For specific descriptions, reference can be made to the embodiments of the simulation analysis method for a porous structure above, and details will not be repeated here.

[0089] The embodiment of the present invention provides a storage medium, which includes a stored program. When the program runs, it controls the device where the storage medium is located to execute the steps of the embodiments of the above simulation analysis method for a porous structure. For specific descriptions, reference can be made to the embodiments of the simulation analysis method for a porous structure above.

[0090] The embodiment of the present invention provides a computer device, which includes a memory and a processor. The memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions. When the program instructions are loaded and executed by the processor, the steps of the embodiments of the above simulation analysis method for a porous structure are implemented. For specific descriptions, reference can be made to the embodiments of the simulation analysis method for a porous structure above.

[0091] Figure 8 A schematic diagram of a computer device provided by the embodiment of the present invention. As Figure 8As shown, the computer device 20 of this embodiment includes: a processor 21, a memory 22, and a computer program 23 stored in the memory 22 and executable on the processor 21. When the computer program 23 is executed by the processor 21, it implements the simulation analysis method applied to the porous structure in the embodiment. To avoid repetition, details are not elaborated here. Alternatively, when the computer program is executed by the processor 21, it implements the functions of each model / unit in the simulation analysis device applied to the porous structure in the embodiment. To avoid repetition, details are not elaborated here.

[0092] The computer device 20 includes, but is not limited to, a processor 21 and a memory 22. Those skilled in the art can understand that Figure 8 This is merely an example of the computer device 20 and does not constitute a limitation on the computer device 20. It may include more or fewer components than those shown in the figure, or combine certain components, or have different components. For example, the computer device may also include input / output devices, network access devices, a bus, etc.

[0093] The so-called processor 21 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0094] The memory 22 may be an internal storage unit of the computer device 20, such as the hard disk or memory of the computer device 20. The memory 22 may also be an external storage device of the computer device 20, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device 20. Further, the memory 22 may also include both the internal storage unit and the external storage device of the computer device 20. The memory 22 is used to store the computer program and other programs and data required by the computer device. The memory 22 may also be used to temporarily store data that has been output or will be output.

[0095] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0096] In several embodiments provided by the present invention, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0097] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0098] In addition, in each embodiment of the present invention, the functional units can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of a combination of hardware and software functional units.

[0099] The above-mentioned integrated units implemented in the form of software functional units can be stored in a computer-readable storage medium. The above-mentioned software functional units stored in a storage medium include several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (Processor) to execute some steps of the methods described in each embodiment of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0100] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A simulation analysis method for a porous structure, characterized in that, including: performing image processing on the acquired original porous structure image to generate a porous structure image; performing three-dimensional reconstruction on the porous structure image to generate a structure model; performing mesh processing on the structure model to generate a mesh model; performing multi-physics field simulation based on the mesh model to generate simulation data, where the simulation data includes pressure gradient, effective conductivity, heat flux density, and permeability; performing calculation on the simulation data based on a genetic algorithm to generate a simulation analysis result.

2. The method according to claim 1, characterized in that, The multi-physics field simulation includes gas flow simulation, the simulation data includes pressure gradient, and performing multi-physics field simulation based on the mesh model to generate simulation data includes: generating a porosity based on the pore volume and total material volume of the acquired porous structure; generating an effective viscosity of the medium in the porous structure based on the porosity, the acquired gas dynamic viscosity, and average particle diameter; generating the pressure gradient based on the effective viscosity and the acquired gas velocity field.

3. The method according to claim 2, characterized in that, The multi-physics field simulation includes conductivity simulation, the simulation data includes effective conductivity, and performing multi-physics field simulation based on the mesh model to generate simulation data includes: generating the effective conductivity based on the porosity and the conductivity of the porous structure material itself that is acquired.

4. The method according to claim 2, characterized in that, The multi-physics field simulation includes permeability simulation, the simulation data includes permeability, and performing multi-physics field simulation based on the mesh model to generate simulation data includes: generating the permeability based on the porosity, the acquired particle diameter, and specific surface area of the porous structure.

5. The method according to claim 1, characterized in that, The multi-physics field simulation includes heat conduction simulation, the simulation data includes heat flux density, and performing multi-physics field simulation based on the mesh model to generate simulation data includes: generating the heat flux density based on the thermal conductivity and temperature gradient of the acquired porous structure.

6. The method according to claim 1, characterized in that, Performing calculation on the simulation data based on a genetic algorithm to generate a simulation analysis result includes: performing calculation on the pressure gradient, the effective conductivity, the heat flux density, the permeability, the set target pressure gradient, target effective conductivity, target heat flux density, and target permeability based on a genetic algorithm to generate a simulation analysis result.

7. The method according to claim 6, characterized in that, Performing calculation on the pressure gradient, the effective conductivity, the heat flux density, the permeability, the set target pressure gradient, target effective conductivity, target heat flux density, and target permeability based on a genetic algorithm to generate a simulation analysis result includes: constituting a fitness function based on the pressure gradient, the effective conductivity, the heat flux density, the permeability, the set target pressure gradient, target effective conductivity, target heat flux density, target permeability, and weight coefficient; performing calculation on the fitness function based on the genetic algorithm to generate a fitness function value; when the fitness function value is less than or equal to a set threshold, evaluating the performance of the porous structure as good; or when the fitness function value is greater than the set threshold, adjusting the structural parameters of the structure model and continuing to execute the step of performing mesh processing on the structure model to generate a mesh model; Among them, the structural parameters include one or any combination of the particle diameter of the porous structure, the gas dynamic viscosity, the specific surface area, the porosity, and / or the heat flux density.

8. A simulation analysis device for a porous structure, characterized in that, Comprising: A first generation module, configured to perform image processing on the acquired original porous structure image to generate a porous structure image; A second generation module, configured to perform three-dimensional reconstruction on the porous structure image to generate a structure model; A third generation module, configured to perform mesh processing on the structure model to generate a mesh model; A fourth generation module, configured to perform multi-physical field simulation according to the mesh model to generate simulation data, where the simulation data includes a pressure gradient, an effective conductivity, a heat flux density, and a permeability; A fifth generation module, configured to calculate the simulation data based on a genetic algorithm to generate a simulation analysis result.

9. A storage medium, characterized in that, The storage medium includes a stored program, wherein when the program runs, it controls the device where the storage medium is located to execute the simulation analysis method of the porous structure according to any one of claims 1 to 7.

10. A computer device, comprising a memory and a processor, the memory being used for storing information including program instructions, and the processor being used for controlling the execution of the program instructions, characterized in that, When the program instructions are loaded and executed by a processor, the steps of the simulation analysis method of the porous structure according to any one of claims 1 to 7 are implemented.

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