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

Abstract

The embodiments of the present invention provide a simulation analysis method, device, storage medium, and computer equipment for porous structures. The method includes: 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 grid processing on the structure model to generate a grid model; performing multi-physics field simulation based on the grid model to generate simulation data, wherein the simulation data includes pressure gradient, effective conductivity, heat flux density, and permeability; and calculating the simulation data based on a genetic algorithm to generate simulation analysis results. In the technical solution provided by the embodiments of the present invention, by performing multi-physics field simulation on the grid model to generate simulation data and calculating the simulation data based on the genetic algorithm to generate simulation analysis results, the simulation accuracy and the reliability of the simulation analysis results 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 equipment for porous structures. Background Art

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

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

[0004] In one aspect, an embodiment of the present invention provides a simulation analysis method for a porous structure, comprising:

[0005] performing image processing on the acquired original porous structure image to generate a porous structure image;

[0006] Performing three-dimensional reconstruction on the porous structure image to generate a structural model;

[0007] Performing grid processing on the structural model to generate a grid model;

[0008] Performing multi-physics field simulation according to the grid model to generate simulation data, wherein the simulation data includes pressure gradient, effective conductivity, heat flux density, and permeability;

[0009] The simulation data is calculated based on a genetic algorithm to generate simulation analysis results.

[0010] Optionally, the multi-physics field simulation includes gas flow simulation, the simulation data includes pressure gradient, and performing the multi-physics field simulation according to the grid model to generate the simulation data includes:

[0011] generating a porosity based on the obtained pore volume and total material volume of the porous structure;

[0012] generating an effective viscosity of a medium in the porous structure according to the porosity, the obtained gas dynamic viscosity, and the average particle diameter;

[0013] The pressure gradient is generated according to the effective viscosity and the acquired gas velocity field.

[0014] Optionally, the multi-physics simulation includes conductivity simulation, the simulation data includes effective conductivity, and performing the multi-physics simulation according to the grid model to generate the simulation data includes:

[0015] The effective conductivity is generated according to the porosity and the obtained conductivity of the porous structure material itself.

[0016] Optionally, the multi-physics field simulation includes permeability simulation, the simulation data includes permeability, and performing the multi-physics field simulation according to the grid model to generate the simulation data includes:

[0017] The permeability is generated based on the porosity and the obtained particle diameter and specific surface area of ​​the porous structure.

[0018] Optionally, the multi-physics field simulation includes heat conduction simulation, the simulation data includes heat flux density, and performing the multi-physics field simulation according to the grid model to generate the simulation data includes:

[0019] The heat flux density is generated according to the obtained thermal conductivity and temperature gradient of the porous structure.

[0020] Optionally, the calculating the simulation data based on a genetic algorithm to generate a simulation analysis result includes:

[0021] The pressure gradient, the effective conductivity, the heat flux, the permeability, the set target pressure gradient, the target effective conductivity, the target heat flux and the target permeability are calculated based on a genetic algorithm to generate a simulation analysis result.

[0022] Optionally, the calculating the pressure gradient, the effective conductivity, the heat flux, the permeability, the set target pressure gradient, the target effective conductivity, the target heat flux, and the target permeability based on the genetic algorithm to generate a simulation analysis result includes:

[0023] A fitness function is constructed according to the pressure gradient, the effective conductivity, the heat flux, the permeability, a set target pressure gradient, a target effective conductivity, a target heat flux, a target permeability, and a weight coefficient;

[0024] Calculating the fitness function according to the genetic algorithm to generate a fitness function value;

[0025] 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,

[0026] When the fitness function value is greater than a set threshold, adjusting the structural parameters of the structural model, and continuing to perform the step of performing grid processing on the structural model to generate a grid model;

[0027] The structural parameters include one or any combination of particle diameter, gas dynamic viscosity, specific surface area, porosity and / or heat flux density of the porous structure.

[0028] In another aspect, an embodiment of the present invention provides a simulation analysis device for a porous structure, comprising:

[0029] A first generating module is used to perform image processing on the acquired original porous structure image to generate a porous structure image;

[0030] A second generating module is used to perform three-dimensional reconstruction on the porous structure image to generate a structural model;

[0031] A third generation module is used to perform grid processing on the structural model to generate a grid model;

[0032] a fourth generation module, configured to perform multi-physics field simulation according to the grid model to generate simulation data, wherein the simulation data includes pressure gradient, effective conductivity, heat flux density, and permeability;

[0033] The fifth generating module is used to calculate the simulation data based on the genetic algorithm to generate simulation analysis results.

[0034] On the other hand, an embodiment of the present invention provides a storage medium, which includes a stored program, wherein when the program is run, the device where the storage medium is located is controlled to execute the above-mentioned simulation analysis method for porous structures.

[0035] On the other hand, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions, wherein the program instructions, when loaded and executed by the processor, implement the steps of the above-mentioned simulation analysis method of the porous structure.

[0036] 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 structural model; grid processing is performed on the structural model to generate a grid model; multi-physics field simulation is performed based on the grid model to generate simulation data, wherein the simulation data includes pressure gradient, effective conductivity, heat flux density, and permeability; and the simulation data is calculated based on a genetic algorithm to generate simulation analysis results. In the technical solution provided by the embodiment of the present invention, by performing multi-physics field simulation on the grid model to generate simulation data and calculating the simulation data based on the genetic algorithm to generate simulation analysis results, the simulation accuracy and reliability of the simulation analysis results of the porous structure are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0038] Figure 1 A flowchart of a simulation analysis method for a porous structure provided by one embodiment of the present invention;

[0039] Figure 2 A schematic diagram of an original porous structure image provided by one embodiment of the present invention;

[0040] Figure 3 A schematic diagram of a structural model provided by one embodiment of the present invention;

[0041] Figure 4 A schematic diagram of a grid model provided by one embodiment of the present invention;

[0042] Figure 5 A flowchart of performing multi-physics field simulation based on a grid model and generating simulation data is provided in one embodiment of the present invention;

[0043] Figure 6 A schematic diagram of a pressure gradient provided by one embodiment of the present invention;

[0044] Figure 7 A schematic diagram of a simulation analysis device for a porous structure provided by one embodiment of the present invention;

[0045] Figure 8 A schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0046] In order to better understand the technical solution of the present invention, the embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0047] It should be understood that the embodiments described are only a portion of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0048] 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 "a", "an", "the" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms unless the context clearly indicates otherwise.

[0049] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. Furthermore, the character " / " in this document generally indicates an "or" relationship between the associated objects.

[0050] Common modeling methods in related technologies use idealized or regular structures (such as honeycombs and sphere stacking) to replace the actual structure of porous structures. This results in inaccurate reflection of pore distribution, fiber orientation, current paths, and other aspects. Furthermore, there is a lack of a unified modeling process, making it unsuitable for multiple simulation platforms. The lack of methods to adjust the structural parameters of porous structures makes it impossible to support structural optimization design, resulting in poor modeling flexibility. Due to inaccurate structural representation, the calculated physical properties of porous structures differ significantly from experimental values, affecting their engineering guidance value, resulting in low simulation accuracy, and low reliability of simulation analysis results.

[0051] In order to solve the technical problems in the related art, the embodiment of the present invention provides a simulation analysis method of a porous structure. Figure 1 A flowchart of a simulation analysis method for a porous structure provided by an embodiment of the present invention is shown in FIG. Figure 1 As shown, the method includes:

[0052] Step 102: performing image processing on the acquired original porous structure image to generate a porous structure image.

[0053] In the embodiment of the present invention, each step is performed by a computer device, for example, a computer, a tablet computer, a server, or a simulation device.

[0054] In the embodiment of the present invention, the porous structure includes porous titanium felt. In the embodiment of the present invention, titanium felt for anode of a proton exchange membrane fuel cell is used as an example for description.

[0055] 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, an image of the original porous structure. Figure 2 A schematic diagram of an original porous structure image provided by one embodiment of the present invention.

[0056] Specifically, the acquired original porous structure image is subjected to image processing, wherein the image processing includes image enhancement, filtering and binarization processing to generate a porous structure image.

[0057] Step 104: Perform three-dimensional reconstruction on the porous structure image to generate a structural model.

[0058] In the embodiment of the present invention, the porous structure image can be 3D reconstructed based on 3D reconstruction software (such as Avizo) to generate a structural model, which is a real structural model of the porous structure. Figure 3 A schematic diagram of a structural model provided by an embodiment of the present invention.

[0059] Step 106: Perform grid processing on the structural model to generate a grid model.

[0060] In the embodiment of the present invention, the structural model may be pre-processed such as repairing to eliminate unclear structures such as burrs in the structural model, and output to an STL format or other format compatible with simulation software.

[0061] In the embodiment of the present invention, the structural model can be imported into a mesh modeling software (such as ANSYS), and mesh processing is performed using an unstructured tetrahedral mesh according to the complexity of the structure, and local encryption is performed to generate a mesh model. Figure 4 A schematic diagram of a grid model provided in accordance with an embodiment of the present invention.

[0062] Due to the irregularity and complex pore structure of porous structures, unstructured tetrahedral meshes are ideal for automatic generation and adaptation to complex surfaces. Localized meshing involves using smaller, denser meshes in key areas of the structural model (rather than the entire model) to improve simulation accuracy, keep the overall mesh count small, and conserve computing resources.

[0063] Step 108: Perform multi-physics field simulation based on the grid model to generate simulation data, wherein the simulation data includes pressure gradient, effective conductivity, heat flux density, and permeability.

[0064] In the embodiment of the present invention, the multi-physics field simulation includes: gas flow simulation, electrical conductivity simulation, heat conduction simulation and permeability simulation.

[0065] Gas Flow Simulation: Utilizing a gas flow model based on Darcy's law, the pressure gradient is used to calculate the flow behavior of gas in porous structures (porous titanium felt). Under low Reynolds number flow conditions, the velocity and pressure fields of the gas flow can be predicted with high accuracy, ensuring that the simulation data matches the actual gas flow conditions.

[0066] Conductivity simulation: Effective Medium Theory (EMT) is used to simulate the conductivity of porous structures (porous titanium felt). Considering the relationship between different porosities and material conductivity, the conductivity characteristics of porous structures (porous titanium felt) under electric fields can be accurately calculated, supporting performance evaluation in electrochemical applications.

[0067] Heat conduction simulation: Using Fourier's law to simulate heat conduction in porous materials (porous titanium felt), we calculate the distribution of heat flux and temperature gradient. Accurate modeling of heat conduction in multiphysics simulations is crucial for optimizing device temperature control performance, particularly in thermal management of fuel cells and battery systems.

[0068] Permeability simulation: Using models such as the Kozeny-Carman equation, we evaluate the permeability of porous structures (porous titanium felt) and reveal the flow efficiency of gases or fluids in porous media. This simulation data provides an important theoretical basis for optimizing the structure of porous structures (porous titanium felt), ensuring their high performance in practical applications.

[0069] Figure 5 A flowchart of performing multi-physics field simulation based on a grid model and generating simulation data is provided in one embodiment of the present invention, such as Figure 5 As shown, step 108 includes:

[0070] Step 1082: Generate porosity based on the acquired pore volume and total material volume of the porous structure.

[0071] In the embodiments of the present invention, Darcy's law (for low Reynolds number flow) is generally used to model gas flow in a porous medium. However, for complex porous structures, the Brinkman model may need to be used for correction.

[0072] In the embodiment of the present invention, the pore volume and the total material volume of the porous structure can be obtained from the grid model.

[0073] Specifically, through the formula The pore volume and total material volume of the porous structure are calculated to generate the porosity. is the porosity, is the pore volume, is the total volume of matter.

[0074] Step 1084: Generate the effective viscosity of the medium in the porous structure according to the porosity, the obtained gas dynamic viscosity, and the average particle diameter.

[0075] In the embodiment of the present invention, the gas dynamic viscosity and the average particle diameter can be obtained from the grid model.

[0076] Specifically, through the formula The porosity, gas dynamic viscosity, and average particle diameter are calculated to generate the effective viscosity of the medium in the porous structure. 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.

[0077] Step 1086: Generate a pressure gradient based on the effective viscosity and the acquired gas velocity field.

[0078] In the embodiment of the present invention, the gas velocity field can be obtained from the grid model.

[0079] Specifically, through the formula The effective viscosity and gas velocity field are calculated to generate the pressure gradient. is the effective viscosity, is the velocity field of the gas (m / s), is the pressure gradient (Pa / m), Figure 6 A schematic diagram of a pressure gradient provided by an embodiment of the present invention. A smaller pressure gradient means a smaller flow resistance.

[0080] Step 1088: Generate effective conductivity based on the porosity and the acquired conductivity of the porous structure material itself.

[0081] In the embodiment of the present invention, in the calculation of effective conductivity, an effective conductivity model (based on the conductivity, porosity and structure of different materials) is generally used, and can be characterized by a random network model or effective medium theory.

[0082] In the embodiment of the present invention, the electrical conductivity of the porous structure material itself can be obtained from the grid model.

[0083] Specifically, through the formula The porosity and the electrical conductivity of the porous structure material itself are calculated to generate the effective conductivity. is the effective conductivity (S / m), is the electrical conductivity of the porous structure material itself (S / m), is the porosity. A larger effective conductivity means that the porous structure has better electron / ion conductivity.

[0084] Step 1090: Generate permeability based on the porosity and the obtained particle diameter and specific surface area of ​​the porous structure.

[0085] In the embodiment of the present invention, the particle diameter and specific surface area of ​​the porous structure can be obtained from the grid model.

[0086] Specifically, there is a certain relationship between permeability and porosity, which is usually expressed by the Kozeny-Carman equation, that is, it can be expressed by the formula The porosity, particle diameter of the porous structure and specific surface area are calculated to generate the permeability. is the permeability (m²), is the particle diameter (m), is the porosity, is the specific surface area (m² / m³). A higher permeability means a better permeability effect.

[0087] Step 1092: Generate heat flux density based on the acquired thermal conductivity and temperature gradient of the porous structure.

[0088] In the embodiment of the present invention, the thermal conductivity and temperature gradient of the porous structure can be obtained from the grid model.

[0089] Specifically, the energy transfer during heat conduction follows Fourier's law, which is expressed by the formula The thermal conductivity and temperature gradient of the porous structure are calculated to generate the heat flux density. is the heat flux density (W / m²), is the thermal conductivity (W / m·K), is the temperature gradient (K / m). A higher heat flux density means a more efficient material heat dissipation capability.

[0090] Step 110: Calculate the simulation data based on the genetic algorithm to generate simulation analysis results.

[0091] In the embodiment of the present invention, a performance-structure parameter database is established, combined with a genetic algorithm, and the fiber structure parameters are reversely adjusted to generate a new generation of structure samples for rapid evaluation and selection of the best design solution.

[0092] In an embodiment of the present invention, the pressure gradient, effective conductivity, heat flux, permeability, set target pressure gradient, target effective conductivity, target heat flux and target permeability can be calculated based on a genetic algorithm to generate simulation analysis results.

[0093] Specifically, step 110 may include:

[0094] Step S1, constructing a fitness function according to the pressure gradient, effective conductivity, heat flux, permeability, set target pressure gradient, target effective conductivity, target heat flux, target permeability and weight coefficient.

[0095] In the embodiment of the present invention, when performing structural optimization, a fitness function can be used to evaluate the performance of different structures. Assuming that the goal is to optimize the balance between permeability and conductivity, the fitness function can be defined as: ,

[0096] in, 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, is the permeability, is the target penetration rate, is the weight coefficient, and the value range of each weight coefficient is 0.2~0.5, and .

[0097] In the embodiment of the present invention, the target pressure gradient, target effective conductivity, target heat flux and target permeability can be set according to actual conditions. For example, the target pressure gradient ranges from 1000 to 10000 Pa / m, the target effective conductivity is 1000 S / m, and the target heat flux ranges from 5000 to 20000 W / m. 2 , the target permeability range is 10 -13 ~10 -11 m 2 .

[0098] Step S2: Calculate the fitness function according to the genetic algorithm to generate a fitness function value.

[0099] In an embodiment of the present invention, the genetic algorithm generates a batch of candidate structures in each generation, calculates the fitness function value of each structure, preferentially retains individuals with low fitness function values ​​(better target performance), generates new candidate structures, and ultimately finds the optimal or near-optimal porous structure through iterative evolution, so that the fitness function value is minimized.

[0100] Step S3: When the fitness function value is less than or equal to the set threshold, the performance of the porous structure is evaluated as good.

[0101] In the embodiment of the present invention, the threshold value can be set according to actual conditions, for example, the threshold value is set to 0.3.

[0102] Step S4: When the fitness function value is greater than the set threshold, adjust the structural parameters of the structural model and continue to step 106. The structural parameters include one or any combination of particle diameter, gas dynamic viscosity, specific surface area, porosity and / or heat flux density of the porous structure.

[0103] In the embodiment of the present invention, the above optimization is aimed at the fluid structure. In fact, the porous structure also needs to consider its structural performance such as support and strength. Therefore, it is necessary to comprehensively consider how to adjust the structural parameters of the structural model.

[0104] In the technical solution provided by the embodiments of the present invention, a porous titanium felt for proton exchange membrane fuel cell anodes was used as an example. A random fiber generation method was used, with fibers set to a diameter of 5 μm, a length of 400 μm, and random orientation. This generated a three-dimensional structural model with a porosity of approximately 75%. This model was then imported into the fluid simulation software (FLUENT), with an inlet flow rate of 0.5 m / s and an outlet pressure set to constant. The average pressure drop and permeability were calculated. The porous structure was then imported into an electric field module, with a 2 V voltage difference applied between the upper and lower surfaces. The equivalent volume conductivity was then calculated. By adjusting the fiber density and orientation angle, the optimal balance between permeability and conductivity was achieved.

[0105] 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 structural model; grid processing is performed on the structural model to generate a grid model; multi-physics field simulation is performed based on the grid model to generate simulation data, wherein the simulation data includes pressure gradient, effective conductivity, heat flux density, and permeability; and the simulation data is calculated based on a genetic algorithm to generate simulation analysis results. In the technical solution provided by the embodiment of the present invention, by performing multi-physics field simulation on the grid model to generate simulation data and calculating the simulation data based on the genetic algorithm to generate simulation analysis results, the simulation accuracy and reliability of the simulation analysis results of the porous structure are improved.

[0106] In the technical solution provided by the embodiment of the present invention, a modeling method for porous structures is established that supports the reconstruction of real original porous structure images and parameter-controllable generation, forming a general process that can be widely adapted to a variety of multi-physics field simulation platforms, achieving accurate mapping between microstructure and performance, improving the reliability of simulation predictions, supporting structural reverse optimization design, and providing support for device performance improvement.

[0107] The technical solution provided in the embodiment of the present invention provides a method for structural modeling that simultaneously supports real structures and controllable parameters, taking into account both simulation accuracy and flexibility. The constructed three-dimensional structural model can truly reflect the microscopic characteristics of porous titanium felt, improve the reliability of simulation analysis, and has strong multi-physical field adaptability. It is suitable for structure-performance correlation analysis and reverse optimization design, and can be widely used in electrochemical system structure optimization, performance prediction and fault diagnosis research related to titanium felt.

[0108] An embodiment of the present invention provides a simulation analysis device for a porous structure. Figure 7 A schematic diagram of a simulation analysis device for a porous structure provided by an embodiment of the present invention is shown in FIG. Figure 7 As shown, the device includes: a first generating module 11, a second generating module 12, a third generating module 13, a fourth generating module 14 and a fifth generating module 15.

[0109] The first generating module 11 is used to perform image processing on the acquired original porous structure image to generate a porous structure image.

[0110] The second generating module 12 is used to perform three-dimensional reconstruction on the porous structure image to generate a structural model.

[0111] The third generating module 13 is used to perform grid processing on the structural model to generate a grid model.

[0112] The fourth generation module 14 is used to perform multi-physics field simulation according to the grid model to generate simulation data, wherein the simulation data includes pressure gradient, effective conductivity, heat flux density and permeability.

[0113] The fifth generating module 15 is used to calculate the simulation data based on a genetic algorithm to generate a simulation analysis result.

[0114] In the embodiment of the present invention, the multi-physics field simulation includes gas flow simulation, and the simulation data includes pressure gradient. The fourth generation module 14 is specifically configured to:

[0115] generating a porosity based on the obtained pore volume and total material volume of the porous structure;

[0116] generating an effective viscosity of a medium in a porous structure according to the porosity and the obtained gas dynamic viscosity and average particle diameter;

[0117] The pressure gradient is generated according to the effective viscosity and the acquired gas velocity field.

[0118] In the embodiment of the present invention, the multi-physics field simulation includes conductivity simulation, the simulation data includes effective conductivity, and the fourth generation module 14 is specifically used to generate the effective conductivity according to the porosity and the obtained conductivity of the porous structure material itself.

[0119] In the embodiment of the present invention, the multi-physics field simulation includes permeability simulation, the simulation data includes permeability, and the fourth generation module 14 is specifically used to generate the permeability according to the porosity and the obtained particle diameter and specific surface area of ​​the porous structure.

[0120] In the embodiment of the present invention, the multi-physics field simulation includes heat conduction simulation, and the simulation data includes heat flux density. The fourth generation module 14 is specifically used to generate the heat flux density according to the acquired thermal conductivity and temperature gradient of the porous structure.

[0121] In an embodiment of the present invention, the fifth generation module 15 is specifically used to calculate the pressure gradient, the effective conductivity, the heat flux, the permeability, the set target pressure gradient, the target effective conductivity, the target heat flux and the target permeability based on a genetic algorithm to generate a simulation analysis result.

[0122] In the embodiment of the present invention, the fifth generating module 15 is specifically configured to:

[0123] A fitness function is constructed according to the pressure gradient, the effective conductivity, the heat flux, the permeability, a set target pressure gradient, a target effective conductivity, a target heat flux, a target permeability, and a weight coefficient;

[0124] Calculating the fitness function according to the genetic algorithm to generate a fitness function value;

[0125] 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,

[0126] When the fitness function value is greater than a set threshold, adjusting the structural parameters of the structural model, and continuing to perform the step of performing grid processing on the structural model to generate a grid model;

[0127] The structural parameters include one or any combination of particle diameter, gas dynamic viscosity, specific surface area, porosity and / or heat flux density of the porous structure.

[0128] 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 structural model; grid processing is performed on the structural model to generate a grid model; multi-physics field simulation is performed based on the grid model to generate simulation data, wherein the simulation data includes pressure gradient, effective conductivity, heat flux density, and permeability; and the simulation data is calculated based on a genetic algorithm to generate simulation analysis results. In the technical solution provided by the embodiment of the present invention, by performing multi-physics field simulation on the grid model to generate simulation data and calculating the simulation data based on the genetic algorithm to generate simulation analysis results, the simulation accuracy and reliability of the simulation analysis results of the porous structure are improved.

[0129] The simulation analysis device for porous structures provided by the embodiment of the present invention can be used to achieve the above Figure 1 The simulation analysis method of the porous structure can be described in detail in the embodiment of the simulation analysis method of the porous structure, which will not be repeated here.

[0130] An embodiment of the present invention provides a storage medium, which includes a stored program, wherein when the program is running, the device where the storage medium is located is controlled to execute the various steps of the embodiment of the above-mentioned simulation analysis method for porous structures. For specific descriptions, please refer to the embodiment of the above-mentioned simulation analysis method for porous structures.

[0131] An embodiment of the present invention provides a computer device including a memory and a processor, wherein 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 embodiment of the above-mentioned simulation analysis method for porous structures are implemented. For a specific description, please refer to the embodiment of the above-mentioned simulation analysis method for porous structures.

[0132] Figure 8 A schematic diagram of a computer device provided by an embodiment of the present invention. Figure 8 As 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 by the processor 21. When executed by the processor 21, the computer program 23 implements the simulation analysis method for porous structures in the embodiment. To avoid repetition, a detailed description is not given here. Alternatively, when executed by the processor 21, the computer program implements the functions of each model / unit in the simulation analysis device for porous structures in the embodiment. To avoid repetition, a detailed description is not given here.

[0133] The computer device 20 includes, but is not limited to, a processor 21 and a memory 22. Those skilled in the art will understand that Figure 8This is merely an example of the computer device 20 and does not constitute a limitation of the computer device 20 . The computer device 20 may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0134] The processor 21 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0135] The memory 22 can 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 can 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 memory card, etc. equipped on the computer device 20. Furthermore, the memory 22 can include both the internal storage unit of the computer device 20 and an external storage device. The memory 22 is used to store computer programs and other programs and data required by the computer device. The memory 22 can also be used to temporarily store data that has been output or is about to be output.

[0136] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0137] In the 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 merely a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or unit, which may be electrical, mechanical or other forms.

[0138] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0139] In addition, the functional units in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or hardware plus software functional units.

[0140] The aforementioned integrated unit implemented as a software functional unit can be stored in a computer-readable storage medium. The software functional unit, stored in a storage medium, includes instructions for causing a computer device (which may be a personal computer, server, or network device, etc.) or a processor to execute portions of the method steps described in various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a removable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0141] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A simulation analysis method for a porous structure, characterized in that: include: 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 structural model; Performing grid processing on the structural model to generate a grid model; Performing multi-physics field simulation according to the grid model to generate simulation data, wherein the simulation data includes pressure gradient, effective conductivity, heat flux density, and permeability; Calculating the simulation data based on a genetic algorithm to generate simulation analysis results; The calculating the simulation data based on the genetic algorithm to generate simulation analysis results includes: A fitness function is constructed according to the pressure gradient, the effective conductivity, the heat flux, the permeability, a set target pressure gradient, a target effective conductivity, a target heat flux, a target permeability, and a 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.

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 the multi-physics field simulation according to the grid model to generate simulation data includes: generating a porosity based on the obtained pore volume and total material volume of the porous structure; generating an effective viscosity of a medium in a porous structure according to the porosity and the obtained gas dynamic viscosity and average particle diameter; The pressure gradient is generated according to the effective viscosity and the acquired gas velocity field.

3. The method according to claim 2, characterized in that The multi-physics simulation includes conductivity simulation, the simulation data includes effective conductivity, and performing the multi-physics simulation according to the grid model to generate simulation data includes: The effective conductivity is generated according to the porosity and the obtained conductivity of the porous structure material itself.

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 the multi-physics field simulation according to the grid model to generate the simulation data includes: The permeability is generated based on the porosity and the obtained particle diameter and specific surface area of ​​the porous structure.

5. The method according to claim 1, wherein The multi-physics field simulation includes heat conduction simulation, the simulation data includes heat flux density, and performing the multi-physics field simulation according to the grid model to generate simulation data includes: The heat flux density is generated according to the obtained thermal conductivity and temperature gradient of the porous structure.

6. The method according to claim 1, characterized in that Also includes: When the fitness function value is greater than a set threshold, adjusting the structural parameters of the structural model, and continuing to perform the step of performing grid processing on the structural model to generate a grid model; The structural parameters include one or any combination of particle diameter, gas dynamic viscosity, specific surface area, porosity and / or heat flux density of the porous structure.

7. A simulation analysis device for porous structures, characterized in that: include: A first generating module is used to perform image processing on the acquired original porous structure image to generate a porous structure image; A second generating module is used to perform three-dimensional reconstruction on the porous structure image to generate a structural model; A third generation module is used to perform grid processing on the structural model to generate a grid model; a fourth generation module, configured to perform multi-physics field simulation according to the grid model to generate simulation data, wherein the simulation data includes pressure gradient, effective conductivity, heat flux density, and permeability; a fifth generating module, configured to calculate the simulation data based on a genetic algorithm to generate a simulation analysis result; The fifth generation module is specifically used to construct a fitness function based on the pressure gradient, the effective conductivity, the heat flux, the permeability, the set target pressure gradient, target effective conductivity, target heat flux, target permeability and 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.

8. A storage medium, characterized in that: The storage medium includes a stored program, wherein when the program is run, the device where the storage medium is located is controlled to execute the simulation analysis method for porous structures according to any one of claims 1 to 6.

9. A computer device comprising a memory and a processor, wherein the memory is used to store information including program instructions, and the processor is used to control the execution of the program instructions, characterized in that: When the program instructions are loaded and executed by the processor, the steps of the simulation analysis method for porous structures according to any one of claims 1 to 6 are implemented.

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