A method, system, device, and medium for predicting gas diffusion layer gas-liquid transport

Abstract

The application discloses a kind of gas diffusion layer gas-liquid transport prediction method, system, equipment and medium, comprising obtaining the structure data of gas diffusion layer compression;Obtain the flow field data of target fluid in the gas diffusion layer and configuration information;The configuration information includes collision equation data, migration equation data and flow field boundary data;According to the structure data, the flow field data and the configuration information, simulation processing is carried out, and the equilibrium state simulation result of the target fluid in the gas diffusion layer is obtained.The application can truly simulate the influence of different assembly conditions on the gas-liquid transport behavior in the gas diffusion layer by combining the structure data of the gas diffusion layer after compression with the flow field data of the target fluid, with configuration information, efficient and accurate, good robustness, high calculation precision, greatly help to optimize the assembly conditions of fuel cell and improve the "waterlogging" phenomenon of membrane electrode.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of fuel cells, in particular to a gas diffusion layer gas-liquid transport prediction method, system, device and medium. BACKGROUND

[0002] Proton exchange membrane fuel cells are considered to be the most promising green energy devices, in which carbon fiber paper or carbon fiber woven fabric is generally used for gas diffusion layer to ensure gas permeability and electrical conductivity; during the fuel cell reaction, the reaction gas (oxygen) is transported to the gas diffusion layer and the catalyst layer in turn through the flow channel, and the reaction product water of the electrochemical reaction is discharged in the form of droplets through the flow channel under the blowing of the incoming gas to ensure the normal operation of the cell. However, because the fuel cell generally works at a lower temperature, and the reaction gas has a high degree of humidity, a phenomenon called "flooding" occurs in the membrane electrode, especially on the cathode side, where water vapor condenses and blocks the gas transport channels of the gas diffusion layer, which can cause the performance of the cell to degrade.

[0003] In addition, during the fuel cell stack assembly process, the end plates on both sides of the stack are usually subjected to a certain amount of pre-tightening force to ensure the sealing of the stack and good contact between different components, but this pre-tightening force can cause different degrees of compression and local carbon fiber breakage of the gas diffusion layer; especially at the location of the bipolar plate ridge, the compression deformation is more serious, resulting in a decrease in the porosity of the gas diffusion layer, affecting gas-liquid transport, increasing the likelihood of "flooding" phenomenon, and seriously affecting the efficiency of the electrochemical reaction and the performance of the fuel cell.

[0004] The local porosity and hydrophobicity of the gas diffusion layer under different assembly conditions are different, and the difficulty of droplet discharge is different; the current prediction model cannot accurately predict the geometric microstructure of the gas diffusion layer after compression and carbon fiber breakage under given assembly conditions, nor can it effectively track the gas-liquid interface of the reaction product water, resulting in the inability to truly simulate the gas-liquid transport phenomenon in the gas diffusion layer under different assembly conditions. SUMMARY

[0005] In view of the problems existing in the prior art, the present application provides a gas diffusion layer gas-liquid transport prediction method, system, device and medium, which can truly simulate the influence of different assembly conditions on the gas-liquid transport behavior in the gas diffusion layer, is efficient and accurate, has good robustness, and has high calculation precision. The technical solution is as follows:

[0006] In one aspect, the present application provides a gas diffusion layer gas-liquid transport prediction method, comprising:

[0007] obtaining the structure data of the compressed gas diffusion layer;

[0008] Obtaining flow field data and configuration information of a target fluid in the gas diffusion layer; the configuration information includes collision equation data, migration equation data and flow field boundary data;

[0009] According to the structure data, the flow field data and the configuration information, performing simulation processing to obtain an equilibrium state simulation result of the target fluid in the gas diffusion layer.

[0010] Further, the obtaining of the compressed structure data of the gas diffusion layer includes:

[0011] Obtaining initial structure data of the gas diffusion layer;

[0012] Obtaining compression boundary data; the compression boundary data represents a compression condition applied to the gas diffusion layer;

[0013] According to the compression boundary data and the initial structure data, obtaining the compressed structure data.

[0014] Further, the simulation processing according to the structure data, the flow field data and the configuration information to obtain the equilibrium state simulation result of the target fluid in the gas diffusion layer includes:

[0015] According to the structure data, the flow field data and the configuration information, obtaining a dynamic intermediate simulation result; the dynamic intermediate simulation result includes a contact angle of the target fluid;

[0016] In a case where the contact angle meets a preset condition, obtaining the equilibrium state simulation result according to the dynamic intermediate simulation result corresponding to the contact angle.

[0017] Further, the obtaining of the dynamic intermediate simulation result according to the structure data, the flow field data and the configuration information includes:

[0018] According to the structure data, the flow field data and the configuration information, obtaining an interaction force of the target fluid;

[0019] According to the interaction force of the target fluid, obtaining the contact angle corresponding to the target fluid.

[0020] Further, the obtaining of the interaction force of the target fluid according to the structure data, the flow field data and the configuration information includes:

[0021] According to the structure data, the flow field data and the configuration information, obtaining a macroscopic quantity of the target fluid; the macroscopic quantity includes a macroscopic density, a macroscopic velocity, a macroscopic pressure and a macroscopic temperature;

[0022] According to the macroscopic quantity, obtaining the interaction force.

[0023] Furthermore, after obtaining a dynamic intermediate simulation result based on the structural data, the flow field data, and the configuration information, the method further includes:

[0024] When the contact angle does not meet the preset condition, the flow field data is updated according to the dynamic intermediate simulation result corresponding to the contact angle, and the dynamic intermediate simulation result is obtained based on the structural data, the flow field data and the configuration information.

[0025] Furthermore, when the contact angle does not meet the preset condition, updating the flow field data according to the dynamic intermediate simulation result corresponding to the contact angle, and returning to execute the dynamic intermediate simulation result obtained according to the structural data, the flow field data and the configuration information includes:

[0026] When the change in the contact angle is greater than a preset threshold, the flow field data is updated according to the dynamic intermediate simulation result corresponding to the contact angle, and the dynamic intermediate simulation result is obtained based on the structural data, the flow field data and the configuration information. The change is obtained based on the difference between the contact angle corresponding to the dynamic intermediate simulation result before the flow field data is updated and the contact angle corresponding to the dynamic intermediate simulation result after the flow field data is updated.

[0027] In another aspect, the present invention provides a prediction system for gas-liquid transmission in a gas diffusion layer, comprising:

[0028] A first acquisition module is used to acquire structural data of the gas diffusion layer after compression;

[0029] A second acquisition module is configured to acquire flow field data and configuration information of the target fluid in the gas diffusion layer; the configuration information includes collision equation data, migration equation data, and flow field boundary data;

[0030] A simulation module is used to perform simulation processing according to the structural data, the flow field data and the configuration information to obtain an equilibrium simulation result of the target fluid in the gas diffusion layer.

[0031] On the other hand, the present invention further provides a device comprising the above-mentioned prediction system for gas-liquid transmission in a gas diffusion layer.

[0032] On the other hand, the present invention also provides a storage medium, which stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by a processor to implement the method for predicting gas-liquid transmission in a gas diffusion layer as described in any one of the above.

[0033] The present application has the following beneficial effects:

[0034] 1、The present application can truly simulate the changes of the gas diffusion layer structure under different assembly conditions by combining the structure data of the compressed gas diffusion layer with the flow field data of the target fluid, and further accurately numerically simulates the transmission behavior of the gas-liquid two-phase in the compressed gas diffusion layer according to the structure data under the premise of fully considering the compression deformation and local carbon fiber fracture of the gas diffusion layer in the assembly compression process, thereby truly simulating the influence of different assembly conditions on the gas-liquid transmission behavior in the gas diffusion layer, and having high efficiency, accuracy, robustness and calculation precision, and being of great significance for optimizing the assembly conditions of the fuel cell and improving the "water flooding" phenomenon of the membrane electrode.

[0035] 2、The present application can accurately predict the influence of different assembly conditions on the microstructure of the gas diffusion layer by using the near-field dynamic method to update the initial structure data with the compression boundary data, and has high efficiency, fast speed and high calculation precision, and is also beneficial to improving the accuracy and reliability of subsequent prediction of the gas-liquid transmission behavior.

[0036] 3、The present application combines the pseudo-potential multi-component multi-phase LBM model of the lattice Boltzmann method to numerically predict the gas-liquid transmission phenomenon in the compressed gas diffusion layer, simulate the movement of oxygen, water vapor and liquid drops in the gas diffusion layer, and predict the occurrence of the "water flooding" phenomenon, and has high simulation accuracy and good robustness, and provides great help for optimizing the assembly conditions of the fuel cell and the gas-liquid transmission performance of the gas diffusion layer. BRIEF DESCRIPTION OF DRAWINGS

[0037] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the embodiments will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0038] Figure 1 The logical structure diagram of the prediction method of the gas diffusion layer gas-liquid transmission in one possible embodiment of the present application;

[0039] Figure 2 The logical structure diagram of the structure data acquisition method provided by the present application;

[0040] Figure 3 The example diagram of the micro scanning image of the gas diffusion layer in the present application;

[0041] Figure 4 The compression schematic diagram of the carbon fiber in the simulated gas diffusion layer in the present application;

[0042] Figure 5 A logical structure diagram of a simulation processing method provided by the present invention;

[0043] Figure 6 Schematic diagram of the contact angle between two components;

[0044] Figure 7 A logical structure diagram of a contact angle calculation method provided by the present invention;

[0045] Figure 8 A logical structure diagram of a method for calculating interaction force provided by the present invention;

[0046] Figure 9 A schematic diagram of grid points in a flow field in a possible embodiment of the present invention;

[0047] Figure 10 Schematic diagram of the structure of a prediction system for gas-liquid transmission in a gas diffusion layer in a possible embodiment of the present invention. DETAILED DESCRIPTION

[0048] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments, and therefore should not be understood as limiting the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0049] It should be noted that the terms "first", "second", etc. in the specification, claims, and drawings of the present invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way are interchangeable where appropriate so that the embodiments of the present invention can be implemented in an order other than the following diagrams or descriptions. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or inherent to these processes, methods, products, or devices.

[0050] In view of the current situation in the prior art where it is difficult to truly simulate the effects of different assembly conditions on the gas diffusion layer and the inability to accurately describe the transmission behavior of the gas-liquid two-phase in the microstructure, this embodiment provides a method for predicting gas-liquid transmission in the gas diffusion layer. By combining near-field dynamics with the pseudo-potential multi-component multi-phase LBM model of the lattice Boltzmann method, a joint simulation of the solid structure and fluid of the gas diffusion layer is achieved, and the flow of the gas-liquid two-phase fluid in the gas diffusion layer after assembly and compression can be simulated. The method for predicting gas-liquid transmission in the gas diffusion layer can also be applied to the prediction system for gas-liquid transmission in the gas diffusion layer of the embodiment of the present invention. First, the structural data of the gas diffusion layer after compression is obtained. The structural data is based on the gas diffusion layer. The initial structural data of the gas diffusion layer is updated based on near-field dynamics; then the flow field data and configuration information corresponding to the target fluid in the gas diffusion layer are obtained, where the configuration information includes collision equation data, migration equation data and flow field boundary data; finally, based on the flow field data, structural data and configuration information, simulation processing is performed based on the pseudo-potential multi-component multi-phase LBM model to simulate the evolution and separation of the gas-liquid two-phase target fluid, and the equilibrium state simulation results of the target fluid in the gas diffusion layer are obtained. The simulation process is accurate and efficient, with high calculation accuracy and good robustness. It can reflect the influence of different assembly conditions on the gas-liquid two-phase fluid transmission performance in the gas diffusion layer, thereby providing support for the performance optimization of fuel cells.

[0051] The technical solutions of the embodiments of the present invention are described in detail below. Figure 1 , the method comprising:

[0052] S101, obtaining structural data of a compressed gas diffusion layer.

[0053] Among them, the structural data is a type of simulated structural data, which is obtained by performing an evolutionary simulation of the gas diffusion layer based on peri-field dynamics. For details, please refer to Patent No. 202210500413.0; this structural data represents the simulated structure of the gas diffusion layer after assembly and compression, and matches the actual microstructure of the gas diffusion layer after assembly and compression, making the structural data more accurate and reliable, close to the microstructure of the gas diffusion layer under actual assembly conditions, which is beneficial to improving the accuracy and robustness of subsequent simulation processing.

[0054] In a possible implementation, the structure data includes geometric simulation data of the gas diffusion layer, representing the length, width and height of the real gas diffusion layer after assembly compression, which can be used to construct a flat solid boundary in the subsequent flow field; the structure data further includes porosity simulation data and carbon fiber fracture degree simulation data of the gas diffusion layer, reflecting the dynamic deformation and material damage of the porous medium of the gas diffusion layer caused by the assembly compression process, so as to obtain structure data close to the real gas diffusion layer after assembly compression, which is beneficial to improve the calculation accuracy.

[0055] In another possible implementation, the structure data includes model image data, which can clearly and intuitively display the simulation structure after assembly compression, has a high degree of visualization, is beneficial to timely find problems in the simulation development process, correct the simulation process, thereby reducing useless calculation and error rate, improving simulation reliability and accelerating research and development efficiency.

[0056] S103, obtaining flow field data of the target fluid in the gas diffusion layer and configuration information; the configuration information includes collision equation data, migration equation data and flow field boundary data.

[0057] In this embodiment, the pseudo-potential multi-component multiphase LBM model of the lattice Boltzmann method is used to effectively track the gas-liquid interface of the reaction product water, and the LBM model includes a Shan-Chen pseudo-potential multi-component multiphase LBM model (referred to as a pseudo-potential model). The pseudo-potential model introduces an interaction potential to represent the microscopic interaction force between different components, adds a source term of a potential function in the original collision equation to simulate the surface tension and realize the automatic evolution and separation between different components and phases, without the need for interface capture and tracking, which can better reflect the real physical properties. In addition, the LBM model is easy to perform parallel computing and is also easy to solve complex boundary conditions and multi-phase multi-component fluid, thereby improving the calculation efficiency and calculation accuracy of the prediction method of the present application.

[0058] In this step, an initial flow field model corresponding to the flow field can be first established according to the configuration information, so as to take the geometric structure corresponding to the structure data as a solid boundary to simulate the flow of the gas-liquid two-phase, that is, the flow field model can be initialized through the configuration information; wherein, in addition to the collision equation data, the migration equation data and the flow field boundary data, the configuration information further includes the initial flow field data, representing the initial physical state of the corresponding target fluid in the flow field corresponding to the compressed gas diffusion layer.

[0059] In a possible implementation, first, various configuration information is configured; when the fuel cell is in operation, the gas diffusion layer has a plurality of different fluids, including oxygen and water vapor in gas phase, and including reaction product water (or droplets) in liquid phase; in the configuration, the components σ of the two target fluids are configured to describe the components in the flow field with grid points (or lattice points), the initial radius of the droplets in the flow field region is configured to be 50 lattice units, i.e., R0=50 lattice units, the internal density of the droplets is 0.25 g / cm3, the external density is 0.045 g / cm3, and each fluid component σ satisfies the LBM evolution equation: 3 3

[0060]

[0061] wherein, is a density distribution function of the component σ; is a collision term of a multiple relaxation time model, representing a change in the density distribution of the component due to collision; is an external force term in the velocity space, representing a change in the density distribution of the component due to external force;e α is a discrete velocity vector, which is:

[0062]

[0063] wherein c=δx / δt is a lattice constant, δx is a grid step, δt is a time step, the grid steps in the x and y directions are the same, the lattice sound speed

[0064] Therefore, by configuring the solution range of the flow field, the initial fluid distribution function (i.e., the lattice sound speed), the initial densities of the two target fluid components, the initial velocities of the two target fluid components, and the fluid node type (gas phase or liquid phase), different types of initial settings can be made for different regions in the flow field to meet various simulation test requirements, which is highly flexible and widely applicable.

[0065] In addition, the LBM evolution equation and the initial flow field data can be used to obtain the density distribution function of the target fluid component at the next time step, i.e., in the S103 step, the flow field data is real-time changing, and the flow field data can be updated through simulation processing according to the configuration information.

[0066] In addition, the flow field boundary data represents the boundary constraint condition of the flow field where the target fluid is located, so as to constrain the motion behavior of the target fluid; in a possible implementation, the left and right boundaries of the flow field are configured as periodic boundaries, i.e., the fluid particles leaving from one side of the boundary will re-enter the flow field from the other side of the simulation region at the next time step; when the periodic boundary format is used, the mass and momentum in the flow field are strictly conserved in principle.​​

[0067] In one possible embodiment, the lower boundary of the flow field is a solid wall, which is configured as a non-slip boundary. A standard rebound format is used so that the velocity of the particles is reversed after colliding with the wall. In this standard rebound format, the solid boundary is located on the grid point, and no collision operation is performed on the fluid grid points on the boundary. The post-collision distribution function comes from the post-collision distribution function of the adjacent grid points.

[0068] In one possible implementation, the upper boundary of the flow field adopts a non-equilibrium extrapolation method, decomposing the distribution function on the boundary node into an equilibrium part and a non-equilibrium part, constructing a new equilibrium distribution according to specific boundary conditions, and determining the non-equilibrium part using an extrapolation method.

[0069] S105 , performing simulation processing according to the structural data, the flow field data, and the configuration information to obtain an equilibrium simulation result of the target fluid in the gas diffusion layer.

[0070] In this step, the simulation process is used to simulate the migration and collision process of the target fluid components, that is, to simulate the movement and evolution behavior of the target fluid in the gas diffusion layer after compression, and finally to solve the corresponding macro variables, which are the equilibrium simulation results; among them, migration refers to the movement of particles from one node to the adjacent node within a time step, and collision refers to the collision of fluid particles and the change of the particle distribution function fα→fα′ under the premise of conservation of mass, momentum and energy.

[0071] After initialization, the target fluid particles in the flow field migrate in the discrete velocity directions of the grid points, and collision migration steps occur at the grid points:

[0072] Migration step: f ασ (x+e α δt, t+δt)=f ασ′ (x,t)

[0073] Collision step: f ασ′ (x,t)=f ασ (x, t) + Ω α (f)

[0074] Among them, x represents the current calculation grid point, xe α δt represents the discrete velocity direction e α The previous grid point, x+e α δt represents the discrete velocity direction e α In the migration step, the grid point x only exchanges information with its adjacent grid points.

[0075] The simulation process is a cyclic iteration process. The equilibrium state simulation result represents the stable state of the target fluid flowing in the compressed gas diffusion layer. For example, when the simulation result shows that the droplet no longer has a dramatic change, and only has a weak pulse on the solid surface until it is stationary on the solid surface, it represents that the target fluid evolves to an equilibrium state (or stable state), and the equilibrium state simulation result can be output. The prediction method can truly predict the flow behavior and transport characteristics of the gas-liquid two-phase in the compressed gas diffusion layer. The equilibrium state simulation result can be analyzed and researched, thereby providing a useful reference for optimizing the gas diffusion layer.

[0076] The steps S103-S105 combine the Shan-Chen pseudo-potential multi-component multiphase model of the lattice Boltzmann method to numerically predict the gas-liquid transport phenomenon in the compressed gas diffusion layer, simulate the movement of oxygen, water vapor and droplets in the gas diffusion layer, and predict the occurrence of the "water flooding" phenomenon. The simulation has high accuracy and good robustness, and provides great help for optimizing the fuel cell assembly conditions and the gas-liquid transport performance of the gas diffusion layer.

[0077] Specifically, in one possible implementation, as shown in Figure 2 The step of obtaining the structure data of the compressed gas diffusion layer, i.e., the step S101, includes:

[0078] S202, obtaining initial structure data of the gas diffusion layer.

[0079] The initial structure data includes real anisotropic geometric morphology data of the gas diffusion layer. The geometric morphology data of the gas diffusion layer can be obtained by scanning imaging to realize real and accurate simulation of the porous medium of the gas diffusion layer, and has high reliability. In one possible implementation, the scanning imaging can be selected from synchrotron X-ray microscopic imaging and electron microscope scanning imaging. The present application does not limit this, and different scanning imaging methods can be selected according to different actual conditions.

[0080] In one optional implementation, when the initial structure data is obtained, the gas diffusion layer is tomographically scanned to obtain micro scanning image data of the gas diffusion layer. Then, the micro scanning image data is reconstructed to obtain the initial structure data.

[0081] As shown in Figure 3As shown, the microscopic scanning image data includes multiple two-dimensional tomographic scanning image data of the gas diffusion layer, and then reconstruction processing is performed based on the multiple two-dimensional tomographic scanning image data to obtain initial structural data corresponding to the gas diffusion layer; in this embodiment, the reconstruction processing can be selected as a reconstruction algorithm such as grayscale value; in addition, in a possible implementation, before the reconstruction processing is performed, the microscopic scanning image data of the gas diffusion layer can also be preprocessed, and the preprocessing can include processing methods such as noise reduction and shadow removal to make the microscopic scanning image data more reliable, which is conducive to improving the accuracy of the initial structural data.

[0082] S204 , obtaining compression boundary data; the compression boundary data represents the compression condition applied to the gas diffusion layer.

[0083] For the gas diffusion layer, the actual preload force it is subjected to during assembly comes from the internal structure of the fuel cell, such as the catalyst layer and bipolar plate adjacent to the gas diffusion layer. In this step, a simulation is performed based on near-field dynamics, that is, the near-field effects on each discrete point within the gas diffusion layer. The compression conditions applied to the gas diffusion layer are represented by pre-configured compression boundary data to simulate the near-field effects on the gas diffusion layer when the preload force is applied to the gas diffusion layer. In one possible embodiment, the compression boundary data includes displacement constraint parameters, velocity constraint parameters, and external load constraint parameters at the boundary of the gas diffusion layer to effectively simulate the process of transmitting the compression conditions (constraints, i.e., preload force) applied to the boundary to the gas diffusion layer.

[0084] S206: Obtain the compressed structure data according to the compressed boundary data and the initial structure data.

[0085] like Figure 4 As shown, in this step, referring to Patent No. 202210500413.0, based on perifield dynamics, the near-field force in the simulation structure can be calculated according to the compression boundary data and the initial structure data, and then the porosity and carbon fiber fracture degree of the simulation structure after compression are calculated and iterated according to the near-field force, and the compressed structure data is obtained by continuous updating, which is efficient, accurate and robust.

[0086] The near-field dynamic method is a solid simulation method based on local interaction force. The method introduces the non-local idea in expressing the internal interaction of the material, that is, it is assumed that there is interaction between any material point in the space and other material points within a certain radius around the material point. Since the method converts the partial differential equation form of the traditional continuous solid motion into an integral form, it is free from the limitations of the traditional method in non-continuous problems in principle, that is, the deformation and carbon fiber fracture of the porous medium can be directly simulated without introducing additional assumptions. The near-field dynamic method is used to simulate the deformation of the gas diffusion layer porous medium during the assembly process of the fuel cell, and the porosity of the compressed porous medium and the damage degree of the carbon fiber can be calculated.

[0087] In one possible implementation, the step specifically can further include:

[0088] According to the compression boundary data and the initial structure data, the stretching amount of the bond between the material points is obtained;

[0089] According to the stretching amount of the bond, the near-field action force on the material point is calculated;

[0090] According to the near-field action force, the displacement of the material point is calculated to update the initial structure data, and the structure data is obtained.

[0091] The material point is a scientific abstraction of an actual object, and the set of material points represents the microstructure of the gas diffusion layer. The bond is a concept introduced in the near-field dynamic theory, which represents the correlation between two material points within a certain near-field range, and can also be understood as the interaction between two material points within a certain near-field range. In this embodiment, the bond can be represented by the relative position vector of the two material points in geometry.

[0092] The stretching amount of the bond represents the deformation of the bond, and the near-field action force can be calculated according to the relative position of the two material points. Further, the material point has a moving trend under the near-field action force, and the displacement of the material point can be calculated according to the near-field action force, so as to obtain the position of the material point at the next moment. The deformation of the gas diffusion layer during the assembly compression process is mainly reflected in the change of the position of each material point, and the structure data (mainly the geometric simulation data) of the compressed gas diffusion layer can be obtained by combining the displacement of each material point generated by the near-field dynamics with the initial data, which has high calculation accuracy and good reliability.

[0093] In one possible implementation, after the stretching amount of the bond between the material points is obtained, the S206 step further includes:

[0094] According to the stretching amount of the bond, the carbon fiber fracture degree of the gas diffusion layer after assembly compression is calculated.

[0095] Wherein, according to the stretching amount of the bond, that is, the deformation condition of the bond, the state of the bond (broken or not broken) can be obtained, and then according to the state of the bond, the broken bond proportion of the material point, that is, the total proportion of the broken bond in all bonds between the single material point and all material points in the surrounding field range, is calculated; and the total damage amount of the gas diffusion layer is the sum of the local damage of each material point, so that the broken bond proportion of each material point is added, that is, the total carbon fiber fracture degree of all material points can be obtained.

[0096] In one possible implementation, the step S206 further includes:

[0097] According to the geometric simulation data, the porosity of the gas diffusion layer after assembly compression is calculated.

[0098] In this embodiment, according to the geometric simulation data, the simulation volume of the compressed gas diffusion layer can be obtained, so that the porosity is calculated according to the volume change, which provides more accurate numerical support for subsequent simulation processing, and is beneficial to improve the simulation accuracy and robustness of the prediction method.

[0099] The step S101 adopts the near-field dynamic method to update the initial structure data with the compression boundary data, which can accurately predict the influence of different assembly conditions on the microstructure of the gas diffusion layer, is efficient and fast, has high calculation accuracy, and is also beneficial to improve the accuracy and reliability of subsequent prediction of gas-liquid transport behavior.

[0100] Specifically, in one possible implementation, as shown in Figure 5 According to the structure data, the flow field data and the configuration information, simulation processing is performed to obtain the equilibrium state simulation result of the target fluid in the gas diffusion layer, that is, the step S105 includes:

[0101] S501, according to the structure data, the flow field data and the configuration information, a dynamic intermediate simulation result is obtained; the dynamic intermediate simulation result includes the contact angle of the target fluid.

[0102] S503, in the case that the contact angle meets the preset condition, the equilibrium state simulation result is obtained according to the dynamic intermediate simulation result corresponding to the contact angle.

[0103] As described above, the simulation process is a process of iterative loop, and before the equilibrium state simulation result is obtained, a plurality of dynamic intermediate simulation results are obtained, which represent the dynamic flow state of the target fluid in the gas diffusion layer at a corresponding time step, and are the dynamic flow state of the target fluid before the target fluid reaches the equilibrium state, reflecting the flow trend of the target fluid. Each time step corresponds to a dynamic intermediate simulation result, and the change between the dynamic intermediate simulation results corresponding to two time steps respectively represents the evolution and migration of the target fluid between the two time steps. When the target fluid evolves to the equilibrium state, the dynamic intermediate simulation result corresponding to the time step at this time is the equilibrium state simulation result, which can truly reflect the final distribution of the target fluid in the gas diffusion layer after compression, thereby providing a useful reference for optimizing the fuel cell.

[0104] The dynamic intermediate simulation result includes a contact angle of the target fluid corresponding to the current time step. The contact angle can reflect whether the dynamic flow state of the target fluid reaches the equilibrium state to a certain extent. When the contact angle meets the preset condition, it is determined that the evolution process of the target fluid has reached the equilibrium state, and the dynamic intermediate simulation result corresponding to the contact angle is output as the equilibrium state simulation result.

[0105] As shown in Figure 6 , the contact angle refers to the included angle θ between the tangent of the gas-liquid interface made at the solid-liquid-gas three-phase junction and the solid-liquid junction line, which is a measure of the wetting degree. The wetting process is related to the interfacial tension, and the contact angle can be calculated by the interfacial tension. Assuming that the target fluid in the flow field includes a first target fluid and a second target fluid, the Young's equation for calculating the contact angle of the first target fluid is:

[0106]

[0107] Wherein, σ s,1 is the interfacial tension value between the first target fluid and the solid surface, σ s,2 is the interfacial tension value between the second target fluid and the solid surface, and σ 1,2 is the interfacial tension value between the first target fluid and the second target fluid.

[0108] Specifically, in one possible implementation, as shown in Figure 5 , after obtaining the dynamic intermediate simulation result according to the structure data, the flow field data and the configuration information, i.e. after S501, the method further comprises:

[0109] S505, in the case that the contact angle does not satisfy the preset condition, updating the flow field data according to the dynamic intermediate simulation result corresponding to the contact angle, and returning to execute the obtaining of the dynamic intermediate simulation result according to the structure data, the flow field data and the configuration information.

[0110] The contact angle not satisfying the preset condition indicates that the flow state of the target fluid in the compressed gas diffusion layer has not reached an equilibrium state, and the physical state of the target fluid will continue to change, that is, the dynamic intermediate simulation result will also change with the time step, and the flow field data will also change with the time step. Therefore, after obtaining the judgment result that the contact angle does not satisfy the preset condition, the flow field data is updated according to the dynamic intermediate simulation result obtained in the S501 step to obtain updated flow field data. For example, taking the initial flow field data as an example, the time step corresponding to the initial flow field data is the initial time step. After the first round of iterative calculation, the flow field data corresponding to the next time step which is one time step length away from the initial time step is obtained, and the flow field data is taken as the initial flow field data for the next round of iterative calculation. In this way, the flow field data corresponding to the target time step is obtained.

[0111] After obtaining the updated flow field data each time, the S501 step is repeatedly executed according to the updated flow field data, so that the simulated target fluid continues to evolve according to the configured migration equation and collision equation, the dynamic intermediate simulation result corresponding to the updated flow field data is obtained, and the contact angle in the updated dynamic intermediate simulation result is compared with the preset condition again. The S503 or S505 step is repeatedly executed to complete the cycle iteration process, and the dynamic intermediate simulation result is continuously updated.

[0112] Specifically, in one possible implementation, the S505 step includes:

[0113] In the case that the change amount of the contact angle is greater than the preset threshold, the flow field data is updated according to the dynamic intermediate simulation result corresponding to the contact angle, and the S501 step is returned to execute the obtaining of the dynamic intermediate simulation result according to the structure data, the flow field data and the configuration information.

[0114] The change amount of the contact angle is obtained according to the difference between the contact angle corresponding to the dynamic intermediate simulation result before updating the flow field data and the contact angle corresponding to the dynamic intermediate simulation result after updating the flow field data. That is, the contact angles obtained in two adjacent cycle iteration processes are compared to obtain the difference between the two contact angles, that is, the change amount of the contact angle.

[0115] When the difference between the two adjacent contact angles is less than or equal to the preset threshold, it is considered that the contact angle meets the preset condition at this time, which represents that the droplet no longer has a dramatic change and has a weak pulse on the object surface until it is stationary on the object surface, reaching a relatively stable equilibrium state, and then S503 step is executed; when the difference between the two adjacent contact angles is greater than the preset threshold, it is considered that the contact angle does not meet the preset condition at this time, which represents that the droplet will still have a dramatic change, and the target fluid in the flow field has not evolved to the equilibrium state, and then S505 step is executed to continue the loop iteration.

[0116] Specifically, in one possible implementation, as shown in Figure 7 the S501 step includes:

[0117] S701, obtaining the interaction force of the target fluid according to the structure data, the flow field data and the configuration information.

[0118] S703, obtaining the contact angle corresponding to the target fluid according to the interaction force of the target fluid.

[0119] Specifically, in one possible implementation, as shown in Figure 8 the S701 step includes:

[0120] S802, obtaining the macroscopic quantity of the target fluid according to the structure data, the flow field data and the configuration information; the macroscopic quantity includes macroscopic density, macroscopic velocity, macroscopic pressure and macroscopic temperature.

[0121] S804, obtaining the interaction force according to the macroscopic quantity.

[0122] The S701-S703 steps and the S802-S804 steps are part of a single round of iteration process, the contact angle can be calculated by the interfacial tension, and the interaction force calculated in the S701 step is the interfacial tension; further, the interaction force can be calculated by the macroscopic quantity, so that the macroscopic quantity is calculated by the structure data, the flow field data and the configuration information, and then the interaction force is calculated according to the macroscopic quantity, so that the contact angle is finally calculated, and the calculation precision is high.

[0123] The macroscopic quantity is a kind of simulation data, which is a macroscopic manifestation of the micro features of the target fluid, represents the macroscopic physical quantity of the real target fluid in the gas diffusion layer, and includes macroscopic density, macroscopic velocity, macroscopic pressure and macroscopic temperature. Figure 9As shown, each grid point in the flow field is composed of 9 directions, and the macroscopic density is the sum of the distribution functions of the 9 directions. The distribution functions in each direction can be obtained according to the flow field data, so as to obtain the macroscopic density ρ σ The calculation formula is:

[0124]

[0125] Wherein, f a,σ is the density distribution function of component σ in S103.

[0126] And the velocity v of each grid point is obtained by dividing the sum of the distribution functions of the 9 directions of the grid point by the density of the grid point. The solving equation of the velocity v is:

[0127]

[0128] Wherein, F c,σ is the interaction force between fluids, and F ads,σ is the interaction force between fluid and solid surface.

[0129] Specifically, the interaction force includes the interaction force between target fluids (referred to as fluid-fluid interaction force F c,σ ), and the interaction force between target fluid and solid surface (referred to as fluid-solid interaction force F ads,σ ); and the fluid-fluid interaction force F c,σ includes the interaction force between the same components and the interaction force between different components, which can be expressed as:

[0130]

[0131] Wherein, σ and represent two different target fluids (i.e. two different components); G σσ and represent the interaction strength between the same components and the different components, respectively. When G is negative, it is an attractive force, and when G is positive, it is a repulsive force. The interaction strength (including G σσ and ) can be calculated by macroscopic quantities; w(|e α | 2 ) is the weight of the particle, w(1)=1 / 3, w(2)=1 / 12; ψ(x) is the pseudo potential function.

[0132] The fluid-solid interaction force F ads,σ is used to adjust the wall wettability, and is expressed as:

[0133]

[0134] where the indicator function s(x+e α t) equals 1 and 0 for solid and fluid nodes, respectively; G ads,σ is an adjusting parameter related to fluid components, for example, the adjusting parameter includes viscosity parameters corresponding to target fluid, and the strength of interaction between each target fluid component and solid wall (i.e. F ads,σ ) can be realized by changing the adjusting parameter G ads,σ .

[0135] In addition, the pseudo potential function of each target fluid can be obtained by introducing a real equation of state:

[0136]

[0137] where p EOS is the equation of state, in the embodiment, the ideal gas equation of state is selected to describe ideal fluid, and the c s equation of state is selected to describe non-ideal fluid.

[0138] where the adjusting parameter G ads,σ in the interaction strength can be determined by the relationship among G σσ , and G ads,σ ; in the multi-component model, a target fluid node is selected, it is assumed that the node is a single-component pure fluid node, and the adjacent nodes of the node have the same density as the node itself, then when the interaction force is calculated, the calculation formula of the fluid-fluid interaction force F c,σ is simplified as:

[0139]

[0140] and the interaction force F ads,σ of the fluid node completely surrounded by the solid surface is:

[0141]

[0142] In addition, when the tangent of the contact angle passes through the grid point, the pseudo potential function of the grid point at the fluid interface is:

[0143]

[0144] The pseudo potential function of the grid point at the fluid-solid interface of different components is:

[0145]

[0146] For example, when the contact angle is 45°, as shown in Figure 9As shown, component σ = 1, 2; on the surface completely wetted by liquid, the adhesion between solid and liquid in the projection to y direction equals the cohesion of liquid, giving the formula of the first target fluid, i.e. component 1:

[0147] n y ·F c,1 +n y ·F ads,1 =0

[0148] Combining the above formula with the formula of the interfacial interaction force F c,σ and the interaction force F ads,σ , the parameter relationship formula can be obtained:

[0149]

[0150] Similarly, the parameter relationship formula of the second target fluid, i.e. component 2, is:

[0151]

[0152] Then through the above steps, the parameter relationship formula of the acting strength in the multi-component model with the contact angle of 0, 90 and 180 degrees can also be calculated, and the relationship among G σσ , and G ads,σ is:

[0153]

[0154] Wherein, a is an indefinite constant, then according to the formula group, the value of the appropriate interaction strength can be selected for calculating the size of the contact angle.

[0155] Through the above steps, first, the flow field model is initialized, the compressed structure data of the gas diffusion layer is imported into the flow field model, the flow field boundary data, the flow field initial data, the migration equation data and the collision equation data are set, then the flow field model is run, the simulation processing is performed, the migration and collision process of the target fluid in the compressed gas diffusion layer is simulated, the corresponding macroscopic quantity at each time step is calculated, then the interaction force is calculated according to the macroscopic quantity, the contact angle is calculated, and then the stability is judged according to the contact angles calculated by the adjacent two iterations, and finally the equilibrium state simulation result is output according to the judgment result, which has high precision, good robustness and high reference value, and can provide great help for optimizing the performance of the fuel cell; it can be seen that, by combining the compressed structure data of the gas diffusion layer with the flow field data of the target fluid, the change of the structure of the gas diffusion layer under different assembly conditions can be truly simulated, and on the premise of fully considering the compression deformation and local carbon fiber fracture of the gas diffusion layer in the assembly compression process, the transmission behavior of the gas-liquid two-phase in the compressed gas diffusion layer is further accurately simulated according to the structure data, so that the influence of different assembly conditions on the gas-liquid transmission behavior in the gas diffusion layer is truly simulated, which is efficient, accurate, robust and has high calculation precision, and has important significance for optimizing the assembly conditions of the fuel cell and improving the "water flooding" phenomenon of the membrane electrode.

[0156] Corresponding to the above-mentioned gas diffusion layer gas-liquid transmission prediction method provided by the embodiment of the present application, the embodiment of the present application also provides a gas diffusion layer gas-liquid transmission prediction system. Since the gas diffusion layer gas-liquid transmission prediction system provided by the embodiment of the present application corresponds to the gas diffusion layer gas-liquid transmission prediction methods provided by the above-mentioned several embodiments, the embodiments of the aforementioned gas diffusion layer gas-liquid transmission prediction method are also applicable to the gas diffusion layer gas-liquid transmission prediction system provided by the embodiment of the present application, which will not be described in detail in this embodiment.

[0157] The gas diffusion layer gas-liquid transmission prediction system provided by the embodiment of the present application can realize the gas diffusion layer gas-liquid transmission prediction method in the above-mentioned method embodiment, as shown in the figure, the system can include: Figure 10

[0158] The first acquisition module 1010 is used for acquiring the compressed structure data of the gas diffusion layer.

[0159] The second acquisition module 1020 is used for acquiring the flow field data and configuration information of the target fluid in the gas diffusion layer; the configuration information includes collision equation data, migration equation data and flow field boundary data.

[0160] The simulation module 1030 is used for performing simulation processing according to the structure data, the flow field data and the configuration information, to obtain the equilibrium state simulation result of the target fluid in the gas diffusion layer. ​

[0161] In one possible implementation, the simulation module 1030 further includes:

[0162] an intermediate state calculation module configured to obtain a dynamic intermediate simulation result according to the structure data, the flow field data and the configuration information; the dynamic intermediate simulation result includes a contact angle of the target fluid;

[0163] a stability judgment module configured to, in a case where the contact angle satisfies a preset condition, obtain the equilibrium state simulation result according to the dynamic intermediate simulation result corresponding to the contact angle.

[0164] In another possible implementation, the simulation module 1030 further includes:

[0165] an iteration calculation module configured to, in a case where the contact angle does not satisfy the preset condition, update the flow field data according to the dynamic intermediate simulation result corresponding to the contact angle, and return to execute the obtaining of the dynamic intermediate simulation result according to the structure data, the flow field data and the configuration information.

[0166] It should be noted that the system provided in the above embodiments, in realizing its functions, only divides the above-mentioned function modules by way of example, and in actual application, the above-mentioned functions can be completed by different function modules according to needs, that is, the internal structure of the device is divided into different function modules to complete all or part of the above-described functions. In addition, the system and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process is detailed in the method embodiments, which will not be described here.

[0167] The embodiments of the present application also provide a device including the above-mentioned gas diffusion layer gas-liquid transport prediction system, integrated in a controller of the device, which can include a processor and a memory, and the memory stores at least one instruction or at least one program, which is loaded and executed by the processor to realize the above-mentioned gas diffusion layer gas-liquid transport prediction method.

[0168] Among them, the processor (or CPU (Central Processing Unit, Central Processing Unit)) is the core component of the gas diffusion layer gas-liquid transport prediction system, and its main function is to interpret the memory instructions and process the data fed back by each module; the structure of the processor is roughly divided into arithmetic logic components and register components, etc., the arithmetic logic components mainly perform related logical calculations (such as shift operations, logical operations, fixed-point or floating-point arithmetic operation operations and address operations, etc.), and the register components are used to temporarily store instructions, data and addresses.

[0169] The memory is a memory device, which can be used to store software programs and modules. The processor executes various function applications and data processing by running the software programs and modules stored in the memory. The memory can mainly include a program storage area and a data storage area. The program storage area can store operating systems, application programs required for functions, etc. The data storage area can store data created according to the use of the device, etc. Correspondingly, the memory can also include a memory controller to provide the processor with access to the memory.

[0170] The embodiment of the present application also provides a medium, which stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by a processor to realize the above-mentioned gas diffusion layer gas-liquid transmission prediction method. Optionally, the medium can be located in at least one network server in a plurality of network servers of a computer network. In addition, the medium can include but is not limited to random access memory (RAM), read-only memory (ROM), a U disk, a mobile hard disk, a disk storage device, a flash memory device, other volatile solid-state storage devices and various storage media that can store program codes.

[0171] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description, not representing the advantages and disadvantages of the embodiments. The above-mentioned embodiments of the present application are described. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be executed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are possible or can be advantageous.

[0172] Each of the embodiments in the present specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other. Each embodiment mainly describes the differences from other embodiments. Especially, the device embodiments are described simply because they are basically similar to the method embodiments, and the relevant parts can be referred to the part of the method embodiments.

[0173] The above description is only some embodiments of the present application and is not used to limit the present application. Those skilled in the art should understand that the present application can have various changes and improvements. Any modification, equivalent replacement and improvement made according to the present application all fall within the scope of the present application.

Claims

1. A method of predicting gas diffusion layer gas-liquid transport, comprising: A pseudo-potential multi-component multiphase LBM model combining near-field dynamics and lattice Boltzmann method, comprising: Obtaining structure data of the compressed gas diffusion layer; the structure data is obtained by updating initial structure data of the gas diffusion layer based on near-field dynamics; Obtaining flow field data and configuration information of target fluid in the gas diffusion layer; the configuration information includes collision equation data, migration equation data and flow field boundary data; the flow field boundary data represents the boundary constraint condition of the flow field where the target fluid is located, so as to constrain the motion behavior of the target fluid; According to the structure data, the flow field data and the configuration information, a dynamic intermediate simulation result is obtained; the dynamic intermediate simulation result represents the dynamic flow state of the target fluid in the gas diffusion layer when it is not in equilibrium state at a corresponding time step, and the dynamic intermediate simulation result includes the contact angle of the target fluid; In the case that the contact angle does not satisfy the preset condition, the flow field data is updated according to the dynamic intermediate simulation result corresponding to the contact angle, and the step of obtaining the dynamic intermediate simulation result according to the structure data, the flow field data and the configuration information is returned to execute; In the case that the contact angle satisfies the preset condition, an equilibrium state simulation result is obtained according to the dynamic intermediate simulation result corresponding to the contact angle.

2. The prediction method of claim 1, wherein, The obtaining of the structure data of the compressed gas diffusion layer comprises: Obtaining initial structure data of the gas diffusion layer; Obtaining compression boundary data; the compression boundary data represents the compression condition applied to the gas diffusion layer; According to the compression boundary data and the initial structure data, the structure data after compression is obtained.

3. The prediction method of claim 1, wherein, The obtaining of the dynamic intermediate simulation result according to the structure data, the flow field data and the configuration information comprises: According to the structure data, the flow field data and the configuration information, the interaction force of the target fluid is obtained; According to the interaction force of the target fluid, the contact angle corresponding to the target fluid is obtained.

4. The prediction method of claim 3, wherein, The obtaining of the interaction force of the target fluid according to the structure data, the flow field data and the configuration information comprises: According to the structure data, the flow field data and the configuration information, the macroscopic quantity of the target fluid is obtained; the macroscopic quantity includes macroscopic density, macroscopic velocity, macroscopic pressure and macroscopic temperature; According to the macroscopic quantity, the interaction force is obtained.

5. The prediction method of claim 1, wherein, The updating of the flow field data according to the dynamic intermediate simulation result corresponding to the contact angle in the case that the contact angle does not satisfy the preset condition, and the return to execute the step of obtaining the dynamic intermediate simulation result according to the structure data, the flow field data and the configuration information comprises: In a case where the variation of the contact angle is greater than a preset threshold, the flow field data is updated according to the dynamic intermediate simulation result corresponding to the contact angle, and the method of obtaining the dynamic intermediate simulation result according to the structure data, the flow field data and the configuration information is executed again; the variation is obtained according to the difference between the contact angle corresponding to the dynamic intermediate simulation result before the flow field data is updated and the contact angle corresponding to the dynamic intermediate simulation result after the flow field data is updated.

6. A system for predicting gas diffusion layer gas-liquid transport, comprising: A pseudo-potential multi-component multiphase LBM model combining near-field dynamics and lattice Boltzmann method, comprising: A first obtaining module is configured to obtain structure data of a compressed gas diffusion layer; the structure data is obtained by updating initial structure data of the gas diffusion layer based on near-field dynamics; A second obtaining module is configured to obtain flow field data of a target fluid in the gas diffusion layer and configuration information; the configuration information includes collision equation data, migration equation data and flow field boundary data; The simulation module comprises an intermediate state calculation module, an iterative calculation module and a stability judgment module; The intermediate state calculation module is configured to obtain a dynamic intermediate simulation result according to the structure data, the flow field data and the configuration information; the dynamic intermediate simulation result includes a contact angle of the target fluid; The iterative calculation module is configured to, in a case where the contact angle does not satisfy a preset condition, update the flow field data according to the dynamic intermediate simulation result corresponding to the contact angle, and return to execute the method of obtaining the dynamic intermediate simulation result according to the structure data, the flow field data and the configuration information; The stability judgment module is configured to, in a case where the contact angle satisfies the preset condition, obtain an equilibrium state simulation result according to the dynamic intermediate simulation result corresponding to the contact angle.

7. An apparatus comprising the gas diffusion layer gas-liquid transport prediction system of claim 6.

8. A storage medium, characterized by The storage medium stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the gas diffusion layer gas-liquid transport prediction method of any one of claims 1-5.

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