Parametric modeling method for proton exchange membrane porous transport layers

Abstract

A parameterized modeling method of proton exchange membrane porous transport layer, the method comprising: fixing the number of sub-layers of the porous transport layer and the overall porosity and target fiber diameter; determining the distribution characteristics of fiber diameter between different sub-layers and the porosity within the layers, and obtaining the fiber diameter and porosity parameters of each sub-layer; determining the cuboid domain accommodating the transport layer and the fiber center height of each layer according to the volume parameters of the porous transport layer; iteratively calculating at the corresponding height of the fiber center of each sub-layer, randomly generating two points on the fiber axis and stretching and cutting the fiber in the cuboid domain until the volume after cutting in each layer meets the target porosity; reconstructing a cuboid domain with the same volume parameters, and subtracting the cuboid domain after the cutting operation through Boolean operation to obtain the porous transport layer sub-layers with overlapping fibers in each layer; moving each fiber sub-layer at a specific interval to form the overall porous transport layer according to the required operating conditions.

Description

Technical Field

[0001] This disclosure pertains to the field of proton exchange membrane electrolyzers, and specifically to a parametric modeling method for porous transport layers of proton exchange membranes. Background Technology

[0002] Proton exchange membrane (PEM) electrolysis of water to produce hydrogen is a crucial solution for alleviating energy shortages and the greenhouse effect. As the core component of a PEM electrolyzer, the hydrothermal regulation, heat dissipation and oxygen removal, as well as conductivity and structural support, are all closely related to the structural characteristics of the porous transport layer. An improperly designed porous transport layer can easily lead to oxygen retention in the flow channels, increasing mass transfer resistance; it can also cause uneven current density distribution in the electrolyzer, reducing the lifespan of the battery module; and further, it may cause excessive local thermal stress in the proton exchange membrane, leading to performance degradation problems such as membrane thermal perforation.

[0003] To study porous transport layers with optimal overall performance, it is necessary to realistically reconstruct the porous transport layer for structural optimization. However, manual modeling cannot obtain porous transport layers with random fiber orientation and specific porosity. It is necessary to comprehensively consider factors such as the structural requirements of sintered titanium diffusion layers, hydrophilicity and compressibility characteristics, as well as porosity and fiber distribution characteristics, and develop an automated modeling method based on parametric algorithms to reconstruct the structural characteristics of realistic porous transport layers, so as to further analyze the performance of porous transport layers in electrolytic cells.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of the present invention, and therefore may contain information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the purpose of this disclosure is to propose a parameterized modeling method for porous transport layers of proton exchange membranes. This method enables customized control of the porosity and fiber diameter distribution of each layer, and comprehensively considers the compression and hydrophilic operation conditions of the porous transport layer as well as the internal pore characteristics. The method has clear physical meaning and is quick to operate.

[0006] To achieve the above objectives, this disclosure provides the following technical solutions:

[0007] A parameterized modeling method for porous transport layers of proton exchange membranes includes the following steps:

[0008] The first step is to fix the number of sublayers L of the porous transport layer in the proton exchange membrane electrolyzer. all and the overall porosity ε all and the target fiber diameter D all ;

[0009] The second step is to determine the distribution characteristics of porosity and fiber diameter of different sublayers, and to establish linked lists to store the fiber diameter of each sublayer and linked lists to store the porosity of each sublayer.

[0010] The third step involves establishing a cuboid domain B1 to accommodate all the porous transport layer fibers. Domain B1 has a square base, a width of W, and a height of H. Simultaneously, a height list for the center of each sublayer fiber is determined.

[0011] The fourth step involves using the random function `random` from the `random` library of Python to generate the coordinates of two random points at the corresponding heights of the fiber centers in each sublayer. The line connecting these two random points is then used as the fiber axis. A section of material with diameter D is then stretched and removed within the cuboid domain B1. i The fibers are generated until the porosity of the sublayer meets the porosity requirement of the layer. After the fibers are removed, the cuboid domain becomes the cuboid domain B1'.

[0012] The fifth step is to reconstruct a complete cuboid domain B2 with a square bottom, a width of W, and a height of H. Then, by subtracting the cuboid domain B1' (which has had its fibers removed) from the cuboid domain B2 using Boolean operations, the separated porous fiber sublayers are obtained.

[0013] The sixth step involves moving each porous fiber sublayer at a specific interval according to different operating conditions to form an integral porous transport layer.

[0014] In the parametric modeling method for the porous transport layer of the proton exchange membrane, the porosity distribution characteristics include single-parameter distribution, gradient distribution, Gaussian distribution, and log-normal distribution.

[0015] In the parametric modeling method for the porous transport layer of the proton exchange membrane, the distribution characteristics of the fiber diameter include single-parameter distribution, gradient distribution, Gaussian distribution, and log-normal distribution.

[0016] In the parametric modeling method for the porous transport layer of the proton exchange membrane, the normalvariate function of the random library in Python is called based on the overall porosity ε. all Automatically obtain the initial porosity distribution linked list And calling the `normalvariate` function from the Python language's `random` library based on the target fiber diameter D. all Automatically obtain the distribution list of initial fiber diameters

[0017] In the parametric modeling method for the porous transport layer of the proton exchange membrane, the mean ε of the elements in the initial porosity distribution linked list is calculated. ave The mean D of the distribution list elements of the initial fiber diameterave The corrected porosity chain and fiber diameter chain are calculated as follows: and

[0018] In the parametric modeling method for the porous transport layer of the proton exchange membrane, the height H of the cuboid satisfies:

[0019] In the parametric modeling method for the porous transport layer of the proton exchange membrane, the sixth step involves setting a movement distance chain for each porous fiber layer based on actual operating conditions. Move the i-th porous fiber sublayer by S i This ensures that the porous fiber layers are tangent, separate, or intersecting.

[0020] In the parametric modeling method for the porous transport layer of the proton exchange membrane, the fiber layer with tangential characteristics represents the porous transport layer without coating hydrophilic material, the fiber layer with phase separation characteristics represents the porous transport layer with coating hydrophilic material, and the fiber layer with intersecting characteristics represents the porous transport layer without coating hydrophilic material and after compression.

[0021] In the parametric modeling method of the porous transport layer of the proton exchange membrane, the intralayer fiber axis of each sublayer is generated by two random points, the fiber diameter is twice the width of the cross-section of the cuboid domain, the fibers can intersect and overlap, and when stretched and cut, they penetrate the outer boundary surface of the cuboid domain.

[0022] In the parametric modeling method for the porous transport layer of the proton exchange membrane, the porosity elements of each sublayer in the porosity chain can be rearranged.

[0023] Compared with the prior art, the beneficial effects of this disclosure are as follows:

[0024] Customized control of porosity and fiber diameter distribution in each sublayer of the porous transport layer is achieved. The porous diffusion layer reconstructed in this disclosure comprehensively considers the compression and hydrophilic operation conditions of the porous transport layer as well as the internal pore characteristics. It has a clear physical meaning and can quickly generate porous transport layers with different compression and hydrophilic / hydrophobic properties. Attached Figure Description

[0025] Various other advantages and benefits of this disclosure will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiments below. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this disclosure. It is obvious that the drawings described below are merely some embodiments of this disclosure, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. Furthermore, the same reference numerals denote the same parts throughout the drawings.

[0026] In the attached diagram:

[0027] Figure 1 This is a schematic diagram of the iterative solution process of the parameterized modeling method for porous transport layers of proton exchange membranes proposed in this invention;

[0028] Figure 2 A schematic diagram of the overall cuboid domain for accommodating fibers and fluids;

[0029] Figure 3 A schematic diagram of the remaining cuboid domain structure after the first layer of fibers has been removed;

[0030] Figure 4 A schematic diagram of the cuboid domain structure after all fibers were removed to meet the porosity requirements.

[0031] Figure 5 A front view of the porous transport layer obtained after Boolean operations to meet porosity requirements;

[0032] Figure 6 A schematic diagram of the complete structure of the porous transport layer obtained by Boolean operation after meeting the porosity requirements;

[0033] Figure 7 A front view showing the movement of fibers from separation to tangency in each layer;

[0034] Figure 8 This is a schematic diagram of the three-dimensional structure showing the movement of fibers from separation to tangency.

[0035] The present invention will be further explained below with reference to the accompanying drawings and embodiments. Detailed Implementation

[0036] Specific embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While specific embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0037] It should be noted that certain terms are used in the specification and claims to refer to specific components. Those skilled in the art will understand that different terms may be used to refer to the same component. This specification and claims do not distinguish components based on differences in terminology, but rather on differences in function. The terms "comprising" or "including" used throughout the specification and claims are open-ended and should be interpreted as "comprising but not limited to." The following descriptions of preferred embodiments of this disclosure are for the purpose of implementing the general principles of the specification and are not intended to limit the scope of this disclosure. The scope of protection of this disclosure is determined by the appended claims.

[0038] To facilitate understanding of the embodiments of this disclosure, further explanations and descriptions will be provided below with reference to the accompanying drawings and specific examples. The accompanying drawings do not constitute a limitation on the embodiments of this invention.

[0039] To better understand, such as Figures 1 to 8 As shown, a parameterized modeling method for a porous transport layer of a proton exchange membrane includes the following steps:

[0040] The first step is to fix the number of sublayers L of the porous transport layer in the proton exchange membrane electrolyzer. all and the overall porosity ε all and the target fiber diameter D all ;

[0041] The second step is to determine the distribution characteristics of porosity and fiber diameter of different sublayers, and to establish linked lists to store the fiber diameter of each sublayer and linked lists to store the porosity of each sublayer.

[0042] The third step involves establishing a cuboid domain B1 to accommodate all the porous transport layer fibers. Domain B1 has a square base, a width of W, and a height of H. Simultaneously, a height list for the center of each fiber layer is determined.

[0043] The fourth step involves using the random function `random` from the `random` library in Python to generate the coordinates of two random points at corresponding heights within the fiber centers of each layer. The line connecting these two random points is then used as the fiber axis. A section of material with diameter D is then stretched and removed within the cuboid domain B1. i The fibers are generated until the porosity of the layer meets the porosity requirement of the layer. After the fibers are removed, the cuboid domain becomes the cuboid domain B1'.

[0044] The fifth step is to reconstruct a complete cuboid domain B2 with a square bottom, a width of W, and a height of H. Then, by subtracting the cuboid domain B1' (which has had its fibers removed) from the cuboid domain B2 using Boolean operations, the separated porous fiber sublayers are obtained.

[0045] The sixth step involves moving each porous fiber sublayer at a specific interval according to different operating conditions to form an integral porous transport layer.

[0046] In a preferred embodiment of the parameterized modeling method for the porous transport layer of the proton exchange membrane, the porosity distribution characteristics include single-parameter distribution, gradient distribution, Gaussian distribution, and log-normal distribution.

[0047] In a preferred embodiment of the parameterized modeling method for the porous transport layer of the proton exchange membrane, the distribution characteristics of the fiber diameter include single-parameter distribution, gradient distribution, Gaussian distribution, and log-normal distribution.

[0048] In a preferred embodiment of the parameterized modeling method for the porous transport layer of the proton exchange membrane, the normalvariate function of the random library in Python is called based on the overall porosity ε. all Automatically obtain the initial porosity distribution linked list And calling the `normalvariate` function from the Python language's `random` library based on the target fiber diameter D. all Automatically obtain the distribution list of initial fiber diameters

[0049] In a preferred embodiment of the parameterized modeling method for the porous transport layer of the proton exchange membrane, the mean ε of the elements in the initial porosity distribution linked list is calculated. ave The mean D of the distribution list elements of the initial fiber diameter ave The corrected porosity chain and fiber diameter chain are calculated as follows: and

[0050] In a preferred embodiment of the parameterized modeling method for the porous transport layer of the proton exchange membrane, the height H of the cuboid satisfies:

[0051] In a preferred embodiment of the parametric modeling method for the porous transport layer of the proton exchange membrane, the sixth step involves setting a movement distance chain list for each porous fiber layer based on actual operating conditions. Move the i-th porous fiber sublayer by S i This ensures that the porous fiber layers are tangent, separate, or intersecting.

[0052] In a preferred embodiment of the parametric modeling method for the porous transport layer of the proton exchange membrane, a fiber layer with tangential characteristics represents a porous transport layer without coating hydrophilic material, a fiber layer with dissociative characteristics represents a porous transport layer with coating hydrophilic material, and a fiber layer with intersecting characteristics represents a porous transport layer without coating hydrophilic material and after compression.

[0053] In a preferred embodiment of the parametric modeling method for the porous transport layer of the proton exchange membrane, the fiber axis of each layer is generated by two random points, the fiber diameter is twice the width of the cross-section of the cuboid domain, the fibers can intersect and overlap, and when stretched and cut, they penetrate the outer boundary of the cuboid domain.

[0054] In a preferred embodiment of the parametric modeling method for the porous transport layer of the proton exchange membrane, the porosity elements of each layer in the porosity chain can be rearranged.

[0055] In one embodiment, the parametric modeling method for a porous transport layer of a proton exchange membrane includes the following steps:

[0056] The first step is to fix the number of sublayers L of the porous transport layer. all and the overall porosity ε all and the target fiber diameter D all ;

[0057] The second step is to determine the distribution characteristics of porosity and fiber diameter in different sublayers and obtain an initial porosity distribution linked list. and the distribution list of initial fiber diameters Calculate the mean ε of the chain table elements for initial porosity and fiber diameter. ave And Dave, the porosity and fiber diameter chains after target value correction can be calculated as follows: and

[0058] The third step involves establishing a cuboid domain B1 capable of accommodating all the porous transport layer fibers. This cuboid domain has a square base, a width of W, and a height of H. To ensure adjustable spacing between each sublayer after construction, H must satisfy the following condition. To ensure the separation of each fiber layer, and to determine the height of the fiber center in each layer, a chain list is used.

[0059] The fourth step is to... (in each sub-layer H) i Within the height, a set of two random point coordinates is generated using the random function from the Python library. The line connecting these two points is then used as the fiber axis to stretch and remove a section of diameter D within the cuboid domain B1. i The fibers are applied until the porosity of the layer satisfies ε. i After fiber generation stops, the cuboid domain after fiber removal becomes B1';

[0060] The fifth step is to reconstruct a complete cuboid domain B2 with a square bottom, a width of W, and a height of H. By subtracting the cuboid domain B1' with the fibers removed from B2 using Boolean operations, the porous fiber layers with each layer separated can be obtained.

[0061] The sixth step is to set up a linked list for the movement distance of each layer according to the actual working conditions. Move the i-th sub-layer by S i This ensures that the sub-layers satisfy the conditions of tangency, separation, or intersection.

[0062] Preferably, the porous transport layer in step two can represent three different application scenarios through three characteristics: tangency, separation, and intersection between the sublayers. Each porous fiber sublayer can have a tangential characteristic, that is, the top and bottom of each sublayer are in contact with the fibers of the adjacent sublayer, indicating that the fibers are in close contact and no hydrophilic material is applied. Each sublayer can have a separation characteristic, that is, the top and bottom of each sublayer maintain a distance greater than 0 from the fibers of the adjacent sublayer, leaving room for the thickness of the hydrophilic agent particles, indicating that the fibers are not in direct contact and hydrophilic material can be applied. Each sublayer can have an intersection characteristic, that is, the top and bottom of each fiber layer are less than 0 from the fibers of the adjacent sublayer, with overlapping parts, indicating that the porous transport layer is not coated with hydrophilic material, and the layers have overlapping interfaces due to compression.

[0063] In one embodiment, the fiber diameter distribution of the porous transport layer can be represented by a linked list using a custom algorithm written in Python within the ANSYS-SpaceClaim software. The order of the fiber diameter elements in each sublayer of the linked list can be rearranged. Alternatively, using parametric modeling, a custom linked list of fiber diameter distribution features for each layer of the porous transport layer can be defined, with each element including a single-parameter fiber diameter 'a'. f (0<a f <1), gradient fiber diameter a f and b f (0<a f <b f <1), Gaussian distributed fiber diameter (mean μ) f The standard deviation is σ f ), Log-normal distribution fiber diameter (mean μ) f，log The variance is Features such as porosity distribution of porous transport layers can be represented by a linked list formed by a custom algorithm written in Python in ANSYS-SpaceClaim software, and the porosity elements of each sublayer in the linked list can be rearranged.

[0064] In one embodiment, using a parametric modeling method, a custom porosity distribution feature list for each sublayer of the porous transport layer can be defined, where each element of the list includes a single-parameter porosity α. p (0<a p <1), gradient porosity a p and b p (0<a p <b p <1), Gaussian porosity (mean μ)p The standard deviation is σ p Log-normal distribution porosity (mean μ) p，log The variance is Features such as: the fiber axis within each sublayer is generated by two random points (x0, y0, z0) and (x1, y1, z1), the fiber diameter is twice the width W of the cuboid domain cross section, and the fibers can intersect and overlap, ensuring that the fiber penetrates the outer boundary surface of the cuboid domain during stretching shearing.

[0065] In one embodiment, the length L, width W, and height H of the space containing the gas diffusion layer fiber in the fifth step are adjustable. The cuboid region containing the cut-off fiber and the complete cuboid region used for Boolean operations have the same length, width, and height. The complete structure of the porous transport layer is obtained by subtracting the complete cuboid region from the cut-off cuboid region using a Boolean operation.

[0066] In one embodiment, the parametric modeling method for a porous transport layer of a proton exchange membrane includes the following steps:

[0067] The first step is to fix the number of sublayers L of the porous transport layer. all and the overall porosity ε all and the target fiber diameter D all ;

[0068] The second step is to determine the distribution characteristics of porosity and fiber diameter in different sublayers. These distribution characteristics include, but are not limited to, single-parameter distributions, gradient porosity distributions, Gaussian distributions, and log-normal distributions. This specific embodiment uses a Gaussian distribution as an example for detailed explanation. The Gaussian distribution of porosity is defined by the mean μ. p The sum and standard deviation are σ p The Gaussian distribution of fiber diameter is constrained by the mean μ. f The standard deviation of porosity is σ f The `normalvariate` function from the Python `random` library is called based on the overall porosity ε. all Automatically obtain the initial porosity distribution linked list and the distribution list of initial fiber diameters Calculate the mean ε of the chain table elements for initial porosity and fiber diameter. ave and D ave The porosity and fiber diameter chains after target value correction can be calculated as follows: and

[0069] The third step, as Figure 2As shown, a cuboid domain B1 is constructed to accommodate all the porous transport layer fibers. The cuboid domain has a square base, a width of W, and a height of H. To ensure that the spacing between each layer is adjustable after construction, H must satisfy the following condition. To ensure the separation of each fiber layer, and to determine the height of the fiber center in each layer, a chain list is used.

[0070] The fourth step is to define the outer iteration step for each layer as Iterout = 0. Within the height H0 of the first layer, define the inner iteration step Iterin = 0, and generate two random point coordinates (x0, y0, z0) and (x1, y1, z1). Then, using the line connecting these two points as the fiber axis, stretch and cut a fiber with a diameter of D0 within the cuboid domain B1, and determine whether the remaining volume V′0 satisfies the condition. If the condition is not met, immediately exit the inner iteration loop. If the condition is met, continue setting the outer iteration step number Iter_in = 1, and continue generating and removing fibers until the remaining volume of the first layer in B1 just meets the condition. Immediately exit the inner iteration loop, complete the fiber removal of the first layer, and the remaining cuboid region after removal is as follows: Figure 3 As shown. Following this, the outer iteration step increments to Iter_out = i, and the inner iteration begins generating the fibers of the i-th layer, continuing until the fibers of the i-th layer satisfy... End the inner iteration, after removing all L all After the fiber layer is removed, the external iteration is completed, and the cuboid domain after fiber removal becomes as follows: Figure 4 B1' as shown;

[0071] The fifth step involves reconstructing a complete cuboid domain B2 with a square base, width W, and height H. By subtracting the fiber-removed cuboid domain B1' from B2 using Boolean operations, the separated porous fiber layers are obtained. The final front view of the generated porous transport layer is shown below. Figure 5 As shown, a complete porous transport layer is as follows: Figure 6 As shown;

[0072] The sixth step is to set up a linked list for the movement distance of each layer according to the actual working conditions. Move the i-th layer by S i This ensures that the layers can be tangent, disjoint, or intersecting.

[0073] To further understand the specific structural features of this disclosure, in a specific embodiment, the relevant model parameters are set as follows: the length and width of the cuboid domain are both 400 μm; the number of fiber sublayers in the porous transport layer is 17, the target porosity and fiber diameter are 0.81 and 20 μm respectively, and the EPS is 0.001; the mean and standard deviation of the fiber diameter with Gaussian distribution are 20 μm and 0.5 respectively; the mean and standard deviation of the porosity with Gaussian distribution are 0.81 and 0.01 respectively; the initial spacing between each sublayer is set to 5 μm, and then each sublayer of the porous transport layer is moved to... Figure 7 The tangential state shown represents a porous transport layer that has not undergone hydrophilic treatment and has not been compressed. The final complete porous transport layer is as follows: Figure 8 As shown.

[0074] Although the embodiments of this disclosure have been described above in conjunction with the accompanying drawings, this disclosure is not limited to the specific embodiments and application fields described above. The specific embodiments described above are merely illustrative and instructive, and not restrictive. Those skilled in the art can make many other forms based on the guidance of this specification and without departing from the scope of protection of the claims of this disclosure, and all of these are within the scope of protection of this invention.

Claims

1. A parametric modeling method for porous transport layers of proton exchange membranes, characterized in that, It includes the following steps: The first step is to fix the number of sublayers L of the porous transport layer in the proton exchange membrane electrolyzer. all and overall porosity and the target fiber diameter D all ; The second step involves determining the distribution characteristics of porosity and fiber diameter in different sublayers, and establishing linked lists to store the fiber diameter and porosity of each sublayer. This also includes calling the `normalvariate` function from the Python `random` library based on the overall porosity. Automatically obtain the initial porosity distribution linked list And calling the `normalvariate` function from the Python language's `random` library based on the target fiber diameter D. all Automatically obtain the distribution list of initial fiber diameters ; Calculate the mean of the elements in the initial porosity distribution linked list. The mean of the distribution list elements of the initial fiber diameter The corrected porosity chain and fiber diameter chain are calculated as follows: and ; The third step involves establishing a cuboid domain B1 to accommodate all the porous transport layer fibers. Domain B1 has a square base, a width of W, and a height of H. Simultaneously, a height list for the center of each sublayer fiber is determined. ; The fourth step involves using the `random` function from the `random` library in Python to iterate and generate the coordinates of two random points at the corresponding heights of the fiber centers in each sublayer. The line connecting these two random points is then used as the fiber axis. Within the cuboid domain B1, a section of material with a diameter of [missing information] is stretched and removed. The fibers are generated until the porosity of the layer meets the porosity requirement of the sublayer. After the fibers are removed, the cuboid domain becomes the cuboid domain B1'. The fifth step is to reconstruct a complete cuboid domain B2 with a square bottom, a width of W, and a height of H. Then, by subtracting the cuboid domain B1' (which has had its fibers removed) from the cuboid domain B2 using Boolean operations, the porous fiber sublayers that are separated from each other are obtained. The sixth step involves moving each porous fiber sublayer at specific intervals according to different operating conditions to form an overall porous transport layer; wherein, a chain table of movement distances for each porous fiber layer is set according to the actual operating conditions. Move the i-th porous fiber sublayer The porous fiber layers are designed to be tangent, disjoint, or intersecting. Fiber layers with tangent characteristics represent porous transport layers without hydrophilic coating, fiber layers with disjoint characteristics represent porous transport layers with hydrophilic coating, and fiber layers with intersecting characteristics represent porous transport layers without hydrophilic coating and that have been compressed.

2. The parameterized modeling method for porous transport layers of proton exchange membranes according to claim 1, characterized in that, The distribution characteristics of porosity include single-parameter distribution, gradient distribution, Gaussian distribution, and log-normal distribution.

3. The parameterized modeling method for porous transport layers of proton exchange membranes according to claim 1, characterized in that, The distribution characteristics of fiber diameter include single-parameter distribution, gradient distribution, Gaussian distribution, and log-normal distribution.

4. The parameterized modeling method for porous transport layers of proton exchange membranes according to claim 1, characterized in that, The height H of the cuboid satisfies: .

5. The parameterized modeling method for porous transport layers of proton exchange membranes according to claim 1, characterized in that, The fiber axes within each sublayer are generated from two random points. The fiber diameter is twice the width of the cross-section of the cuboid domain. The fibers can intersect and overlap, and when stretched and sheared, they penetrate the outer boundary of the cuboid domain.

6. The parameterized modeling method for porous transport layers of proton exchange membranes according to claim 1, characterized in that, The porosity elements of each sublayer in the porosity chain can be rearranged.

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