A method, system, device and medium for predicting ice melting process in a gas diffusion layer

Abstract

The present invention relates to the field of fuel cell technology and discloses a method, system, device, and medium for predicting the ice melting process in a gas diffusion layer. The method comprises: establishing a microstructure model of the gas diffusion layer under different carbon fiber and pore distributions based on the random distribution of carbon fibers and pores in the gas diffusion layer; obtaining the equivalent thermal conductivity of the gas diffusion layer under different carbon fiber and pore distributions through the microstructure model; establishing an energy conservation equation for the gas diffusion layer as the position of the ice melting front in the gas diffusion layer evolves with time based on a one-dimensional Stefan problem, integrating the energy conservation equation with the temperature distribution of the ice melting region in the gas diffusion layer as a linear distribution, and obtaining the relationship between the position of the ice melting front in the gas diffusion layer and the time evolution under different carbon fiber and pore distributions in combination with the equivalent thermal conductivity; and obtaining the ice melting rate of the gas diffusion layer under different carbon fiber and pore distributions based on the change process of the ice melting front position.

Description

Technical Field

[0001] The present invention relates to the technical field of fuel cells, and in particular to a method, system, equipment and medium for predicting ice melting process in a gas diffusion layer. Background Art

[0002] In recent years, proton exchange membrane fuel cells (PEMFCs) have garnered widespread attention in new energy vehicles and distributed energy applications due to their high efficiency and zero emissions. However, in practical applications, the cold start issue remains a key technical challenge hindering the commercialization of PEMFCs. Particularly at low temperatures, water freezes within the fuel cell, particularly within the gas diffusion layer (GDL), causing ice blockage. This can severely hinder the transport of reactant gases to the catalyst layer, reducing reaction rates and even leading to startup failure. The GDL's substrate is typically composed of woven or non-woven carbon fiber paper. The carbon fibers are randomly cross-woven within the substrate. This randomness allows them to evenly distribute mechanical stress while providing excellent electrical conductivity and gas transport pathways. Furthermore, the randomly distributed pores within the GDL create a porous structure that provides pathways for gas and water transport.

[0003] Existing ice melt prediction methods are mostly based on pore-scale models, such as the Lattice Boltzmann method, which numerically simulates multiphase heat transfer and phase change processes. While these methods accurately capture microscopic phenomena, they are computationally intensive and time-consuming, making them unsuitable for rapid prediction and real-time control in engineering practice. On the other hand, earlier mean-field models achieved rapid predictions through simplified assumptions, but they did not fully consider the impact of the complex microstructure of the GDL on ice melt, resulting in lower prediction accuracy. Summary of the Invention

[0004] The object of the present invention is to provide a method, system, device and medium for predicting the ice melting process in a gas diffusion layer, which can solve the problem of low prediction efficiency and prediction accuracy of the ice melting process in a gas diffusion layer.

[0005] To solve the above technical problems, an embodiment of the present invention provides a method for predicting ice melting process in a gas diffusion layer, comprising the following steps:

[0006] According to the random distribution of carbon fibers and pores in the gas diffusion layer, the microstructure model of the gas diffusion layer under different carbon fiber and pore distributions is established;

[0007] The microstructure model is used to simulate the heat transfer effects of liquid water, ice, gas, and solid in the gas diffusion layer to obtain the equivalent thermal conductivity of the gas diffusion layer under different carbon fiber and pore distributions.

[0008] Based on the one-dimensional Stefan problem, an energy conservation equation for the gas diffusion layer is established as the position of the ice-melting front in the gas diffusion layer evolves with time. The temperature distribution in the ice-melting region of the gas diffusion layer is used as a linear distribution, and the energy conservation equation is integrated. Combined with the equivalent thermal conductivity, the relationship between the position of the ice-melting front and the time evolution of the gas diffusion layer under different carbon fiber and pore distributions is obtained. The change process of the ice-melting front position is used to reflect the ice-melting process in the ice-melting region of the gas diffusion layer.

[0009] According to the change process of the ice melting front position, the ice melting rate of the gas diffusion layer under different carbon fiber and pore distributions is obtained.

[0010] Furthermore, the equivalent thermal conductivity of the gas diffusion layer is obtained by the following formula:

[0011] ;

[0012] Where, k l 、 k g 、 k c 、 k eff are the equivalent thermal conductivity of liquid water, gas, carbon fiber and melted liquid water filling area in the gas diffusion layer, S and ε are the ice saturation and porosity in the gas diffusion layer, δ is the preset correction factor.

[0013] Furthermore, the equivalent thermal conductivity of the carbon fibers in the gas diffusion layer is Determine this by following these steps:

[0014] According to the effect of the assembly pressure of carbon fibers in the gas diffusion layer on the contact thermal resistance caused by insufficient contact between the carbon fibers in each layer, the equivalent thermal conductivity of the carbon fibers is calculated using the following formula: Make corrections:

[0015] ;

[0016] Where, For the corrected , L is the thickness of the gas diffusion layer, A cont It represents the ratio of the contact area between the carbon fibers in the gas diffusion layer to the cross-sectional area of ​​the gas diffusion layer. R i is the contact thermal resistance.

[0017] Furthermore, the energy conservation equation of the gas diffusion layer is expressed by the following formula:

[0018] ;

[0019] The relationship between the evolution of the ice melting front position and time is as follows:

[0020] ;

[0021] The ice melting rate is determined by the following formula:

[0022] ;

[0023] Where, ρ l is the density of liquid water, L f is the latent heat of water, dT / dx is the temperature distribution in the thickness direction of the gas diffusion layer, dT / dx =( T h - T m ) / s , T h and T m are the hot side temperature and ice melting point temperature of the gas diffusion layer, It is the front line of ice melting. It's time.

[0024] Furthermore, the distribution of ice in the gas diffusion layer includes: being completely filled with ice, being filled with discrete ice particles, and being distributed non-uniformly in layers with ice saturation along the thickness direction of the gas diffusion layer.

[0025] Furthermore, if the distribution of ice in the gas diffusion layer is that the ice saturation is layered and non-uniform along the thickness of the gas diffusion layer, the relationship between the evolution of the ice melting front position and time is determined by the following steps:

[0026] The gas diffusion layer is divided into two regions, the left and the right, and the corresponding energy conservation equation is established for each region;

[0027] The piecewise integration method is used to obtain the relationship between the evolution of the ice melting front position in each region and time according to the energy conservation equation, as shown in the following formula:

[0028] ;

[0029] Wherein, the subscripts L and R represent the corresponding parameters of the left and right regions of the gas diffusion layer, respectively.t 1 for s = L / 2 hours of ice melting time.

[0030] Furthermore, if the distribution of ice in the gas diffusion layer is completely filled with ice or filled with discrete ice particles, the relationship between the evolution of the position of the ice melt front and time is determined by the following steps:

[0031] According to the distribution of ice in the gas diffusion layer, the correction coefficient in the equivalent thermal conductivity formula is adjusted to obtain the target equivalent thermal conductivity of the gas diffusion layer under different carbon fiber and pore distributions;

[0032] According to the target equivalent thermal conductivity, the relationship between the evolution of the ice melting front position and time is obtained.

[0033] An embodiment of the present invention further provides a system for predicting ice melting process in a gas diffusion layer, comprising:

[0034] A model building module is used to build a microstructure model of the gas diffusion layer under different carbon fiber and pore distributions based on the random distribution of carbon fibers and pores in the gas diffusion layer;

[0035] The thermal conductivity determination module is used to simulate the heat transfer effects of liquid water, ice, gas and solid in the gas diffusion layer through a microstructure model to obtain the equivalent thermal conductivity of the gas diffusion layer under different carbon fiber and pore distributions;

[0036] A position determination module is used to establish an energy conservation equation for the gas diffusion layer as the position of the ice melt front in the gas diffusion layer evolves over time based on a one-dimensional Stefan problem. The energy conservation equation is integrated using the temperature distribution in the ice melt region of the gas diffusion layer as a linear distribution. Combined with the equivalent thermal conductivity, the relationship between the position of the ice melt front and the time evolution of the gas diffusion layer under different carbon fiber and pore distributions is obtained. The change process of the ice melt front position is used to reflect the ice melting process in the ice melt region of the gas diffusion layer.

[0037] The ice melting prediction module is used to obtain the ice melting rate of the gas diffusion layer under different carbon fiber and pore distributions based on the change process of the ice melting front position.

[0038] An embodiment of the present invention also provides a computer device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned method for predicting the ice melting process in the gas diffusion layer.

[0039] An embodiment of the present invention further provides a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the above-mentioned method for predicting ice melting process in a gas diffusion layer.

[0040] The method for predicting the ice melting process in a gas diffusion layer provided by the present invention has at least the following beneficial effects:

[0041] First, a microstructural model of the GDL was established, taking into account the random distribution of carbon fibers and pores within the GDL (microstructural effects). This model was used to obtain the GDL microstructural model under different carbon fiber and pore distributions. This model simulated the heat transfer effects of liquid water, ice, gas, and solid phases within the GDL, and the equivalent thermal conductivity of the GDL under different carbon fiber and pore distributions was obtained. This coefficient reflects the heat transfer capacity of the GDL under different heat transfer mechanisms and microstructures. Then, based on the one-dimensional Stefan problem, the energy conservation equation for the GDL was established as the position of the melt front within the GDL evolves with time. The one-dimensional Stefan problem describes the movement of phase interfaces and the heat conduction process. The energy conservation equation was integrated, assuming the temperature distribution in the melt region within the GDL as a linear distribution. Combined with the equivalent thermal conductivity, the relationship between the temporal evolution of the melt front position under different carbon fiber and pore distributions was obtained, reflecting the melting process within the melt region within the GDL. Finally, the ice melting rate of the GDL under different carbon fiber and pore distributions was obtained based on this equation.

[0042] This process not only takes into account the influence of the complex microstructure of the gas diffusion layer (different carbon fibers and pore distributions) on ice melting, thereby improving the prediction accuracy of the gas diffusion layer ice melting process, but also uses the carbon fibers and pore distribution of the gas diffusion layer to simulate the heat transfer effect, which is equivalent to directly substituting the microscopic parameters of the gas diffusion layer into a one-dimensional simplified mean field model, thereby improving the prediction efficiency of the gas diffusion layer ice melting process. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0044] Figure 1 A schematic flow chart of a method for predicting ice melting process in a gas diffusion layer provided by the present invention;

[0045] Figure 2 A schematic diagram comparing the equivalent thermal conductivity prediction and pore-scale calculation results provided by the present invention;

[0046] in, Figure 2 (a) is the result of considering full ice and discrete ice distribution. Figure 2 (b) is the result considering the effect of assembly pressure on contact thermal resistance;

[0047] Figure 3 A schematic diagram of the prediction of ice melting process in a gas diffusion layer provided by the present invention;

[0048] in, Figure 3 (a) and Figure 3 (b) in the figure shows the ice melting process in normal state and considering the effect of assembly pressure on contact thermal resistance. The horizontal axis is the initial dimensionless form [ Fo·St ], Figure 3 (c) and Figure 3 (d) in the figure are the dimensionless forms of the horizontal coordinates of the two above-mentioned ice melting processes [ Fo·St· ( k eff / k l )];

[0049] Figure 4 A schematic diagram of the evolution process of the dimensionless ice melting front in a gas diffusion layer with stratified distribution of ice saturation provided by the present invention. DETAILED DESCRIPTION

[0050] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and corresponding drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. 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.

[0051] Current ice melt prediction methods use pore-scale models to numerically simulate multiphase heat transfer and phase change processes. While these methods can accurately capture microscopic phenomena, they are computationally intensive and time-consuming, making them unsuitable for rapid prediction and real-time control in engineering applications. Mean-field models, which achieve rapid predictions through simplified assumptions, often assume a full ice filling within the GDL and fail to fully account for the GDL's complex microstructure, localized uneven ice distribution, and the contact thermal resistance caused by imperfect contact between carbon fibers. Furthermore, assembly pressure, a key factor affecting carbon fiber contact area and heat transfer efficiency, is not effectively described.

[0052] Therefore, there is an urgent need for an ice melting prediction method that can reflect the influence of microstructure, take into account the contact thermal resistance and assembly pressure effects, and has the advantage of fast calculation to support the optimal design and actual control of the fuel cell cold start process.

[0053] The present invention's method for predicting ice melting within a gas diffusion layer (GDL) is based on a one-dimensional Stefan problem. By combining information about the GDL's complex microstructure and the inter-carbon fiber contact thermal resistance, the method constructs an equivalent thermal conductivity prediction model, enabling rapid and accurate prediction of ice melting within the GDL. Specifically, the method employs pore-scale reconstruction technology to determine the random distribution of carbon fibers and pores within the GDL. Key heat transfer parameters are extracted through pore-scale numerical simulation, leading to a rapid prediction model for equivalent thermal conductivity that reflects microstructural effects. Subsequently, the local temperature field is approximated as a linear distribution, and the Stefan problem is used to integrate the melt front position. The equivalent thermal conductivity obtained from the prediction model is then incorporated to derive a universal relationship between ice melting rate and melting time under varying ice distribution conditions. A segmented correction method is also introduced to account for local ice saturation non-uniformity, further improving prediction accuracy. Furthermore, the model incorporates the effect of assembly pressure on inter-carbon fiber contact thermal resistance, enabling prediction of the effect of assembly pressure changes on equivalent thermal conductivity and, consequently, ice melting rate.

[0054] The technical solutions provided by various embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0055] One embodiment of the present invention relates to a method for predicting ice melting process in a gas diffusion layer. The specific process of the method for predicting ice melting process in a gas diffusion layer of this embodiment can be as follows: Figure 1 Shown, including:

[0056] Step 101 : establishing a microstructure model of the gas diffusion layer under different carbon fiber and pore distributions according to the random distribution of carbon fibers and pores in the gas diffusion layer.

[0057] Step 102 , simulating the heat transfer effect of the four phases of liquid water, ice, gas and solid in the gas diffusion layer through a microstructure model to obtain the equivalent thermal conductivity of the gas diffusion layer under different carbon fiber and pore distributions.

[0058] Step 103, based on the one-dimensional Stefan problem, establish the energy conservation equation of the gas diffusion layer when the position of the melting front in the gas diffusion layer evolves with time, and integrate the energy conservation equation with the temperature distribution of the melting area in the gas diffusion layer as a linear distribution. Combined with the equivalent thermal conductivity, the relationship between the evolution of the melting front position of the gas diffusion layer with time under different carbon fiber and pore distributions is obtained; wherein, the change process of the melting front position is used to reflect the melting process of the melting area in the gas diffusion layer.

[0059] Step 104 : Obtain the ice melting rate of the gas diffusion layer under different carbon fiber and pore distributions according to the change process of the ice melting front position.

[0060] The following is a detailed description of the implementation details of the method for predicting the ice melting process in the gas diffusion layer of this embodiment. The following content is only provided for easy understanding of the implementation details and is not necessary for implementing this solution.

[0061] In steps 101 and 102, pore-scale reconstruction technology is first used to accurately obtain the random distribution information of the carbon fibers and pores in the gas diffusion layer, thereby establishing a microstructural model of the gas diffusion layer. Then, through numerical simulation and experimental data, the heat transfer parameters of the four phases of liquid water, ice, gas, and solid are extracted, and an equivalent thermal conductivity model reflecting the microstructural effects is constructed. This allows the equivalent thermal conductivity of the gas diffusion layer to be obtained under different carbon fiber and pore distributions.

[0062] The equivalent thermal conductivity model considers both the contact thermal resistance between carbon fibers and the change in contact area due to changes in assembly pressure. The expression is:

[0063] ;

[0064] Where, k l 、 k g 、 k c 、 k eff are the equivalent thermal conductivity of liquid water, gas, carbon fiber and melted liquid water filling area in the gas diffusion layer, S and ε are the ice saturation and porosity in the gas diffusion layer, δ is a preset correction coefficient, which is 1 when the gas diffusion layer is fully filled with ice and about 1.5 when the ice is randomly distributed.

[0065] In one example, when considering the effect of the assembly pressure of carbon fibers in the gas diffusion layer on the contact thermal resistance caused by insufficient contact between the carbon fibers in each layer, the equivalent thermal conductivity of the carbon fibers needs to be calculated using the following formula: Make corrections:

[0066] ;

[0067] Where, For the corrected , L is the thickness of the gas diffusion layer, A cont It represents the ratio of the contact area between carbon fibers in the gas diffusion layer to the cross-sectional area of ​​the gas diffusion layer. It is determined by reconstructing the microstructure of the gas diffusion layer. The value is usually between 0.05 and 0.08. R i is the contact thermal resistance, which is usually around 10-8 -10 -5 m 2 KW -1 within the range.

[0068] For a certain type of gas diffusion layer, its assembly pressure p load and R i The relationship between them can be expressed as follows:

[0069] ;

[0070] Therefore, this formula can be used to relate the abstract carbon fiber-to-carbon fiber contact thermal resistance to the assembly pressure in actual implementation.

[0071] Compared with high-precision pore-scale simulations, the average maximum error of the equivalent thermal conductivity of the gas diffusion layer under different ice distribution conditions obtained using this equivalent thermal conductivity prediction model is 2.04%.

[0072] In step 103, after obtaining the equivalent thermal conductivity, the ice melting front ( s ) About time ( t ) is expressed by the following energy conservation equation:

[0073] ;

[0074] Where, ρ l is the density of liquid water, L f is the latent heat of water, dT / dx is the temperature distribution in the thickness direction of the gas diffusion layer.

[0075] Since the time scale of heat diffusion is usually smaller than the time scale of ice melting, the temperature distribution in the melting area can be assumed to be linear, i.e. dT / dx =( T h - T m ) / s , T h and T m are the hot side temperature and ice melting point of the gas diffusion layer, respectively.

[0076] Substituting this temperature gradient into the energy conservation equation and integrating it, we can derive the relationship between the evolution of the melt front and time, as follows:

[0077] ;

[0078] Here is the melting front s It is expressed as the average position of the ice melt front in the GDL. Therefore, the melting rate of ice in the GDL ( MR )for:

[0079] ;

[0080] Then, rewrite the above formula into dimensionless form:

[0081] ;

[0082] Where, Fo and St are Fourier number and Stefan number respectively, expressed as:

[0083] ;

[0084] ;

[0085] Where, α l and c p,l are the thermal diffusivity and specific heat capacity of liquid water, respectively.

[0086] This dimensionless formula is applicable to various ice distribution situations in the gas diffusion layer: full ice filling, discrete ice particles, and layered non-uniform distribution of ice saturation along the thickness of the gas diffusion layer.

[0087] The above steps theoretically verify the universality of the rapid prediction model for the gas diffusion layer ice melting process proposed in the present invention.

[0088] In the specific implementation, when the gas diffusion layer is completely filled with ice and ice particles are discretely distributed, the ice melting process can be given correction coefficients according to the above equivalent thermal conductivity coefficient model. δ Different values ​​to obtain the corresponding k eff ， This allows prediction of ice melting conditions for both ice distributions.

[0089] For the case of non-uniform distribution of ice saturation in the thickness direction of the gas diffusion layer, the above ice melting prediction method needs to be segmented. That is, the gas diffusion layer is divided into left and right or multiple regions, and a local energy conservation model is established for each region. Then, the evolution relationship of the overall ice melting front is obtained by using the segmented integration method. The specific steps are as follows:

[0090] Taking the left and right layers as an example, the energy conservation equation is rewritten as follows according to the different segment positions:

[0091] ;

[0092] ;

[0093] Wherein, the subscripts L and R represent the corresponding parameters of the left and right regions of the gas diffusion layer, respectively.

[0094] Therefore, by integrating the above two equations, the evolution of the ice melt front, that is, the relationship between the position of the ice melt front in each area and the time evolution, can be obtained as follows:

[0095] ;

[0096] Where, t 1 for s = L / 2 hours of ice melting time.

[0097] The left and right regions of the gas diffusion layer satisfy the rewritten energy conservation expression respectively, and after integration, their respective melting fronts are obtained s L and s R , and according to s = L / 2 moment ( t 1) Achieve transition and ensure the continuity and accuracy of overall forecast.

[0098] In one example, this embodiment introduces the effect of assembly pressure on carbon fiber contact thermal resistance into the model and establishes the P load and R i The functional relationship between them is used to modify the corresponding k c ′ value and equivalent thermal conductivity k eff , so that under different assembly pressure conditions, the model can achieve quantitative prediction of ice melting rate, and its prediction error can be controlled within 4.4%.

[0099] In some embodiments, in order to realize the automated calculation of the method for predicting the ice melting process in the gas diffusion layer of the present invention, this embodiment provides a corresponding program to implement the calculation module. This module includes four submodules: data input, model calculation, and result output. The data input module is given the microstructure parameters, temperature field, and assembly pressure information of the gas diffusion layer; the model calculation module combines the above-mentioned equivalent thermal conductivity model with the one-dimensional Stefan ice melting prediction model to calculate the ice melting rate in real time. MRThe result output module feeds back the calculation results to the user in the form of line graphs or data reports, realizing online, fast and high-precision monitoring of the ice melting process.

[0100] The method for predicting ice melting in the gas diffusion layer (GDL) of the present invention can quickly predict the ice melting process in the GDL during the cold start of a PEMFC, while taking into account the complex microstructure and contact thermal resistance effects between fibers. By converting pore-scale information into mean-field model parameters, a good balance between computational efficiency and prediction accuracy is achieved. The method has high applicability for uniform and stratified ice distribution conditions with full or partial discrete filling. It provides a reliable numerical tool for cold start strategy optimization and GDL design of fuel cell systems, and has high engineering application value.

[0101] Several specific embodiments are used below to illustrate the specific implementation of the method for predicting the ice melting process in a gas diffusion layer of the present invention.

[0102] Example 1: Prediction of uniformly distributed ice:

[0103] In this example, the method of the present invention is used to predict the ice melting process under two uniform conditions: one in which the GDL is completely filled with ice, and the other in which discrete ice particles are randomly filled. The specific steps are:

[0104] (1) Using pore-scale reconstruction technology to obtain the distribution information of carbon fibers and pores in GDL;

[0105] (2) Extract the heat transfer parameters of liquid water, ice, gas and solid based on numerical simulation, and construct an equivalent thermal conductivity model ( Figure 2 (a) in the figure);

[0106] (3) Combined with the one-dimensional Stefan problem to establish the ice melting rate ( MR ) and dimensionless time. Experimental and simulation results show that the prediction error can be controlled within 5% in both the case of full ice filling and discrete ice particles (e.g. Figure 3 (a) and Figure 3 As shown in (c) in the figure. Figure 3 (a) is the horizontal axis in the initial dimensionless form [ Fo·St ]; Figure 3 (c) in the figure is the horizontal axis in the completely dimensionless form [ Fo·St· ( k eff / k l )]).

[0107] Example 2: Prediction of stratified ice saturation distribution:

[0108] Considering the potential for uneven ice saturation distribution within the GDL during actual operation, this embodiment divides the GDL into left and right or multiple zones along the heat transfer direction. A local one-dimensional Stefan energy conservation model is independently established within each zone. By calculating the position of the local ice melt front in each zone, the overall ice melting process is synthesized through time integration. Experimental results show that the model, after considering the hierarchical distribution, can more accurately reflect local temperature gradient differences, and the prediction error can be controlled within 6% (e.g., Figure 4 shown).

[0109] Example 3: Prediction of the impact of assembly pressure:

[0110] This embodiment introduces the influence of assembly pressure on the basis of the above model. The functional relationship established through experiments or numerical simulations is substituted into the equivalent thermal conductivity model to obtain accurate equivalent thermal conductivity prediction results (such as Figure 2 (b) in Figure 1). The improved mean field model was then used to predict the ice melting process within the GDL. The results show that as the assembly pressure increases, the contact area between carbon fibers increases and the contact thermal resistance decreases, which in turn increases and accelerates the ice melting process. The experimental data agree well with the model predictions, verifying the applicability and accuracy of this method under different assembly pressure conditions (e.g., Figure 3 (b) and Figure 3 (d) shows the assembly pressure p load =0.265MPa GDL ice melting process. Figure 3 (b) in the figure shows the horizontal axis in the initial dimensionless form [ Fo·St ]; Figure 3 (d) in the figure is the horizontal axis in the completely dimensionless form [ Fo·St· ( k eff / k l )]).

[0111] The steps of the various methods above are divided only for the purpose of clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are within the scope of protection of the present invention. Adding insignificant modifications or introducing insignificant designs to the algorithm or process without changing the core design of the algorithm and process are all within the scope of protection of the invention.

[0112] Another embodiment of the present invention relates to a system for predicting ice melting in a gas diffusion layer. The implementation details of the system for predicting ice melting in a gas diffusion layer of this embodiment are described in detail below. The following content is provided only for ease of understanding and is not required for implementing this solution. The system for predicting ice melting in a gas diffusion layer of this embodiment includes:

[0113] A model building module is used to build a microstructure model of the gas diffusion layer under different carbon fiber and pore distributions based on the random distribution of carbon fibers and pores in the gas diffusion layer;

[0114] The thermal conductivity determination module is used to simulate the heat transfer effects of liquid water, ice, gas and solid in the gas diffusion layer through a microstructure model to obtain the equivalent thermal conductivity of the gas diffusion layer under different carbon fiber and pore distributions;

[0115] The position determination module is used to establish the energy conservation equation of the gas diffusion layer as the position of the ice melting front in the gas diffusion layer evolves with time based on the one-dimensional Stefan problem. The energy conservation equation is integrated using the temperature distribution of the ice melting area in the gas diffusion layer as a linear distribution. Combined with the equivalent thermal conductivity, the relationship between the position of the ice melting front and the time evolution of the gas diffusion layer under different carbon fiber and pore distributions is obtained. The change process of the ice melting front position is used to reflect the ice melting process in the ice melting area of ​​the gas diffusion layer.

[0116] The ice melting prediction module is used to obtain the ice melting rate of the gas diffusion layer under different carbon fiber and pore distributions based on the change process of the ice melting front position.

[0117] It is not difficult to find that this embodiment is a system embodiment corresponding to the above-mentioned method embodiment, and this embodiment can be implemented in conjunction with the above-mentioned method embodiment. The relevant technical details and technical effects mentioned in the above-mentioned embodiment are still valid in this embodiment, and to reduce repetition, they are not repeated here. Accordingly, the relevant technical details mentioned in this embodiment can also be applied to the above-mentioned embodiment.

[0118] It is worth noting that all modules involved in this embodiment are logical modules. In actual applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, to highlight the innovations of the present invention, this embodiment does not include units that are not closely related to solving the technical problems proposed by the present invention. However, this does not mean that other units do not exist in this embodiment.

[0119] Another embodiment of the present invention relates to a computer device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method for predicting the ice melting process in the gas diffusion layer in the above-mentioned embodiments.

[0120] The memory and processor are connected using a bus, which can include any number of interconnected buses and bridges. The bus connects various circuits of one or more processors and memories. The bus can also connect various other circuits such as peripheral devices, voltage regulators, and power management circuits. These are all well known in the art and are therefore not described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single component or multiple components, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over a wireless medium via an antenna. Furthermore, the antenna receives data and transmits it to the processor.

[0121] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

[0122] Another embodiment of the present invention relates to a computer-readable storage medium storing a computer program, which implements the above method embodiment when executed by a processor.

[0123] That is, those skilled in the art will understand that all or part of the steps in the above-described method embodiments can be implemented by instructing the relevant hardware through a program. The program is stored in a storage medium and includes a number of instructions for causing a device (such as a microcontroller or chip) or a processor to execute all or part of the steps in the method embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0124] Those skilled in the art will appreciate that the above embodiments are specific embodiments for implementing the present invention, and that in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present invention.

Claims

1. A method for predicting ice melting process in a gas diffusion layer, characterized in that: The method comprises: According to the random distribution of carbon fibers and pores in the gas diffusion layer, the microstructure model of the gas diffusion layer under different carbon fiber and pore distributions is established; The microstructure model is used to simulate the heat transfer effects of liquid water, ice, gas, and solid in the gas diffusion layer to obtain the equivalent thermal conductivity of the gas diffusion layer under different carbon fiber and pore distributions. Based on the one-dimensional Stefan problem, an energy conservation equation for the gas diffusion layer is established as the position of the ice-melting front in the gas diffusion layer evolves with time. The temperature distribution in the ice-melting region of the gas diffusion layer is used as a linear distribution, and the energy conservation equation is integrated. Combined with the equivalent thermal conductivity, the relationship between the position of the ice-melting front and the time evolution of the gas diffusion layer under different carbon fiber and pore distributions is obtained. The change process of the ice-melting front position is used to reflect the ice-melting process in the ice-melting region of the gas diffusion layer. According to the change process of the ice melting front position, the ice melting rate of the gas diffusion layer under different carbon fiber and pore distribution is obtained; The equivalent thermal conductivity of the gas diffusion layer is obtained by the following formula: Where k l 、k g 、k c 、k eff are the equivalent thermal conductivity of liquid water, gas, carbon fiber, and melted liquid water-filled area in the gas diffusion layer, respectively. S and ε are the ice saturation and porosity in the gas diffusion layer, respectively. δ is a preset correction factor. The equivalent thermal conductivity k of the carbon fibers in the gas diffusion layer is c Determine this by following these steps: According to the effect of the assembly pressure of carbon fibers in the gas diffusion layer on the contact thermal resistance caused by insufficient contact between the carbon fibers in each layer, the equivalent thermal conductivity k of the carbon fibers is calculated using the following formula: c Make corrections: Where k c ' is the corrected k c , L is the thickness of the gas diffusion layer, A cont R represents the ratio of the contact area between carbon fibers in the gas diffusion layer to the cross-sectional area of ​​the gas diffusion layer. i is the contact thermal resistance; The energy conservation equation of the gas diffusion layer is expressed by the following formula: Where, ρ l is the density of liquid water, L f is the latent heat of water, dT / dx is the temperature distribution in the thickness direction of the gas diffusion layer, dT / dx=(T h -T m ) / s, t is time.

2. The method for predicting ice melting process in a gas diffusion layer according to claim 1, characterized in that: The relationship between the evolution of the ice melting front position and time is as follows: The ice melting rate is determined by the following formula: Where, T h and T m are the hot side temperature of the gas diffusion layer and the ice melting point temperature, respectively, and s is the position of the ice melting front.

3. The method for predicting ice melting process in a gas diffusion layer according to claim 1, characterized in that: The distribution of ice in the gas diffusion layer includes: being completely filled with ice, being filled with discrete ice particles, and being distributed non-uniformly in layers with ice saturation along the thickness direction of the gas diffusion layer.

4. The method for predicting ice melting process in a gas diffusion layer according to claim 3, characterized in that: If the distribution of ice in the gas diffusion layer is such that the ice saturation is layered and non-uniform along the thickness of the gas diffusion layer, the relationship between the evolution of the position of the ice melt front and time is determined by the following steps: The gas diffusion layer is divided into two regions, the left and the right. For each region, the corresponding energy conservation equation is established as follows: The piecewise integration method is used to obtain the relationship between the evolution of the ice melting front position in each region and time according to the energy conservation equation, as shown in the following formula: Wherein, the subscripts L and R represent the corresponding parameters of the left and right regions of the gas diffusion layer, respectively, and t1 is the ice melting time when s = L / 2.

5. The method for predicting ice melting process in a gas diffusion layer according to claim 4, characterized in that: If the distribution of ice in the gas diffusion layer is completely filled with ice or filled with discrete ice particles, the relationship between the evolution of the position of the ice melt front and time is determined by the following steps: According to the distribution of ice in the gas diffusion layer, the correction coefficient in the equivalent thermal conductivity formula is adjusted to obtain the target equivalent thermal conductivity of the gas diffusion layer under different carbon fiber and pore distributions; According to the target equivalent thermal conductivity, the relationship between the evolution of the ice melting front position and time is obtained.

6. A system for predicting ice melting process in a gas diffusion layer, characterized in that: The system comprises: A model building module is used to build a microstructure model of the gas diffusion layer under different carbon fiber and pore distributions based on the random distribution of carbon fibers and pores in the gas diffusion layer; The thermal conductivity determination module is used to simulate the heat transfer effects of liquid water, ice, gas and solid in the gas diffusion layer through a microstructure model to obtain the equivalent thermal conductivity of the gas diffusion layer under different carbon fiber and pore distributions; A position determination module is used to establish an energy conservation equation for the gas diffusion layer as the position of the ice melt front in the gas diffusion layer evolves over time based on a one-dimensional Stefan problem. The energy conservation equation is integrated using the temperature distribution in the ice melt region of the gas diffusion layer as a linear distribution. Combined with the equivalent thermal conductivity, the relationship between the position of the ice melt front and the time evolution of the gas diffusion layer under different carbon fiber and pore distributions is obtained. The change process of the ice melt front position is used to reflect the ice melting process in the ice melt region of the gas diffusion layer. The ice melting prediction module is used to obtain the ice melting rate of the gas diffusion layer under different carbon fiber and pore distributions based on the change process of the ice melting front position; The equivalent thermal conductivity of the gas diffusion layer is obtained by the following formula: Where k l 、k g 、k c 、k eff are the equivalent thermal conductivity of liquid water, gas, carbon fiber, and melted liquid water-filled area in the gas diffusion layer, respectively. S and ε are the ice saturation and porosity in the gas diffusion layer, respectively. δ is a preset correction factor. The thermal conductivity determination module is also used to determine the equivalent thermal conductivity coefficient k of the carbon fiber according to the influence of the assembly pressure of the carbon fiber in the gas diffusion layer on the contact thermal resistance caused by insufficient contact between the carbon fibers in each layer through the following formula: c Make corrections: Where k c ' is the corrected k c , L is the thickness of the gas diffusion layer, A cont R represents the ratio of the contact area between carbon fibers in the gas diffusion layer to the cross-sectional area of ​​the gas diffusion layer. i is the contact thermal resistance; The energy conservation equation of the gas diffusion layer is expressed by the following formula: Where, ρ l is the density of liquid water, L f is the latent heat of water, dT / dx is the temperature distribution in the thickness direction of the gas diffusion layer, dT / dx=(T h -T m ) / s, t is time.

7. A computer device, characterized in that: include: at least one processor; And, a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method for predicting ice melting process in the gas diffusion layer according to any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for predicting ice melting process in a gas diffusion layer according to any one of claims 1 to 5 is implemented.

Images