Bipolar plate runner structure-mass transfer efficiency parameter simulation optimization method

Abstract

The invention relates to the technical field of hydrogen production, in particular to a bipolar plate runner structure-mass transfer efficiency parameter simulation optimization method. Comprising the steps that a flow channel structure is designed, specifically, a bipolar plate flow channel is constructed through a fractal tree-shaped network topological structure, the flow channel is composed of multiple stages of branch channels, the section size of each stage of branch channel is decreased progressively according to a self-similarity proportion, and a three-dimensional flow channel network with fractal dimensions is formed; multi-physics field coupling modeling: establishing a multi-physics field coupling model including fluid flow, electrochemical reaction and heat and mass transfer based on computational fluid mechanics and a finite element method, wherein the model considers a turbulence effect in a flow channel, interface impedance of a porous medium diffusion layer and reaction kinetic parameters at the same time; parameter sensitivity analysis: screening key parameters influencing mass transfer efficiency through orthogonal test design, including flow channel fractal dimension, branch angle, porosity and surface wettability parameters, and constructing a parameter response curved surface; the flow channel structure of the bipolar plate can be accurately optimized, and the mass transfer efficiency is effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of hydrogen production technology, and in particular to a method for simulating and optimizing bipolar plate flow channel structure-mass transfer efficiency parameters. Background Art

[0002] In the field of anion exchange membrane hydrogen production technology, the design of the bipolar plate flow channel structure is directly related to the mass transfer efficiency and overall performance of the electrolyzer. At present, traditional AEM hydrogen production electrolyzer bipolar plates mostly use parallel flow channels or simple serpentine flow channel structures. This type of design has the significant defect of low mass transfer efficiency. Due to the uneven distribution of fluid in the flow channel, mass transfer dead zones are easily formed, which prevents the reactants from fully participating in the electrochemical reaction, limiting the electrolyzer's ability to operate at high current density. At the same time, existing simulation optimization methods often analyze physical fields such as fluid flow, electrochemical reaction, heat and mass transfer in isolation, without considering the coupling effects between the various physical fields, resulting in a large deviation between the simulation model and the actual working conditions. The optimization results obtained based on this are difficult to apply to actual production. In addition, a single optimization algorithm cannot effectively balance multiple conflicting optimization objectives such as mass transfer efficiency, pressure drop and temperature distribution, making it difficult for the flow channel structure to meet the requirements of AEM hydrogen production technology for efficient and stable operation.

[0003] Based on the above problems, there is an urgent need for a simulation optimization technology solution that can comprehensively consider the coupling characteristics of multiple physical fields, accurately optimize the bipolar plate flow channel structure, effectively improve the mass transfer efficiency, and take into account the pressure drop and temperature distribution. Summary of the Invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and propose a bipolar plate flow channel structure-mass transfer efficiency parameter simulation optimization method, including: Flow channel structure design: The bipolar plate flow channel is constructed using a fractal tree network topology. The flow channel is composed of multiple levels of branch channels. The cross-sectional size of each level of branch channels decreases in a self-similar proportion, forming a three-dimensional flow channel network with fractal dimension. Multi-physics coupling modeling: Based on computational fluid dynamics and the finite element method, a multi-physics coupling model that includes fluid flow, electrochemical reactions, and heat and mass transfer is established. The model simultaneously considers the turbulent effects within the flow channel, the interfacial impedance of the porous medium diffusion layer, and the reaction kinetic parameters. Parameter sensitivity analysis: Orthogonal experimental design was used to screen key parameters affecting mass transfer efficiency, including flow channel fractal dimension, branching angle, porosity, and surface wettability parameters, and construct a parameter response surface. Multi-objective optimization algorithm: A non-dominated sorting genetic algorithm is used to globally optimize the multi-physics coupling model, with maximizing mass transfer efficiency, minimizing pressure drop, and optimizing temperature distribution uniformity as the objective functions, to obtain the Pareto optimal solution set of flow channel structure and operating parameters; Experimental verification and correction: Bipolar plate samples were prepared based on the optimized flow channel structure. The simulation results were verified through electrochemical testing and in-situ visualization technology, and the boundary conditions and material parameters in the model were corrected.

[0005] Preferably, the fractal tree-like flow channel has 4-6 branching levels, the first-level flow channel width is 0.8-1.2 mm, the last-level flow channel width is 0.1-0.3 mm, the width ratio of two adjacent flow channels is 1:2 to 1:3, and the ratio of flow channel depth to width is 0.8:1 to 1.2:1.

[0006] Further preferably, in the multi-physics field coupling model, the fluid flow control equation adopts the Reynolds-averaged Navier-Stokes equation, and is combined with the k-ωSST turbulence model to simulate the turbulent flow in the flow channel; the electrochemical reaction process is described by the Butler-Volmer equation, considering the kinetic parameters of the anode oxygen evolution reaction and the cathode hydrogen evolution reaction; the heat and mass transfer process includes liquid water diffusion, bubble generation and transport, and heat conduction in the solid domain.

[0007] Further preferably, in the parameter sensitivity analysis, the value range of the fractal dimension α is 1.5-2.5, the value range of the branching angle θ is 30°-60°, the value range of the porosity ε is 0.3-0.6, and the value range of the surface contact angle φ is 60°-120°; the contribution rate of each parameter to the mass transfer efficiency is determined by variance analysis, and the parameters with a contribution rate greater than 15% are selected as key parameters.

[0008] Further preferably, the mass transfer efficiency is calculated by the following formula: ; in: is the current density; is the molar mass of water; is the liquid water concentration; is the cross-sectional area of ​​the flow channel; is the molecular diffusion coefficient; is the porous medium tortuosity factor; is the reaction activation energy; is the gas constant; is the operating temperature.

[0009] Further preferably, the pressure drop is calculated by the following formula: ; in: is the fluid dynamic viscosity; is the flow channel length; is the volume flow rate; is the flow channel radius; is the fluid density; is the fractal dimension; is the branch angle.

[0010] Further preferably, the temperature distribution uniformity is evaluated by the following formula: ; in: is the temperature standard deviation; is the average temperature.

[0011] Further preferably, in the multi-objective optimization algorithm, the population size is set to 100-200, the number of iterations is 50-100, the crossover probability is 0.8-0.9, and the mutation probability is 0.05-0.1; the optimized flow channel structure must meet the requirements of an increase in mass transfer efficiency of more than 30%, a reduction in pressure drop of more than 20%, and a temperature standard deviation of less than 5K.

[0012] Further preferably, the bipolar plate material is a surface-modified titanium alloy or stainless steel, and the surface is prepared with a gradient pore structure by laser microporous technology, the micropore diameter is 50-200 μm, the porosity is 15%-30%, and a nano-composite coating with a thickness of 50-200 nm is coated, and the coating is composed of titanium carbonitride and graphene.

[0013] Further preferably, during the experimental verification process, an in-situ optical microscope is used to observe the bubble behavior in the flow channel, the interfacial impedance is measured by electrochemical impedance spectroscopy, and the temperature distribution is monitored by a thermal imager; the prediction error of the corrected model is less than 10%, and the deviation between the experimentally measured mass transfer efficiency and the simulation results is less than 5%.

[0014] Technical effect: The present invention effectively solves the problems of low mass transfer efficiency and inaccurate simulation optimization in the background technology through the technical solution provided. The flow channel is constructed using a fractal tree network topology to increase the mass transfer area and improve fluid distribution; multi-physics field coupling modeling comprehensively considers the interaction between various physical fields to improve simulation accuracy; orthogonal experiments and non-dominated sorting genetic algorithms are used to screen key parameters and perform multi-objective optimization to balance mass transfer efficiency, pressure drop and temperature distribution; experimental verification and correction ensure the reliability of the optimization results. These technical points work together to significantly improve the mass transfer efficiency and overall performance of the bipolar plate flow channel of the AEM hydrogen production electrolyzer, realizing the effective transformation of simulation optimization technology from theory to practical application. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Flowchart of the simulation optimization method for the bipolar plate flow channel structure-mass transfer efficiency parameters for this application. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] The traditional AEM hydrogen electrolyzer bipolar plate flow channel structure simulation optimization method has the following technical problems: the existing flow channel structure mostly adopts a simple parallel or serpentine design, with low mass transfer efficiency, which cannot meet the reaction requirements under high current density; the simulation process often ignores the coupling effect between multiple physical fields, resulting in a large deviation between the model and the actual working conditions; the optimization algorithm is single and it is difficult to balance multiple objectives such as mass transfer efficiency, pressure drop and temperature distribution; and there is a lack of experimental verification and model correction mechanism, making it difficult to apply the optimization results to actual production. Based on this, This embodiment provides a method for simulating and optimizing the bipolar plate flow channel structure and mass transfer efficiency parameters for an anion exchange membrane (AEM) hydrogen production electrolyzer. The method comprises the following steps: constructing the bipolar plate flow channels using a fractal tree network topology, wherein the cross-sectional dimensions of the multi-level branch channels decrease in a self-similar manner to form a three-dimensional flow channel network; establishing a multi-physics coupling model including fluid flow, electrochemical reaction, heat and mass transfer based on computational fluid dynamics (CFD) and the finite element method (FEM), while taking into account turbulence effects, interfacial impedance, and reaction kinetic parameters; screening key parameters affecting mass transfer efficiency through orthogonal experimental design and constructing a parameter response surface; globally optimizing the multi-physics coupling model using a non-dominated sorting genetic algorithm (NSGA-II), with the objective functions of maximizing mass transfer efficiency, minimizing pressure drop, and optimizing temperature distribution uniformity to obtain a Pareto optimal solution set; preparing bipolar plate specimens based on the optimized flow channel structure, verifying the simulation results through electrochemical testing and in-situ visualization technology, and revising the boundary conditions and material parameters in the model.

[0018] This approach significantly increases the mass transfer area, changes the fluid flow state, and improves mass transfer capacity through fractal tree-like flow channel design. Multi-physics field coupling modeling more realistically simulates the actual operating conditions of the electrolyzer, improving model accuracy. The combination of orthogonal experiments and the NSGA-II algorithm effectively screens key parameters and performs multi-objective optimization, avoiding the limitations of traditional single algorithms. Experimental verification and correction ensure that the simulation results can be effectively applied to actual production. This method forms a complete and scientific simulation optimization system, encompassing flow channel design, model construction, optimization algorithms, and result verification. This provides a reliable technical path for optimizing the bipolar plate flow channel structure of AEM hydrogen electrolyzers, addressing the problems of poor mass transfer, inaccurate models, poor optimization results, and difficulty in application associated with traditional methods.

[0019] In the bipolar plate flow channel structure of the AEM hydrogen production electrolyzer, if the flow channel size design is unreasonable, many technical problems will arise: the flow channel width is too large, the fluid flow rate is low, and the mass transfer is insufficient; if the width is too small, the pressure drop increases and energy consumption increases; the inappropriate ratio of the flow channel depth and width will affect the flow pattern and mass transfer effect of the fluid in the flow channel; the number of branch levels and the size ratio of each level are not appropriate, which cannot fully utilize the advantages of the fractal flow channel and make it difficult to achieve efficient mass transfer and reasonable pressure drop.

[0020] Based on this, the branching level of the fractal tree-like flow channel is 4-6, the width of the first-level flow channel is 0.8-1.2mm, the width of the last-level flow channel is 0.1-0.3mm, the width ratio of two adjacent levels of flow channels is 1:2 to 1:3, and the ratio of flow channel depth to width is 0.8:1 to 1.2:1.

[0021] This technical solution precisely defines the key dimensional parameters of the fractal tree-like flow channel. The appropriate number of branching levels ensures the complexity and mass transfer area of ​​the flow channel network, which can fully disperse the fluid without making the structure too complex and causing manufacturing difficulties; the setting of the width of the first and last stage flow channels and the ratio of the width of each stage ensures that the fluid can gradually accelerate in the flow channel, forming a good turbulent state, improving the mass transfer efficiency, and controlling the pressure drop within a reasonable range; the ratio of the flow channel depth to the width optimizes the three-dimensional flow characteristics of the fluid in the flow channel, allowing the fluid to fully contact the flow channel wall, further enhancing the mass transfer effect. Through the reasonable design of these dimensional parameters, the problems of low mass transfer efficiency and large pressure drop caused by unreasonable flow channel size are effectively solved, so that the bipolar plate flow channel can stably and efficiently achieve mass transfer during the operation of the AEM hydrogen production electrolyzer, reduce energy consumption, and improve the overall performance and operational stability of the electrolyzer.

[0022] Previous AEM hydrogen electrolyzer bipolar plate flow channel simulation models had the following technical problems: only considering a single physical field of fluid flow or electrochemical reaction, it could not reflect the complex interaction of multiple physical fields in actual operation; the simulation of fluid flow was inaccurate and the influence of turbulence was not fully considered; the electrochemical reaction process was simplified and the reaction kinetics could not be accurately described; the analysis of the heat and mass transfer process was incomplete and key factors such as bubble generation and transport were ignored, resulting in a large difference between the model and the actual operating conditions and an inability to provide a reliable basis for flow channel optimization.

[0023] Based on this, in the multi-physics field coupling model, the fluid flow control equation adopts the Reynolds-averaged Navier-Stokes equations (RANS), combined with the k-ωSST turbulence model to simulate the turbulent flow in the flow channel; the electrochemical reaction process is described by the Butler-Volmer equation, considering the kinetic parameters of the anode oxygen evolution reaction (OER) and the cathode hydrogen evolution reaction (HER); the heat and mass transfer processes include liquid water diffusion, bubble generation and transport, and heat conduction in the solid domain.

[0024] This technical solution constructs a comprehensive and accurate multi-physics coupling model. In terms of fluid flow simulation, the combination of the RANS equations and the k-ωSST turbulence model can accurately capture the complex turbulent flow characteristics in the flow channel, such as eddies, boundary layer separation and other phenomena, and provide accurate flow field information for mass transfer analysis; the Butler-Volmer equation's description of the electrochemical reaction fully considers the reaction kinetic parameters, so that the model can truly reflect the electrochemical reaction process on the electrode surface, including key parameters such as reaction rate and overpotential; the heat and mass transfer process covers the dynamic behavior of liquid water and bubbles and solid domain heat conduction, fully presenting the transfer process of matter and energy in the electrolyzer. Through this deep coupling of multiple physical fields, the limitations of the single physical field analysis of the traditional model are effectively solved, making the simulation results closer to the actual working conditions, and being able to accurately predict the performance of the flow channel under different operating conditions. It provides a scientific and reliable theoretical basis for the optimization design of the bipolar plate flow channel structure, helping to improve the overall performance and efficiency of the AEM hydrogen production electrolyzer.

[0025] The following technical problems exist in the process of optimizing the bipolar plate flow channel structure and mass transfer efficiency parameters of AEM hydrogen production electrolyzers: the parameters that affect mass transfer efficiency are numerous and interrelated, making it difficult to determine the key parameters; blindly optimizing all parameters will lead to excessive computational complexity and low efficiency; the lack of a scientific parameter screening method makes it impossible to accurately determine the degree of influence of each parameter on mass transfer efficiency, making the optimization direction unclear and difficult to obtain ideal optimization results.

[0026] Based on this, in the parameter sensitivity analysis, the fractal dimension α is in the range of 1.5-2.5, the branching angle θ is in the range of 30°-60°, the porosity ε is in the range of 0.3-0.6, and the surface contact angle φ is in the range of 60°-120°; the contribution rate of each parameter to the mass transfer efficiency is determined by variance analysis (ANOVA), and parameters with a contribution rate greater than 15% are selected as key parameters.

[0027] This technical solution first defines the key parameters that affect mass transfer efficiency and their value ranges, and defines a reasonable range for parameter sensitivity analysis. Using the scientific method of analysis of variance (ANOVA), it is possible to quantify the degree of influence of each parameter on mass transfer efficiency, and to screen out the key parameters that truly have a significant impact on mass transfer efficiency by setting a contribution rate threshold. This process effectively avoids the invalid calculation of a large number of irrelevant parameters, focuses on key parameters for optimization, and greatly improves optimization efficiency. At the same time, accurate identification of key parameters makes the optimization direction clearer, and in-depth research and adjustments can be carried out on these key factors, thereby more effectively improving mass transfer efficiency. This solves the problems of confusing parameter analysis, low optimization efficiency, and unclear direction in the traditional optimization process, and provides strong support for the efficient optimization of the bipolar plate flow channel structure of the AEM hydrogen electrolyzer.

[0028] Traditional mass transfer efficiency calculation methods have the following technical problems: they do not fully consider the impact of flow channel structure on mass transfer and cannot accurately reflect the mass transfer differences under different flow channel designs; they ignore reaction kinetics factors and cannot reflect the effects of temperature, activation energy, etc. on the mass transfer process; the calculation model is simple and does not incorporate key factors such as fluid diffusion and porous media characteristics, resulting in a large deviation between the calculated results and the actual mass transfer efficiency, making it difficult to serve as an accurate basis for flow channel structure optimization and performance evaluation.

[0029] Based on this, the mass transfer efficiency is calculated by the following formula: ; in: is the current density (A / cm²); is the molar mass of water (g / mol); is the liquid water concentration (mol / cm³); is the cross-sectional area of ​​the flow channel (cm²); is the molecular diffusion coefficient (cm² / s); is the porous medium tortuosity factor; is the reaction activation energy (J / mol); is the gas constant ; is the operating temperature (K).

[0030] In an AEM hydrogen production electrolyzer, mass transfer directly impacts the efficiency and stability of the electrolysis reaction. This formula comprehensively and meticulously considers multiple key factors influencing mass transfer efficiency.

[0031] Among them, the current density (A / cm²) is a parameter that measures the current per unit area. Its size directly reflects the intensity of the electrochemical reaction. A larger current density means that more reactants need to participate in the reaction, which places higher demands on mass transfer efficiency. The molar mass of water is (g / mol) is an inherent property of a substance and is used to convert the amount of a substance to its mass. It is essential for calculating the amount of substance transferred during mass transfer. Liquid water concentration (mol / cm³) reflects the richness of the reactants. The higher the value, the more substances are available for reaction in theory. However, too high a concentration may affect the diffusion rate, so comprehensive consideration is needed.

[0032] Flow channel cross-sectional area (cm²) determines the flow space of the fluid in the flow channel. The area size affects the flow rate of the fluid and the contact with the flow channel wall, which in turn affects mass transfer; molecular diffusion coefficient (cm² / s) characterizes the ability of molecules to diffuse in a medium. It is related to the properties of the substance itself and the environmental conditions and is a key parameter for describing the molecular diffusion behavior in the mass transfer process. This is a major innovation of the formula. Its introduction aims to quantify the impact of complex structures such as fractal tree-like flow channels on the mass transfer path. Since the actual flow channel is not an ideal straight channel, the tortuous path will increase the mass transfer resistance. The larger the value, the more tortuous the mass transfer path is and the greater the mass transfer difficulty is.

[0033] The exponential term in the second half of the formula It reflects the effect of reaction kinetics on mass transfer efficiency. (J / mol) represents the energy barrier that needs to be overcome for a chemical reaction to occur. Its value is related to the difficulty of the reaction. The higher the activation energy, the more difficult the reaction is to proceed, and the mass transfer efficiency will also be inhibited. The gas constant Is a fixed physical constant used to build a bridge between energy, amount of substance and temperature in formulas; operating temperature (K) directly affects the thermal motion of molecules. As temperature rises, molecular thermal motion intensifies, helping to improve mass transfer efficiency. However, excessively high temperatures can adversely affect material and reaction stability. This formula organically combines the aforementioned parameters, not only taking into account the fluid mechanics of material transfer, but also incorporating flow channel structural characteristics and reaction kinetic parameters. Compared to traditional mass transfer efficiency calculation methods, it can more accurately reflect the mass transfer efficiency of the bipolar plate flow channels in AEM hydrogen electrolyzers under different operating conditions and structural designs. This provides a quantitative and reliable evaluation metric for the optimized design of flow channel structures, helping engineers to specifically adjust design parameters to achieve higher mass transfer efficiency.

[0034] The mass transfer efficiency calculation formula provided by this technical solution takes into account a variety of key factors affecting mass transfer. The porous medium tortuosity factor is introduced into the formula. , quantified the effect of flow channel structure (such as the complex structure of fractal tree-like flow channels) on the mass transfer path; through the exponential term Reflects the reaction kinetic parameters, including the reaction activation energy and operating temperature Impact on mass transfer efficiency; at the same time, combined with current density , liquid water concentration , flow channel cross-sectional area and the molecular diffusion coefficient Parameters such as mass transfer efficiency and mass transfer efficiency comprehensively reflect factors such as fluid flow, substance concentration, and diffusion characteristics. Compared with traditional calculation methods, this formula can more accurately calculate the mass transfer efficiency under different flow channel structures and operating conditions. It provides a precise quantitative indicator for the design and optimization of bipolar plate flow channel structures, effectively solving the problem of traditional calculation methods being inaccurate and unable to reflect actual mass transfer conditions, and helps to accurately evaluate and optimize the mass transfer performance of AEM hydrogen electrolyzers.

[0035] Existing methods for calculating pressure drop in bipolar plate flow channels suffer from the following technical issues: Most are based on simple fluid dynamics formulas, failing to consider the complex structure of the flow channels, such as the impact of the branching characteristics of a fractal tree structure on pressure drop. They also ignore the coupling relationship between flow channel structural parameters, such as fractal dimension and branching angle, and fluid dynamics parameters, resulting in calculations that fail to accurately reflect actual pressure drop. In complex flow channel systems, traditional calculation methods cannot effectively predict pressure drop variations, making it impossible to rationally design flow channels to control energy consumption, impacting the economic and stable operation of AEM hydrogen electrolyzers.

[0036] Based on this, the pressure drop is calculated by the following formula: ; in: is the dynamic viscosity of the fluid ; is the flow channel length (cm); is the volume flow rate (cm³ / s); is the flow channel radius (cm); is the fluid density (g / cm³); is the fractal dimension; is the branching angle (°).

[0037] In the bipolar plate flow channels of the AEM hydrogen production electrolyzer, accurate calculation of the pressure drop is crucial for optimizing the flow channel design and reducing energy consumption. The formula consists of two parts, corresponding to different types of pressure loss. The first half of the formula Based on traditional fluid mechanics principles, it is used to calculate the pressure loss along the flow path of viscous fluid.

[0038] Among them, the dynamic viscosity of the fluid It reflects the characteristics of the fluid's internal resistance to relative flow. The greater the viscosity, the greater the internal friction when the fluid flows, and the greater the pressure loss along the flow path. (cm) directly determines the distance the fluid flows in the flow channel. The longer the distance, the longer the friction with the flow channel wall, and the greater the pressure loss; volume flow rate (cm³ / s) reflects the volume of fluid passing through the flow channel per unit time. The greater the flow rate, the more intense the interaction between the fluid and the wall, and the corresponding increase in pressure loss; the flow channel radius (cm) has a significant impact on the pressure loss along the process, and its fourth power appears in the denominator, which means that a small change in radius will cause a large change in pressure loss. Increasing the radius can effectively reduce the pressure loss along the process.

[0039] The second half of the formula The focus is on the influence of the fractal tree-like flow channel structure on the local pressure loss. (g / cm³) is a basic physical property of the fluid, which is related to the inertia of the fluid and is an important parameter in calculating the local pressure loss; and The meaning is the same as the first half, and the flow channel cross-sectional area (cm²) further participates in the calculation of local pressure loss and affects the flow state of the fluid at the branch of the flow channel. The core innovation of this formula is the fractal dimension It is used to describe the complexity of the fractal tree-like flow channel structure. The larger the value, the more complex the flow channel structure, the greater the flow resistance of the fluid at the branch, and the greater the pressure loss; branch angle (°) directly affects the turning of the fluid at the branch. The larger the angle, the greater the impact and energy loss when the fluid turns. By introducing these two parameters, the formula can accurately quantify the impact of the fractal tree-like flow channel structure on local pressure loss.

[0040] This formula combines the traditional calculation of along-the-line pressure loss with the calculation of local pressure loss caused by the fractal tree-like flow channel structure. It comprehensively and accurately reflects the pressure drop of the bipolar plate flow channel of the AEM hydrogen electrolyzer under different operating conditions and structural parameters. It provides an accurate calculation basis for balancing mass transfer efficiency and pressure drop during flow channel design, helping engineers design flow channel structures with lower energy consumption and better performance.

[0041] The pressure drop calculation formula given by this technical solution fully considers the structural characteristics of the fractal tree-like flow channel and its interaction with the fluid mechanics parameters. In the formula, It is partly based on the traditional fluid flow pressure drop calculation, which describes the pressure loss along the flow path of viscous fluid; Some of them introduce fractal dimension and branch angles , which reflects the influence of the flow channel structure on the local pressure loss, especially the pressure change at the branch. In this way, the formula can accurately calculate the pressure drop of the fractal tree-like flow channel under different working conditions, and clearly show the influence of the flow channel structure parameters on the pressure drop. Compared with the traditional calculation method, it provides a more accurate pressure drop prediction for the bipolar plate flow channel design, helps to balance the mass transfer efficiency and pressure drop during the design process, optimize the flow channel structure to reduce energy consumption, and improve the operating economy and stability of the AEM hydrogen electrolyzer, effectively solving the problem of inaccurate traditional pressure drop calculation and inability to adapt to complex flow channels.

[0042] During the operation of the bipolar plate flow channels of the AEM hydrogen production electrolyzer, the following technical problems exist in the evaluation of temperature distribution uniformity: there is a lack of scientific and reasonable quantitative indicators to evaluate the temperature distribution, making it difficult to intuitively judge whether the flow channel temperature distribution is uniform; traditional evaluation methods only focus on the average temperature, ignore the degree of temperature dispersion, and cannot detect local overheating or overcooling phenomena, which may lead to a decrease in electrochemical reaction performance, accelerated material aging, and even affect the safe operation of the electrolyzer; the lack of a unified evaluation standard makes it difficult to compare the temperature distribution performance of different design schemes, which is not conducive to the optimization and improvement of the flow channel structure.

[0043] Based on this, the temperature distribution uniformity is evaluated by the following formula: ; in: is the temperature standard deviation (K); is the average temperature (K). The temperature distribution uniformity evaluation formula proposed in this technical solution is based on the temperature standard deviation With average temperature Based on the ratio of , an intuitive and effective quantitative index is constructed. The temperature standard deviation reflects the degree of dispersion of temperature data. The smaller the value, the more uniform the temperature distribution. By subtracting the ratio from 1, we get The closer the value is to 1, the better the temperature distribution uniformity. This formula can accurately reflect the temperature fluctuation in the flow channel. It not only focuses on the average temperature, but also highlights the discrete characteristics of the temperature, so that local temperature anomalies can be discovered in a timely manner. Through this quantitative indicator, the temperature distribution uniformity of different flow channel structure design schemes can be objectively compared and evaluated, providing a clear direction for the optimization of the flow channel structure. In the optimization process, to improve With the temperature distribution as the target, it can effectively improve the thermal management performance of the flow channel, avoid the adverse effects of local overheating or overcooling on the electrochemical reaction and material properties, ensure the stable and efficient operation of the AEM hydrogen electrolyzer, and solve the problems of traditional temperature distribution evaluation lacking quantitative indicators, difficulty in discovering local anomalies and inability to effectively compare solutions.

[0044] During the operation of the AEM hydrogen production electrolyzer, the uniformity of temperature distribution has a significant impact on the electrochemical reaction performance, material stability and the overall life of the electrolyzer. The traditional method only focuses on the average temperature and cannot effectively evaluate the uniformity of temperature distribution. This formula introduces the temperature standard deviation to (K) and average temperature (K), a scientific and intuitive quantitative index of temperature distribution uniformity was constructed. Average temperature It is a parameter that reflects the overall temperature level in the flow channel and provides a benchmark for evaluating the uniformity of temperature distribution. It is an important indicator in statistics for measuring the degree of data dispersion. In this formula, it accurately describes the fluctuation of the temperature of each point in the flow channel relative to the average temperature. The smaller the value, the smaller the difference between the temperature at each point and the average temperature, that is, the more uniform the temperature distribution; on the contrary, The larger the value, the more uneven the temperature distribution is, and there may be local overheating or overcooling. The ratio of the temperature distribution uniformity index is obtained , this design makes The value range of is between 0 and 1, which is easy to understand and compare. When it approaches 1, it means Approaches 0, that is, the temperature distribution in the flow channel is very uniform; when When it approaches 0, it means that the temperature distribution is extremely uneven. This formula provides an important quantitative tool for the thermal management of the bipolar plate flow channel of the AEM hydrogen electrolyzer. Engineers can use this formula to objectively and accurately evaluate and compare the temperature distribution uniformity of different flow channel structure design schemes. In the process of flow channel structure optimization, in order to improve With the temperature distribution value as the target, the flow channel design parameters can be adjusted in a targeted manner, such as changing the flow channel shape and size or optimizing the cooling method, so as to effectively avoid the adverse effects of local overheating or overcooling on the electrochemical reaction and material properties, ensure the stable and efficient operation of the AEM hydrogen electrolyzer, and solve the problems of traditional temperature distribution evaluation lacking effective quantitative indicators and difficulty in comparing different design schemes.

[0045] The following technical problems exist in the multi-objective optimization process of the bipolar plate flow channel structure of the AEM hydrogen production electrolyzer: traditional optimization algorithms find it difficult to find a balanced solution among multiple conflicting objectives, such as maximizing mass transfer efficiency, minimizing pressure drop, and optimizing temperature distribution uniformity; unreasonable algorithm parameter settings will cause the optimization results to fall into local optimality and fail to obtain a global optimal solution; there is a lack of clear evaluation criteria for optimization results, making it difficult to determine whether the optimized flow channel structure meets actual application requirements, making the optimization process blind and inefficient.

[0046] Based on this, in the multi-objective optimization algorithm, the population size is set to 100-200, the number of iterations is 50-100, the crossover probability is 0.8-0.9, and the mutation probability is 0.05-0.1; the optimized flow channel structure must meet the requirements of an increase in mass transfer efficiency of more than 30%, a reduction in pressure drop of more than 20%, and a temperature standard deviation of less than 5K.

[0047] This technical solution rationally sets the parameters of the multi-objective optimization algorithm and defines the evaluation criteria for the optimization results. Appropriate settings for population size, number of iterations, crossover probability, and mutation probability ensure that the non-dominated sorting genetic algorithm (NSGA-II) maintains sufficient diversity during the search process to avoid falling into local optima, while also converging to the global optimal solution or a near-optimal solution within a reasonable computational time.

[0048] By setting specific optimization targets for mass transfer efficiency, pressure drop, and temperature standard deviation, the algorithm provides clear quantitative indicators and evaluation criteria for optimizing the flow channel structure. During the optimization process, the algorithm can adaptively adjust based on these targets, finding the optimal balance between multiple objectives and generating a Pareto optimal solution set.

[0049] Engineers can select the most appropriate flow channel structure solution from this solution set based on actual needs. This effectively solves the problems of traditional optimization algorithms such as their inability to balance multiple objectives, their susceptibility to falling into local optimality, and their lack of evaluation criteria. It improves the scientific nature and practicality of the bipolar plate flow channel structure optimization of AEM hydrogen electrolyzers, and ensures that the optimized flow channel structure can achieve efficient and stable operation in actual applications.

[0050] The bipolar plates of AEM hydrogen production electrolyzers face the following technical problems in actual applications: traditional bipolar plate materials (such as ordinary metal materials) have poor corrosion resistance and are prone to corrosion in alkaline electrolyte environments, shortening their service life; the interface mass transfer performance between the material surface and the electrolyte and gas is poor, affecting the efficiency of the electrochemical reaction; the pore structure of the bipolar plate is unreasonable, which easily leads to bubble blockage, hindering material transfer and reducing electrolyzer performance; the comprehensive performance of the material cannot meet the high current density and long life operation requirements of the AEM hydrogen production electrolyzer.

[0051] Based on this, the bipolar plate material is a surface-modified titanium alloy or stainless steel, and the surface is prepared with a gradient pore structure by laser microporous technology. The micropore diameter is 50-200μm, the porosity is 15%-30%, and it is coated with a nano-composite coating with a thickness of 50-200nm. The coating is composed of titanium carbonitride (TiCN) and graphene (GO).

[0052] This technical solution improves bipolar plates in terms of material selection, surface treatment, and coating design. Surface-modified titanium alloy or stainless steel is selected as the base material, leveraging its excellent mechanical properties and modifiable characteristics to provide a solid structural foundation for the bipolar plate. The gradient pore structure, created through laser microporation technology, maintains a reasonable range of micropore diameter and porosity, effectively guiding the discharge of bubbles and preventing bubble accumulation and blockage within the flow channel. This also increases the contact area between the material and the electrolyte and gas, optimizing the material transfer path and improving mass transfer efficiency. The applied nanocomposite coating combines the high hardness and corrosion resistance of titanium carbonitride (TiCN) with the excellent conductivity and chemical stability of graphene (GO). This coating forms a dense protective layer that effectively resists corrosion from alkaline electrolytes and extends the service life of the bipolar plate. It also improves the wettability and electronic conductivity of the material surface, promoting electrochemical reactions. Through multi-dimensional material optimization design, this technical solution effectively solves the problems of poor corrosion resistance, poor mass transfer, and unreasonable pore structure of traditional bipolar plate materials, significantly improving the overall performance of the bipolar plates and providing reliable guarantees for the stable and efficient operation of the AEM hydrogen production electrolyzer, enabling it to adapt to high current density working environments and meet the needs of long-life operation.

[0053] Analysis shows that during the simulation and optimization process of the bipolar plate flow channel structure-mass transfer efficiency parameters of the AEM hydrogen production electrolyzer, the following technical problems exist: due to simplified assumptions and inaccurate parameter settings, the simulation model deviates from the actual operating conditions, resulting in unreliable optimization results; there is a lack of effective experimental verification methods, which makes it impossible to accurately evaluate the accuracy of the simulation model and the actual performance of the optimized flow channel structure; traditional verification methods have limited observation capabilities for complex phenomena inside the electrolyzer, making it difficult to obtain key information to correct the model, which makes the simulation disconnected from actual applications and the optimization results difficult to transform into actual productivity.

[0054] Based on this, during the experimental verification process, an in-situ optical microscope was used to observe the bubble behavior in the flow channel, the interfacial impedance was measured by electrochemical impedance spectroscopy (EIS), and the temperature distribution was monitored by a thermal imager. The prediction error of the revised model was less than 10%, and the deviation between the experimentally measured mass transfer efficiency and the simulation results was less than 5%.

[0055] This technical solution has established a systematic and precise experimental verification and model correction system. By using an in-situ optical microscope to observe the behavior of bubbles in the flow channel, the generation, growth, detachment and transport process of bubbles can be obtained in real time and intuitively, providing an intuitive basis for analyzing the mass transfer and two-phase flow characteristics in the flow channel; by using electrochemical impedance spectroscopy (EIS) to measure the interface impedance, the electrochemical reaction kinetics and mass transfer process at the electrode-electrolyte interface can be deeply studied, and the interface performance can be accurately evaluated; the thermal imager can quickly and comprehensively monitor the temperature distribution of the flow channel and promptly detect local overheating or abnormal temperature areas. Through these advanced experimental methods, key information on the operation of the electrolyzer is obtained from multiple dimensions, and compared and analyzed with the simulation results. Based on the experimental data, the boundary conditions and material parameters in the model are corrected, and the model prediction error is strictly controlled to be less than 10%, ensuring that the deviation between the experimentally measured mass transfer efficiency and the simulation results is less than 5%.

[0056] This process effectively bridges the gap between simulation and reality, improves the accuracy and reliability of the simulation model, and enables the optimization results based on the model to be effectively applied to the design of the bipolar plate flow channel structure of AEM hydrogen production electrolyzers. It realizes the effective transformation of simulation optimization methods from theoretical research to engineering practice, and promotes the practical application and development of AEM hydrogen production technology.

[0057] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. Bipolar plate flow channel structure-mass transfer efficiency parameter simulation optimization method, characterized in that: include: Flow channel structure design: The bipolar plate flow channel is constructed using a fractal tree network topology. The flow channel is composed of multiple levels of branch channels. The cross-sectional size of each level of branch channels decreases in a self-similar proportion, forming a three-dimensional flow channel network with fractal dimension. Multi-physics coupling modeling: Based on computational fluid dynamics and the finite element method, a multi-physics coupling model that includes fluid flow, electrochemical reactions, and heat and mass transfer is established. The model simultaneously considers the turbulent effects within the flow channel, the interfacial impedance of the porous medium diffusion layer, and the reaction kinetic parameters. Parameter sensitivity analysis: Orthogonal experimental design was used to screen key parameters affecting mass transfer efficiency, including flow channel fractal dimension, branching angle, porosity, and surface wettability parameters, and construct a parameter response surface. Multi-objective optimization algorithm: A non-dominated sorting genetic algorithm is used to globally optimize the multi-physics coupling model, with maximizing mass transfer efficiency, minimizing pressure drop, and optimizing temperature distribution uniformity as the objective functions, to obtain the Pareto optimal solution set of flow channel structure and operating parameters; Experimental verification and correction: Bipolar plate samples were prepared based on the optimized flow channel structure. The simulation results were verified through electrochemical testing and in-situ visualization technology, and the boundary conditions and material parameters in the model were corrected.

2. The bipolar plate flow channel structure-mass transfer efficiency parameter simulation optimization method according to claim 1, characterized in that: The fractal tree-like flow channel has 4-6 branching levels, the first-level flow channel width is 0.8-1.2 mm, the last-level flow channel width is 0.1-0.3 mm, the width ratio of two adjacent flow channels is 1:2 to 1:3, and the ratio of flow channel depth to width is 0.8:1 to 1.2:

1.

3. The bipolar plate flow channel structure-mass transfer efficiency parameter simulation optimization method according to claim 1, characterized in that: In the multi-physics coupling model, the fluid flow governing equations use the Reynolds-averaged Navier-Stokes equations, combined with the k-ωSST turbulence model to simulate turbulent flow within the flow channel. The electrochemical reaction process is described by the Butler-Volmer equation, taking into account the kinetic parameters of the anode oxygen evolution reaction and the cathode hydrogen evolution reaction. The heat and mass transfer processes include liquid water diffusion, bubble generation and transport, and heat conduction in the solid domain.

4. The bipolar plate flow channel structure-mass transfer efficiency parameter simulation optimization method according to claim 1, characterized in that: In the parameter sensitivity analysis, the fractal dimension α ranges from 1.5 to 2.5, the branching angle θ ranges from 30° to 60°, the porosity ε ranges from 0.3 to 0.6, and the surface contact angle φ ranges from 60° to 120°. The contribution rate of each parameter to the mass transfer efficiency is determined by variance analysis, and parameters with a contribution rate greater than 15% are selected as key parameters.

5. The bipolar plate flow channel structure-mass transfer efficiency parameter simulation optimization method according to claim 1, characterized in that: The mass transfer efficiency is calculated by the following formula: ； in: is the current density; is the molar mass of water; is the liquid water concentration; is the cross-sectional area of ​​the flow channel; is the molecular diffusion coefficient; is the porous medium tortuosity factor; is the reaction activation energy; is the gas constant; is the operating temperature.

6. The bipolar plate flow channel structure-mass transfer efficiency parameter simulation optimization method according to claim 1, characterized in that: The pressure drop is calculated by the following formula: ； in: is the fluid dynamic viscosity; is the flow channel length; is the volume flow rate; is the flow channel radius; is the fluid density; is the fractal dimension; is the branch angle.

7. The bipolar plate flow channel structure-mass transfer efficiency parameter simulation optimization method according to claim 1, characterized in that: The temperature distribution uniformity is evaluated by the following formula: ； in: is the temperature standard deviation; is the average temperature.

8. The bipolar plate flow channel structure-mass transfer efficiency parameter simulation optimization method according to claim 1, characterized in that: In the multi-objective optimization algorithm, the population size is set to 100-200, the number of iterations is 50-100, the crossover probability is 0.8-0.9, and the mutation probability is 0.05-0.1; the optimized flow channel structure must meet the requirements of an increase in mass transfer efficiency of more than 30%, a reduction in pressure drop of more than 20%, and a temperature standard deviation of less than 5K.

9. The bipolar plate flow channel structure-mass transfer efficiency parameter simulation optimization method according to claim 1, characterized in that: The bipolar plate material is a surface-modified titanium alloy or stainless steel, and the surface is prepared with a gradient pore structure using laser microporous technology. The micropore diameter is 50-200 μm, the porosity is 15%-30%, and it is coated with a nano-composite coating with a thickness of 50-200 nm. The coating is composed of titanium carbonitride and graphene.

10. The bipolar plate flow channel structure-mass transfer efficiency parameter simulation optimization method according to claim 1, characterized in that: During the experimental verification process, an in-situ optical microscope was used to observe the behavior of bubbles in the flow channel, the interfacial impedance was measured using electrochemical impedance spectroscopy, and the temperature distribution was monitored using a thermal imager. The prediction error of the revised model was less than 10%, and the deviation between the experimentally measured mass transfer efficiency and the simulation results was less than 5%.

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