A method for designing a flow field structure for a solid oxide electrolysis cell

Abstract

The application discloses a flow field structure design method for a solid oxide electrolytic cell, and belongs to the technical field of solid oxide electrolytic cells; the method is based on the establishment of an electrolytic cell flow field topology domain, fluid flow, heat transfer and mass transfer processes are coupled modeling, the flow field structure is optimized and designed through the construction of multi-physical field constraint conditions, so that the flow field structure suitable for high-temperature electrolysis working conditions is obtained; the method takes improving the concentration of reaction gas and improving the temperature distribution as the design target, and the volume fraction of the flow channel and the pressure drop of the inlet and outlet are constrained, so that the obtained flow field structure can consider the mass transfer performance, the heat management performance and the operation stability; the flow field structure obtained by the method can effectively reduce the internal temperature gradient of the electrolytic cell, improve the gas distribution uniformity of the reaction area, and thus improve the current density and the operation stability of the electrolytic cell.

Description

Technical Field

[0001] This invention belongs to the field of solid oxide electrolytic cell technology, specifically a flow field structure design method for solid oxide electrolytic cells. Background Technology

[0002] Solid oxide electrolysis cells (SOECs) are a highly efficient water electrolysis technology operating at high temperatures. They enable the electrolysis of water vapor and water / carbon dioxide, offering advantages such as high energy utilization and fast electrode reaction rates. The flow field, a key structural component of the SOEC, plays a crucial role in uniformly distributing reactant gases, facilitating product outflow, conducting current, and managing heat. Its structural design directly impacts gas transport uniformity, temperature field distribution, electrochemical efficiency, and long-term durability. Therefore, flow field optimization is essential for improving the overall performance of the SOEC.

[0003] Existing flow field design methods are mostly based on preset geometries, improving mass transfer and pressure drop by changing the channel width, rib width ratio, or introducing turbulence structures; some studies use biomimetic leaf veins and porous materials to improve gas distribution and thermal management capabilities. While these methods can improve performance to some extent, they are limited by the designer's experience and fixed geometric framework, resulting in low design freedom and difficulty in breaking through traditional channel layouts, thus limiting further performance improvements.

[0004] Topology optimization, one of the three major structural optimization methods, does not require pre-defined flow channel shapes and can autonomously find the optimal solution within a larger design space, demonstrating significant advantages in the fuel cell field in recent years. Existing research shows that topology optimization can improve flow and mass transfer performance, resulting in more uniform reactant supply and validating its effectiveness in fuel cell flow field design. However, most existing studies on fuel cell flow field topology optimization are limited to improving flow and mass transfer processes, failing to incorporate the strong coupling characteristics of multiple electrical, thermal, and mass fields within fuel cells.

[0005] In SOEC (Self-Organizing Electrolyte), temperature distribution has a decisive impact on the efficiency and structural reliability of the electrolytic cell. The combined effect of the endothermic and exothermic effects of the electrolysis reaction leads to uneven local temperature fluctuations. Excessive temperature differences not only reduce electrolysis efficiency but also increase interfacial thermal stress, inducing problems such as material cracking and performance degradation. Existing research has shown that optimizing the flow channel structure can effectively improve the uniformity of the temperature field, thereby enhancing reaction activity and improving long-term operational stability. Therefore, it is urgent to construct a multiphysics coupled topology optimization framework tailored to the operational requirements of SOEC to achieve efficient flow field structure design and further performance improvement. Summary of the Invention

[0006] To address the problem that existing flow field designs rely on pre-defined geometric structures and struggle to accommodate multi-physics requirements, this invention proposes a flow field structure design method for solid oxide electrolyzers (SOECs). This method aims to solve the issues of insufficient coupling and poor adaptability in gas distribution, current transport, and heat conduction inherent in previous flow field design methods. First, a two-dimensional porous topological domain is constructed, coupling and solving the fluid flow, heat transfer, and mass transfer processes. Then, a flow field topology optimization model is built with multiple objectives, including improving concentration distribution and temperature distribution, generating an optimized flow field structure that meets the requirements of SOEC operations. Simulation results show that, compared to traditional parallel flow fields, this method can increase current density by approximately 6.61%, significantly enhance gas transport and temperature distribution uniformity, thereby improving the operational stability and lifespan of the electrolyzer. This method combines high design freedom with strong adaptability, providing a feasible solution for the structural optimization of SOECs and other multi-physics coupled energy devices.

[0007] The present invention adopts the following technical solution: A flow field structure design method for solid oxide electrolytic cells, the method comprising the following steps: S1 establishes a two-dimensional porous flow field topology based on the geometry, inlet and outlet arrangement and operating characteristics of the solid oxide electrolytic cell; S2 determines the topological parameters of the two-dimensional porous flow field based on the multiphysics parameters, flow field parameters, fluid physics parameters and boundary conditions of the solid oxide electrolytic cell; S3 introduces design variables to characterize the flow field structure distribution within the two-dimensional porous flow field topology domain, and describes the relationship between the design variables and the parameters of the two-dimensional porous flow field topology domain through an interpolation function to obtain the first optimization model for the flow field structure design. S4 constructs an objective function by measuring the fluid concentration distribution and temperature distribution in the two-dimensional porous flow field topology. At the same time, it sets pressure constraints to control the pressure drop at the inlet and outlet of the two-dimensional porous flow field topology and sets volume fraction constraints to constrain the proportion of porous channels in the two-dimensional porous flow field topology, thus obtaining the second optimization model for flow field structure design. S5 uses multiphysics field coupled control equations to calculate the flow field topology optimization model for the first optimization model and the second optimization model; S6. Mesh the two-dimensional porous flow field topology domain; perform a filtering projection operation on the design variables, update the design variables, and iteratively solve the flow field topology optimization model; S7 determines whether the iterative solution result of the flow field topology optimization model has converged: if the convergence result is met, proceed to the next step; otherwise, return to step S3 to update the design variables of the interpolation function and repeat the iterative process from step S4 to S6. After the flow field topology optimization model S8 converges, the optimal flow field structure is obtained. The optimal flow field structure is processed and stretched to obtain a three-dimensional flow field structure. A multi-physics coupling simulation model of a three-dimensional solid oxide electrolytic cell is established, and the performance of the three-dimensional flow field structure is analyzed.

[0008] Furthermore, the two-dimensional porous flow field topology includes porous ribs and porous channels, wherein: the porous ribs are composed of ribs and a diffusion layer; the porous channels are composed of channels and a diffusion layer, wherein: The parameters of the porous rib plate are: ; The parameters of the porous flow channel are: ; in: Indicates rib parameters, Indicates flow channel parameters, Indicates the parameters of the diffusion layer; Indicates the thickness of the flow field, This indicates the thickness of the cathode diffusion layer.

[0009] Further, in step S3, the solid isotropic material penalty method is used to describe the relationship between the design variables and the topological parameters of the two-dimensional porous flow field, obtaining the first optimization model for the flow field structure design, including: 301 employs a density-based topology optimization method to optimize the flow field structure, defining design variables. i By varying the values ​​of the design variables, the spatial distribution of the porous flow channel region and the porous rib region within the two-dimensional porous flow field topology can be controlled; that is: when i =1 indicates a porous flow channel; i =0 indicates a porous rib; 302 uses the solid isotropic material penalty method to describe the porosity, permeability, density, diffusion coefficient, thermal conductivity, and specific heat capacity of the two-dimensional porous flow field topology domain in relation to the design variables. i The relationship allows for continuous control of various physical properties of the porous flow field topology through the design variables, thereby achieving comprehensive optimization of the flow field structure in terms of mass and heat transfer performance, i.e.: ; ; ; ; ; ; In this context, the subscript pr indicates porous rib material, and the subscript pc indicates porous flow channel material. q ε ,q κ , q ρ , , , These are the penalty factors for porosity, permeability, density, diffusion coefficient, thermal conductivity, and specific heat capacity, respectively. e pr , e pc Porosity of the porous ribbed domain and the porous flow channel domain, respectively; k pr , k pc These represent the permeability of the porous ribbed domain and the porous flow channel domain, respectively. r pr , r pc These represent the densities of the porous ribbed region and the porous flow channel region, respectively. D pr , D pc These represent the diffusion coefficients of the porous ribbed domain and the porous flow channel domain, respectively. l pr , l pc These represent the thermal conductivity of the porous ribbed region and the porous flow channel region, respectively. , These represent the specific heat capacities of the porous ribbed region and the porous flow channel region, respectively.

[0010] Furthermore, in step S4, an objective function is constructed to adjust the fluid concentration distribution and temperature distribution in the two-dimensional porous flow field topology domain according to the following formula. F ,Right now: ; ; ; ; in, This represents the average concentration of the fluid. This represents the average temperature of the topological domain; This represents the average concentration weighting coefficient; This represents the average temperature weighting factor; This is the concentration normalization constant; Ω is the temperature normalization constant; Ω is the topology optimization design domain. Water vapor concentration; T For temperature.

[0011] Furthermore, the flow field topology optimization model described in step S5 is as follows: Find Maximize Subject to

[0012]

[0013]

[0014]

[0015]

[0016]

[0017] in, r Density; p For pressure; p Set the pressure drop value for import and export; V This represents the volume fraction of the fluid domain. For fluid velocity; For quality source items; It is the identity matrix; The diffusion coefficient is determined by the interpolation function; This is the source term for the reaction of matter.

[0018] Further, step S6 involves dividing the two-dimensional porous flow field topology domain into a mesh, updating the design variables, and iteratively solving the flow field topology optimization model; including: 601 Finite element meshing was performed on the two-dimensional porous flow field topology domain. The meshing method was selected from mapped mesh, free quadrilateral mesh, and free triangular mesh according to the shape of the topology domain. The meshing was based on the ability to match the structural features of the model, and the mesh independence was verified to determine the number of meshes. 602 Design Variables i Perform a Helmholtz filtering operation to obtain the first topological variable after filtering. i f and for the first topological variable i f Perform hyperbolic tangent projection to obtain the projected topological variables. i p Filtered projections prevent checkerboard patterns from appearing in design variables; 603 uses the adjoint method to calculate sensitivity and updates the design variable θ after filtering projection using the moving asymptote algorithm.

[0019] Beneficial effects 1. Multi-physics field coupling optimization: Incorporate multi-physics field mechanisms such as fluid flow, heat transfer and mass transfer into the optimization process to achieve coordinated matching between the flow field structure and the performance requirements of the electrolytic cell.

[0020] 2. Improved current density and temperature uniformity: The enhanced heat and mass transfer performance of the flow field obtained through topology optimization effectively improves the performance of the electrolytic cell. For example, in this case, the current density of the solid oxide electrolytic cell is increased by about 6.61%.

[0021] 3. High degree of design freedom and strong versatility: The optimization process does not pre-determine the specific shape of the flow channel, and can automatically generate the optimal flow field structure under the premise of meeting constraints such as pressure and volume fraction; the method has good scalability and is applicable to the design of flow fields of different sizes and different operating conditions.

[0022] 4. Reduce design costs and time: Automatic iterative optimization through algorithms replaces manual trial and error and experience screening, reducing design workload and shortening the R&D cycle, thereby improving design efficiency. Attached Figure Description

[0023] Figure 1 Schematic diagram of flow field structure optimization process; Figure 2 Flow field schematic diagram: (a) 3D model of SOEC fuel side; (b) Composition of porous flow field topology; Figure 3 Two-dimensional porous flow field topology; Figure 4 Flow field performance comparison: (a) Schematic diagram of SOEC fuel side three-dimensional model with different flow field structures; (b) Current density of the cathode electrode mid-section; (c) Water concentration of the cathode electrode mid-section; (d) Temperature of the cathode electrode mid-section; Detailed Implementation

[0024] The following is in conjunction with the appendix Figure 1 -Appendix Figure 4 The present invention will be described in detail as follows: This invention provides a flow field structure design method for solid oxide electrolyzers (SOECs). The method first selects a two-dimensional flow field topology domain based on the geometry and actual operating conditions of the SOEC to define the optimization space, and then determines the topology domain parameters, including boundary conditions and physical / control parameters. Next, design variables are defined to clarify the flow field distribution, and a design domain interpolation function is established to describe the topology domain parameters. Based on this, an objective function and constraints are constructed to clarify the optimization direction and limitations. Then, based on the topology optimization method and the multiphysics coupling control equations for fluid flow, heat transfer, and mass transfer, a flow field topology optimization model is constructed, and numerical iterative solutions are performed. The process then proceeds to a decision stage: determining whether the optimization has converged. If not, the process returns to "establishing the design domain interpolation function," updates the variables, and repeats the subsequent steps; if so, the loop is exited, and the optimal flow field structure is finally obtained. The optimized topology structure is post-processed and stretched to obtain a three-dimensional flow field structure. A complete three-dimensional multiphysics coupling simulation model of the solid oxide electrolyzer is established to analyze the impact of the topology-optimized flow field on the electrolyzer performance. The specific process is as follows: Figure 1 As shown, the steps of a flow field structure design method for a solid oxide electrolytic cell in this embodiment are as follows: 1. Establishing a porous topological flow field domain: A two-dimensional porous flow field topological domain is established based on the geometry, inlet and outlet arrangement, and operating characteristics of the solid oxide electrolytic cell; Performing flow field topology optimization in a three-dimensional model involves high computational cost and poor convergence. Therefore, a dimensionality-reduced two-dimensional model is used for flow field topology optimization research, which significantly reduces the solution difficulty and improves optimization efficiency. However, there is significant coupling transport between the flow field and the diffusion and catalytic layers in SOEC, and the three-dimensional transport effect cannot be ignored. For example... Figure 2 As shown, in addition to diffusion along the flow channel direction, the gas in the channel also diffuses laterally between adjacent channels through the diffusion layer, thus affecting the overall flow and mass transfer distribution. Based on this, in the two-dimensional modeling, the flow field and diffusion layer are equivalent to a porous media layer: such as... Figure 2 As shown in (b), the ribs and diffuser layer are equivalent to porous ribs, and the flow channels and diffuser layer are equivalent to porous flow channels. Together, they constitute a two-dimensional porous topological domain for topology optimization. The porous flow field topological domain structure in this example is as follows: Figure 3 As shown, the size of the topology domain is determined based on the geometry of SOEC. Specifically, in this example, the topology domain size is 40 mm × 40 mm, and the inlet and outlet widths are 1.5 mm (L = 40 mm, a = 1.5 mm).

[0025] 2. Determine the topology parameters: Construct a flow field topology model based on the multiphysics parameters, flow field parameters, fluid physics parameters, and boundary conditions of the solid oxide electrolytic cell; determine the multiphysics, flow field parameters, fluid physics parameters, and boundary conditions to be solved. Considering the actual operating conditions of SOEC, a multiphysics model of fluid flow, mass transport, and fluid-heat transfer coupling is required. The porosity of the aforementioned topological domain is determined using a depth-weighted method. Penetration rate diffusion coefficient Thermal conductivity The parameters are as shown in the equation: ; ; in: , Parameters representing porous ribs and porous flow channels (such as porosity, permeability, etc.); , , Indicates the initial parameters of the ribs, flow channels, and diffusion layer; , It refers to the flow field and the thickness of the cathode diffusion layer.

[0026] Fluid properties include thermal conductivity. l ,density r Isobaric heat capacity c p and viscosity m.

[0027] Flow field topology optimization boundary condition parameters include inlet fluid temperature T in ,flow m in Fluid composition, heat source calorific value S T Concentration consumed in the reaction Δc and export pressure p 0.

[0028] 3. Establish the design domain interpolation function: The solid isotropic material penalty method is used to interpolate the porous structure of the two-dimensional porous flow field topology domain to obtain the first optimization model for the flow field structure design. Specifically, a density-based topology optimization method is used to optimize the flow field structure, defining design variables. i To reflect the topological domain structure, when i =1 indicates a porous flow channel; i =0 indicates a porous ribbed plate. Under specific constraints, this allows for more efficient flow field design. Interpolation functions are used to describe the porosity, permeability, density, diffusion coefficient, thermal conductivity, and specific heat capacity of the design domain, along with the structure of the topological domain, i.e., the design variables. i The relationship between the material properties and design variables is discussed. Specifically, the solid isotropic material penalty method (SIMP) is used to interpolate the above parameters, linking the material properties with the design variables. iThe correlation allows for continuous control of various physical properties of the porous flow field topology through the design variables, thereby achieving comprehensive optimization of the flow field structure in terms of mass and heat transfer performance.

[0029]

[0030]

[0031]

[0032]

[0033] ; Among them, subscript pr Indicates the material of the porous ribbed plate, subscript pc Indicates porous flow channel material; q ε q κ q ρ , 、 、 These are the penalty factors for porosity, permeability, density, diffusion coefficient, thermal conductivity, and specific heat capacity, respectively. e pr , e pc Porosity of the porous ribbed domain and the porous flow channel domain, respectively; k pr , k pc These represent the permeability of the porous ribbed domain and the porous flow channel domain, respectively. r pr , r pc These represent the densities of the porous ribbed region and the porous flow channel region, respectively. D pr , D pc These represent the diffusion coefficients of the porous ribbed domain and the porous flow channel domain, respectively. l pr , l pc These represent the thermal conductivity of the porous ribbed region and the porous flow channel region, respectively. , These represent the specific heat capacities of the porous ribbed region and the porous flow channel region, respectively.

[0034] 4. Constructing the objective function and constraints: The objective function is constructed with the goal of improving the fluid concentration distribution and temperature distribution in the two-dimensional porous flow field topology. At the same time, pressure constraints are set to control the pressure drop at the inlet and outlet of the two-dimensional porous flow field topology, and volume fraction constraints are set to constrain the proportion of porous channels in the two-dimensional porous flow field topology, thus obtaining the second optimization model for the flow field structure design. Taking into account the effects of gas distribution, current transport, and heat conduction in the flow field, the objective function is constructed to maximize the fluid concentration and minimize the temperature change in the topological domain under constant voltage (SOEC is in an endothermic process under the given voltage, so minimizing the temperature drop, i.e., maximizing the average temperature, is the temperature field objective). F :

[0035]

[0036]

[0037]

[0038] in, This represents the average concentration of the fluid. This represents the average temperature of the topological domain; This represents the average concentration weighting coefficient; This represents the average temperature weighting factor; This is the concentration normalization constant; Ω is the temperature normalization constant; Ω is the topology optimization design domain. Water vapor concentration; T For temperature.

[0039] Considering the conductivity of the flow field, the volume fraction of the flow channel is controlled to be less than 0.6. By limiting the volume fraction of the flow channel, the conductivity and overall structural stability of the flow field structure can be improved while ensuring the gas transport capacity. Pressure constraints are also set to control the pressure drop from the inlet to the outlet.

[0040] 5. Constructing the flow field topology optimization model: The flow field topology optimization model is obtained by calculating the first optimization model and the second optimization model using multiphysics coupled control equations; based on the topology optimization method, heat transfer, mass transfer, and fluid flow control equations, the topology optimization problem is constructed as follows: Find Maximize Subject to

[0041]

[0042]

[0043]

[0044]

[0045]

[0046] in, r Density; p For pressure; p Set the pressure drop value for import and export; V This represents the volume fraction of the fluid domain. For fluid velocity; For quality source items; It is the identity matrix; The diffusion coefficient is determined by the interpolation function; This is the source term for the reaction of matter.

[0047] 6. Set up the flow field topology optimization model: Set up the flow field topology optimization model; including: performing finite element mesh generation on the topology domain; performing filtering and projection processing on the design variables, and then using the adjoint method to calculate the sensitivity and update the design variables. This includes: 601 Finite element meshing is performed on the two-dimensional porous flow field topology domain. The meshing method can be selected from mapped mesh, free quadrilateral mesh, and free triangular mesh according to actual needs. The meshing should match the structural features of the model, and the mesh independence should be verified to determine the number of meshes. 602 Design Variables i Perform a Helmholtz filter operation to obtain the filtered design variables. i f and on variables i f Perform hyperbolic tangent projection to obtain the projected design variables. i p After the filtering projection operation, the checkerboard pattern can be effectively avoided; 603 uses the adjoint method to calculate sensitivity, and employs the moving asymptote algorithm to refine the design variables after filtering and projection in step 602. i Update; 604. Based on the design variables processed above, the flow field topology optimization model constructed in step S5 is solved iteratively.

[0048] 7. Determine if the optimization has converged: Based on the results of the iterative solution of the flow field topology optimization model in step S6, determine the convergence of the optimization process. The convergence conditions are set as described in step S4, and check whether the constraint conditions are met (all constraint function values ​​are within the allowable error range). If the optimization result meets the preset convergence conditions and also meets the constraint conditions, it indicates that the current optimization process has reached a stable state, and the subsequent steps can be stopped; if the convergence conditions are not met, it is necessary to return to step S3 to update the design variables in the interpolation function, and then re-execute steps S4, S5, S6 and the convergence judgment process of this step until the optimization result meets the convergence requirements or reaches the maximum number of iterations.

[0049] 8. Obtain the optimal flow field structure: Based on the simulation results, output the topology-optimized flow field structure design, and perform post-processing on the output topology structure to remove burrs, etc. Alternatively, post-processing software can be used to reconstruct the topology structure, eliminate breakpoints, and perform smoothing operations. Finally, stretch the obtained topology plane by 1 mm along the thickness direction to obtain the optimal flow field structure under this condition.

[0050] Construct a three-dimensional multiphysics coupled simulation model of a solid oxide electrolytic cell (mesh size greater than 1 million). Simulate and analyze the electrolytic cell performance with the topological flow field configured in step 8, and compare it with an electrolytic cell model configured with parallel flow channels. Analyze the influence and causes of the topological and parallel flow fields on the electrolytic cell performance, gas distribution, heat transfer, and mass transfer. Specifically, this includes: examining the vapor concentration distribution cloud map of the cathode electrode to compare the gas distribution uniformity of the topological and parallel flow fields; analyzing the current density distribution of the electrodes to compare the average current density and current density distribution uniformity under the two flow fields; and observing the temperature distribution cloud map inside the electrolytic cell to evaluate the temperature field distribution uniformity.

[0051] To test the topology optimization structure obtained from the above steps, the following simulation example is used: Simulation parameters The flow field design for a solid oxide electrolyzer operating at 1.2 V for extended periods is described in step 1. The selection and determination of the two-dimensional porous flow field topology are as described in step 1. The topology design domain and boundary conditions are set as follows. Figure 3 As shown. The porous topology has dimensions of 40 mm × 40 mm and a flow channel inlet characteristic length of 1.5 mm, compared with an electrolytic cell configured with a parallel flow field under the same operating conditions. The heat source was set as follows during optimization. S T =-200 kW / m 3 The inlet mass flow rate is m in =2.66×10 -6 kg / s, thickness 1 mm, inlet temperature is T in=1023.15 K, outlet static pressure is p out =0 Pa, the inlet fluid is a mixture of hydrogen and water, and the inlet concentrations of hydrogen and water are respectively... , The penalty factor in the interpolation function SIMP is always 2. Taking a value of 0.1 as an example, the flow field topology optimization model is solved according to the above steps to obtain the topological flow field structure under this operating condition. The topology-optimized flow field structure is stretched, and a three-dimensional SOEC multiphysics simulation is established. The performance is then compared with that of an SOEC with a parallel flow field under the same operating conditions.

[0052] 2. Simulation Results This embodiment, based on the aforementioned flow field structure design method for solid oxide electrolyzers, constructs a flow field topology optimization model and designs the flow field structure for SOEC, resulting in the following: Figure 4 (a) The flow field shown in the right figure. The flow field structure obtained through topology optimization achieves adaptive configuration in terms of channel path and local flow resistance, resulting in more uniform gas transport within the flow channel and a more stable overall temperature field. The improvement in fluid distribution and temperature field directly enhances mass transfer efficiency and temperature distribution uniformity, thereby improving the performance and long-term operational reliability of SOEC. Figure 4 The diagrams show the structure and physical field distribution of two types of electrolytic cells operating at 1.2 V. It can be seen that, compared to a parallel flow field, the flow field structure obtained by the method of this invention exhibits an irregularly connected multi-branched channel form within the flow field plane. These channels are interconnected, forming multiple gas flow paths, enabling the reactant gas to be uniformly distributed within the electrode reaction region and effectively reducing the temperature gradient inside the electrolytic cell. Specific data comparisons are shown in Table 1.

[0053] Table 1. Performance Comparison of Topology Flow Field Optimization Design and Parallel Flow Field

[0054] As shown in Table 1, compared with the parallel flow field, the average water vapor concentration of the flow field structure obtained by the topology optimization of this invention is increased to 4.37 mol / m. 3 The concentration non-uniformity index decreased from 0.96 to 0.84, and the temperature non-uniformity index significantly decreased from 1.63 to 1.46, indicating a marked improvement in material distribution and temperature field uniformity. This is more conducive to the safe and efficient operation of the electrolytic cell, ultimately increasing the average current density of SOEC from 5288.9 A / m³. 2 Increased to 5638.6 A / m 2 This represents an increase of approximately 6.61%. Note: The unevenness index is defined as follows: Where x iThis refers to the temperature or water concentration of the catalyst layer in the three-dimensional model. Its average temperature or average water concentration, V The electrode volume is given.

[0055] Although the present invention has been described above, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many modifications under the guidance of the present invention without departing from the spirit of the present invention, and these modifications are all within the protection scope of the present invention.

Claims

1. A flow field structure design method for solid oxide electrolytic cells, characterized in that: The method includes the following steps: S1. A two-dimensional porous flow field topology is established based on the geometry, inlet and outlet arrangement and operating characteristics of the solid oxide electrolytic cell; S2. Determine the topological domain parameters of the two-dimensional porous flow field based on the multiphysics parameters, flow field parameters, fluid physics parameters and boundary conditions of the solid oxide electrolytic cell; S3. Introduce design variables to characterize the flow field structure distribution within the two-dimensional porous flow field topology domain, and describe the relationship between the design variables and the parameters of the two-dimensional porous flow field topology domain through an interpolation function to obtain the first optimization model for the flow field structure design. S4. Construct the objective function by the fluid concentration distribution and temperature distribution in the two-dimensional porous flow field topology domain. At the same time, set pressure constraints to control the pressure drop at the inlet and outlet of the two-dimensional porous flow field topology domain, and set volume fraction constraints to constrain the proportion of porous channels in the two-dimensional porous flow field topology domain, to obtain the second optimization model for flow field structure design. S5. The flow field topology optimization model is obtained by calculating the first optimization model and the second optimization model using multiphysics field coupled control equations; S6. Grid the topological domain of the two-dimensional porous flow field; The design variables are filtered and projected, and the design variables are updated. The flow field topology optimization model is then solved iteratively. S7. Determine whether the iterative solution result of the flow field topology optimization model has converged: if the convergence result is met, proceed to the next step; otherwise, return to step S3 to update the design variables of the interpolation function and repeat the iterative process from step S4 to S6. S8. After the above flow field topology optimization model converges, the optimal flow field structure is obtained. The optimal flow field structure is then processed and stretched to obtain the three-dimensional flow field structure. A multiphysics coupling simulation model of a three-dimensional solid oxide electrolytic cell was established to analyze the performance of the three-dimensional flow field structure.

2. The flow field structure design method for solid oxide electrolytic cells according to claim 1, characterized in that: The two-dimensional porous flow field topology includes porous ribs and porous channels, wherein: the porous ribs are composed of ribs and a diffusion layer; the porous channels are composed of channels and a diffusion layer, wherein: The parameters of the porous rib plate are: ; The parameters of the porous flow channel are: ; in: Indicates rib parameters, Indicates flow channel parameters, Indicates the parameters of the diffusion layer; Indicates the thickness of the flow field, This indicates the thickness of the cathode diffusion layer.

3. The flow field structure design method for solid oxide electrolytic cells according to claim 1, characterized in that: In step S3, the solid isotropic material penalty method is used to describe the relationship between the design variables and the topological parameters of the two-dimensional porous flow field, obtaining the first optimization model for the flow field structure design, including: S301. Optimize the flow field structure using a density-based topology optimization method, defining design variables. θ By varying the values ​​of the design variables, the spatial distribution of the porous flow channel region and the porous rib region within the two-dimensional porous flow field topology can be controlled; that is: when θ =1 indicates a porous flow channel; θ =0 indicates a porous rib; S302. The porosity, permeability, density, diffusion coefficient, thermal conductivity, and specific heat capacity of the two-dimensional porous flow field topology are described using the solid isotropic material penalty method, in relation to the design variables. θ The relationship allows for continuous control of various physical properties of the porous flow field topology through the design variables, thereby achieving comprehensive optimization of the flow field structure in terms of mass and heat transfer performance, i.e.: ； ； ； ； ； ； In this context, the subscript pr indicates porous rib material, and the subscript pc indicates porous flow channel material. q ε , q κ , q ρ , , , These are the penalty factors for porosity, permeability, density, diffusion coefficient, thermal conductivity, and specific heat capacity, respectively. ε pr , ε pc Porosity of the porous ribbed domain and the porous flow channel domain, respectively; κ pr , κ pc These represent the permeability of the porous ribbed domain and the porous flow channel domain, respectively. ρ pr , ρ pc These represent the densities of the porous ribbed region and the porous flow channel region, respectively. D pr , D pc These represent the diffusion coefficients of the porous ribbed domain and the porous flow channel domain, respectively. λ pr , λ pc These represent the thermal conductivity of the porous ribbed region and the porous flow channel region, respectively. , These represent the specific heat capacities of the porous ribbed region and the porous flow channel region, respectively.

4. The flow field structure design method for solid oxide electrolytic cells according to claim 1, characterized in that: In step S4, an objective function for adjusting the fluid concentration distribution and temperature distribution in the two-dimensional porous flow field topology is constructed according to the following formula. Ф ,Right now: ； ； ； ； in, This represents the average concentration of the fluid. This represents the average temperature of the topological domain; This represents the average concentration weighting coefficient; This represents the average temperature weighting factor; This is the concentration normalization constant; Ω is the temperature normalization constant; Ω is the topology optimization design domain. Water vapor concentration; T For temperature.

5. The flow field structure design method for solid oxide electrolytic cells according to claim 1, characterized in that: The flow field topology optimization model mentioned in step S5 is as follows: Find Maximize Subject to in, ρ Density; p For pressure; p Set the pressure drop value for import and export; V This represents the volume fraction of the fluid domain. For fluid velocity; For quality source items; It is the identity matrix; The diffusion coefficient is determined by the interpolation function; This is the source term for the reaction of matter.

6. The flow field structure design method for a solid oxide electrolytic cell according to claim 1, characterized in that: Step S6 involves dividing the two-dimensional porous flow field topology domain into a mesh, updating the design variables, and iteratively solving the flow field topology optimization model; including: S601. Perform finite element mesh generation on the two-dimensional porous flow field topology domain. The meshing method is selected from mapped mesh, free quadrilateral mesh, and free triangular mesh according to the shape of the topology domain. The mesh generation should match the structural features of the model. Mesh independence verification is performed to determine the number of meshes. S602. Design Variables θ Perform a Helmholtz filtering operation to obtain the first topological variable after filtering. θ f and for the first topological variable θ f Perform hyperbolic tangent projection to obtain the projected topological variables. θ p Filtered projections prevent checkerboard patterns from appearing in design variables; S603. Sensitivity calculation is performed using the adjoint method, and the filtered projected design variables are adjusted using the moving asymptote algorithm. θ Update.

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