A grid optimization method, system, device, medium and product for SOC stack multi-physics field coupling simulation
By dividing the SOC stack into functional regions and optimizing the differentiated mesh, the problem of long calculation time in multiphysics coupling simulation of SOC stacks is solved, and the calculation efficiency and accuracy are improved. It is applicable to the simulation analysis of various SOC stacks.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- UNIV OF SCI & TECH OF CHINA
- Filing Date
- 2026-05-22
- Publication Date
- 2026-06-19
AI Technical Summary
In existing technologies, multiphysics coupling simulation of SOC stacks is time-consuming and inefficient, making it difficult to meet the computational accuracy requirements of different functional areas. Mesh optimization is not targeted enough, resulting in a significant increase in computational resource consumption and limiting the practical usability of the model in parameter scanning, structural optimization, and engineering applications.
By constructing a three-dimensional geometric model of the SOC stack, it is divided into multiple functional regions. The computational sensitivity level is determined based on key physical quantities, and differentiated mesh partitioning methods and parameters are set in different regions to optimize the mesh configuration and form a non-uniformly distributed mesh structure. The optimal mesh configuration is verified by combining multiphysics field coupling simulation results.
While ensuring simulation accuracy, it significantly reduces the computational scale, improves computational efficiency and simulation stability, enhances engineering applicability, and is suitable for SOC stack simulation analysis under different structural forms and operating modes.
Smart Images
Figure CN122242076A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of numerical simulation and calculation of SOC stacks, and in particular to a mesh optimization method, system, device, medium and product for multiphysics coupling simulation of SOC stacks. Background Technology
[0002] Solid oxide cell (SOC) stacks include solid oxide electrolyzer cells (SOEC) and solid oxide fuel cell (SOFC) stacks. During operation, they involve multiple coupled physical processes such as electrochemical reactions, gas diffusion and convection, current conduction, and heat transfer. In order to deeply reveal the internal physical behavior of SOC stacks and their impact on performance and reliability, multiphysics coupled numerical simulation has become an important research tool in stack design, operation optimization, and life assessment.
[0003] However, due to the tight coupling between physical fields and the complex structure of the fuel cell stack, the simulation process for SOC fuel cell stack models involving multi-field coupling is relatively time-consuming. Currently, simulations of SOC fuel cell stacks are generally time-consuming and computationally inefficient. To improve computational efficiency, the most direct method is mesh optimization.
[0004] In existing technologies, multiphysics coupled simulation of SOC (Solar Charge) fuel cells generally employs a uniform or approximately uniform mesh generation strategy to ensure the stability of the numerical solution process and simplify model setup. However, this type of method struggles to simultaneously meet the computational accuracy requirements of different functional regions, often necessitating a global increase in mesh density. This leads to a dramatic increase in mesh size, significantly increasing computation time and resource consumption, thus limiting the practical usability of the model in parameter scanning, structural optimization, and engineering applications.
[0005] To address the aforementioned issues, some existing studies have attempted to introduce adaptive meshing techniques to locally refine the computational domain. However, these methods typically adjust based on general error estimation criteria or single physics variables, making it difficult to fully reflect the multi-physics coupling relationships within the SOC stack and the differentiated computational accuracy requirements of different functional regions. Consequently, they still suffer from insufficient targeted mesh optimization and limited improvement in computational efficiency. Therefore, a mesh optimization method tailored to the structural characteristics and multi-physics coupling properties of the SOC stack is urgently needed to improve computational efficiency and simulation stability while ensuring simulation accuracy. Summary of the Invention
[0006] The purpose of this application is to provide a mesh optimization method, system, device, medium, and product for multiphysics coupling simulation of SOC stacks. By combining the structural characteristics and physical quantity variation laws of different functional regions of the SOC stack, the simulation mesh is optimized and configured in a targeted manner, effectively reducing the computational scale while ensuring computational accuracy, thereby improving the computational efficiency and engineering applicability of multiphysics coupling simulation.
[0007] To achieve the above objectives, this application provides the following solution: Firstly, this application provides a mesh optimization method for multiphysics coupled simulation of SOC (Solar Charge) fuel cells, including: Construct a three-dimensional geometric model of the SOC fuel cell stack; Based on the structural composition and operating mechanism of the SOC stack, the three-dimensional geometric model is divided into multiple functional regions; the functional regions include electrochemical reaction active regions, gas flow regions, and current collection structure regions; Based on the key physical quantities involved in multiphysics coupling calculations within each functional region, the computational sensitivity level of each functional region to grid resolution is determined. Based on the calculated sensitivity level, corresponding grid division methods and grid parameters are set in different functional areas to form a non-uniformly distributed differentiated grid; Based on the differentiated grid, the grid parameters of the SOC stack are searched and optimized to determine candidate grid configurations; Multiphysics coupling simulation is performed on the candidate mesh configurations, and the optimal mesh configuration is determined based on the changes in several key physical quantities.
[0008] Secondly, this application provides a mesh optimization system for multiphysics coupled simulation of SOC (Solar Charge) fuel cells, comprising: The model building module is used to build a three-dimensional geometric model of the SOC stack; The functional area division module is used to divide the three-dimensional geometric model into multiple functional areas according to the structural composition and operating mechanism of the SOC stack; the functional areas include electrochemical reaction active areas, gas flow areas, and current collection structure areas; The sensitivity level determination module is used to determine the computational sensitivity level of each functional region to the grid resolution based on the key physical quantities involved in multiphysics coupling calculations within each functional region. The differentiated grid forming module is used to set the corresponding grid division method and grid parameters in different functional areas according to the computational sensitivity level, so as to form a non-uniformly distributed differentiated grid. The candidate grid configuration determination module is used to search and optimize the grid parameters of the SOC stack based on the differentiated grid to determine the candidate grid configuration; The optimal mesh configuration determination module is used to perform multiphysics coupling simulation on the candidate mesh configurations and determine the optimal mesh configuration based on the changes of multiple key physical quantities.
[0009] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described mesh optimization method for multiphysics coupling simulation of SOC stacks.
[0010] Fourthly, this application provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described mesh optimization method for multiphysics coupling simulation of SOC stacks.
[0011] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described mesh optimization method for multiphysics coupling simulation of SOC stacks.
[0012] According to the specific embodiments provided in this application, this application has the following technical effects: 1. This application divides the SOC stack into multiple functional regions and configures different meshing methods and mesh parameters that match the physical characteristics of each functional region based on the key physical quantities in each region. This makes the meshing more in line with the actual needs of multiphysics coupling calculation. Under the premise of ensuring the accuracy of multiphysics coupling simulation, it can effectively avoid the problem of excessive global mesh refinement caused by drastic changes in local physical quantities, thereby improving the pertinence and rationality of simulation modeling.
[0013] 2. Based on differentiated grid settings, this application searches and optimizes the grid parameters of the SOC stack, and verifies the candidate grid configurations by combining multiphysics coupling simulation results to determine the optimal grid configuration, thereby forming a non-uniformly distributed optimized grid structure. This grid structure can significantly reduce the overall grid size while meeting the requirements of simulation accuracy and numerical stability, reduce the computation time and computational resource consumption of multiphysics coupling simulation, improve the efficiency of numerical simulation, and significantly enhance the practical usability of complex SOC stack models in engineering parameter analysis, structural optimization, and multi-condition calculations.
[0014] 3. This application introduces multiple key physical quantities as evaluation indicators to avoid misjudgments that may be caused by relying on a single physical quantity for grid determination, thereby improving the reliability and robustness of the optimal grid configuration determination process. Attached Figure Description
[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0016] Figure 1 This is a flowchart illustrating a mesh optimization method for multiphysics coupling simulation of a SOC fuel cell stack according to an embodiment of this application. Figure 2 This is a schematic diagram of a three-dimensional geometric model of a planar SOC stack. Figure 3 A schematic diagram of the mesh configuration for a single-layer flat-plate SOC stack model; Figure 4 This is a schematic diagram of the grid independence test results of a flat-plate SOEC single-layer fuel cell stack model; where (a) shows the simulation results of fuel homogeneity under different grid configurations at 25% and 75% steam utilization rates, (b) shows the simulation results of stack voltage under different grid configurations at 25% and 75% steam utilization rates, and (c) shows the absolute differences between the maximum and minimum temperatures of the anode-electrolyte-cathode (PEN) region and the baseline results (360,000 grids) under different grid configurations at 25% and 75% steam utilization rates. Figure 5 This is a schematic diagram of the grid independence test results of a planar SOFC single-layer stack model; where (a) is the simulation results of fuel uniformity under different grid configurations at 50% hydrogen utilization, (b) is the simulation results of stack voltage under different grid configurations at 50% hydrogen utilization, and (c) is the absolute difference between the maximum and minimum temperatures of the anode-electrolyte-cathode (PEN) region under different grid configurations and the baseline results (550,000 grids) at 50% hydrogen utilization. Figure 6 The diagram shows a comparison of the distribution of key physical quantities along the centerline of the solar cell in a planar SOEC single-layer stack model using two different grid configurations; where (a) is the water vapor molar concentration distribution, (b) is the current density distribution, and (c) is the temperature distribution. Detailed Implementation
[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0018] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0019] In one exemplary embodiment, such as Figure 1 As shown, a mesh optimization method for multiphysics coupling simulation of SOC electric stack is provided. This method is executed by a computer device, specifically by a computer device such as a terminal or server alone, or by a terminal and a server together. In this embodiment, the method is described using a server as an example, including the following steps S1 to S6.
[0020] S1: Construct a three-dimensional geometric model of the SOC stack, which includes the battery cells, connectors, sealing layers, frame, and gas flow channel structure.
[0021] S2: Based on the structural composition and operating mechanism of the SOC stack, the three-dimensional geometric model is divided into multiple functional regions; the functional regions include electrochemical reaction active regions, gas flow regions, and current collection structure regions.
[0022] S3: Based on the key physical quantities involved in multiphysics coupling calculations within each functional region, determine the computational sensitivity level of each functional region to grid resolution.
[0023] S4: Based on the calculated sensitivity level, set the corresponding grid division method and grid parameters in different functional areas to form a non-uniformly distributed differentiated grid.
[0024] S5: Based on the differentiated grid, search and optimize the grid parameters of the SOC stack to determine the candidate grid configuration.
[0025] S6: Perform multiphysics coupling simulation on the candidate mesh configurations, and determine the optimal mesh configuration based on the changes in multiple key physical quantities.
[0026] By implementing steps S1 to S6 above, this application optimizes the simulation mesh by combining the structural characteristics and physical quantity variation laws of different functional regions of the SOC stack. This effectively reduces the computational scale while ensuring computational accuracy, thereby improving the computational efficiency and engineering applicability of multiphysics coupled simulation. This method can adapt to SOC stack simulation models under different structural forms, operating modes, and operating conditions (including steady-state and dynamic conditions), exhibiting good engineering versatility and scalability. It is suitable for multiphysics coupled simulation analysis of solid oxide fuel cells, solid oxide electrolytic cells, and their reversible stacks.
[0027] In a specific embodiment, step S1 specifically includes: In this embodiment, a typical flat-plate SOC stack is used as an example for illustration. In order to improve working efficiency, due to the periodicity of the stack structure, it is only necessary to perform meshing and verify the rationality of the single-layer stack structure. After determining the mesh configuration parameters, it can be directly applied to the multi-layer stack.
[0028] First, a three-dimensional geometric model of the planar SOC stack is constructed based on its structural composition, such as... Figure 2 As shown. Figure 2 A typical fuel cell stack unit is shown, comprising solar cells 1, connectors 2, sealing layers, a frame 3, and gas channels. It employs a stacked structure with each functional layer compactly stacked. Solar cells 1, consisting of an anode, electrolyte, and cathode, are the reaction zone within the stack. Connectors 2, located between solar cells 1, serve as gas channels and current collection. The sealing layers are further subdivided into an air electrode sealing layer 4, a solar cell sealing layer 5, and a fuel electrode sealing layer 6, preventing gas leakage and preventing "cross-flow" of fuel gas and air within the stack. The frame 3 typically supports the thin solar cells, enhancing their mechanical strength. The gas channels include an air inlet 7, an air outlet 8, a fuel inlet 9, and a fuel outlet 10.
[0029] In one specific embodiment, step S2 specifically includes: 1) Electrochemical reaction active region: that is, the region of the battery cell (anode-electrolyte-cathode, PEN). Due to the coupling of multiple physical fields, the current density, reaction rate and temperature change significantly. 2) Gas flow channel region: mainly involves the coupling of flow field and temperature field, and the changes in physical quantities are relatively gradual; 3) Current collection structure region: Located in the frame and sealing layer region of the connector and its vicinity, heat transfer is dominant and the spatial gradient of physical quantities is relatively small.
[0030] By dividing the computational regions into functional areas, computational regions dominated by different physical mechanisms can be spatially distinguished, providing a basis for subsequent differentiated grid settings.
[0031] In a specific embodiment, step S3 specifically includes: obtaining key physical quantities involved in multiphysics coupling calculations within each functional region; analyzing the spatial distribution characteristics and gradient change characteristics of the key physical quantities within the corresponding functional regions; determining the computational sensitivity level of each functional region to the grid resolution based on the spatial distribution characteristics and the gradient change characteristics; the computational sensitivity level includes high-sensitivity regions, medium-sensitivity regions, and low-sensitivity regions.
[0032] Specifically, key physical quantities may include one or more of the following: temperature, current density, reactant or product gas concentration, and potential distribution. By analyzing the spatial distribution characteristics and gradient variation properties of these key physical quantities in different functional regions, the computational sensitivity of each functional region to grid resolution is evaluated. First, under a pre-defined baseline grid, the steady-state field distribution inside the SOC stack is obtained through multiphysics coupling simulation. For key physical quantities, the absolute values of their scalar gradients in three-dimensional space are calculated. In the electrochemically reactive region, the gradients of component consumption rate and current density are analyzed; in the gas flow channel region, the variation characteristics of pressure drop and flow velocity along the flow path are analyzed; and in the current collector structure region, the temperature change rate is analyzed.
[0033] Based on the distribution characteristics and gradient changes of key physical quantities, each functional region is divided into different computational sensitivity levels: 1) High-sensitivity region (electrochemically active region): In PEN, due to the intense coupling of charge transfer and component diffusion involved in electrochemical reactions, the physical quantity gradient is extremely large. This region is extremely sensitive to changes in mesh size; even small mesh adjustments can cause temperature deviations of several Kelvin or significant voltage calculation fluctuations; 2) Medium-sensitivity region (gas flow region): Inside the gas flow channels, although heat exchange and mass transfer exist, the changes in physical quantities are relatively regular, and the gradient value is at an intermediate level; 3) Low-sensitivity region (current collection structure region): Inside current collection structures such as connectors and borders, since the process is mainly controlled by linear conduction, the spatial gradient of physical quantities (such as potential distribution and solid thermal conductivity) is small, and the calculation results show a low dependence on changes in mesh density.
[0034] In a specific embodiment, step S4 specifically includes: for the highly sensitive region, setting a grid parameter smaller than the preset grid parameter; for the low sensitive region, setting a grid parameter larger than the preset grid parameter; for the medium sensitive region, adopting a grid division method with different scales along the flow direction and perpendicular to the flow direction according to the flow direction and gradient change characteristics.
[0035] Based on the analysis results of the computational sensitivity of different functional regions, mesh generation methods and mesh parameters that match the variation characteristics of physical quantities are set for each functional region: 1) In the electrochemically active region, a relatively small grid size is used to improve the ability to resolve drastic changes in local physical quantities; 2) In the gas flow channel region, based on the flow direction and the characteristics of physical quantity changes, different grid division methods with different scales are adopted along the flow direction and perpendicular to the flow direction; 3) In the flow collection structure region, a relatively large grid size is used to reduce redundant computing units.
[0036] The above methods are used to form a differentiated grid structure that exhibits a non-uniform distribution as a whole.
[0037] In a specific embodiment, step S5 specifically includes: selecting a set of mesh configurations that have passed rationality verification and ensure the accuracy of multiphysics coupling simulation as a reference mesh configuration; based on the differentiated mesh, gradually reducing the corresponding mesh parameters for the target structural components or target spatial directions of the SOC stack to generate multiple initial candidate mesh configurations; performing multiphysics coupling simulation on the initial candidate mesh configurations, and comparing the simulation results with the simulation results of the reference mesh configuration to determine the candidate mesh configuration.
[0038] Specifically, after completing the differentiated mesh settings, in order to further reduce the overall mesh size while ensuring the accuracy of multiphysics coupling simulation results, this embodiment takes SOEC single-layer stack as the object and searches and optimizes the mesh parameter configuration of SOC stack.
[0039] In this embodiment, based on the structural characteristics and simulation experience of the SOEC stack, multiple candidate mesh configurations are set for the mesh parameters of different structural components in both directions parallel and perpendicular to the battery plane. Specifically, the mesh parameters parallel to the battery plane include at least the number of meshes along the length of the air passage, the width of the air passage, the width of the ribs, and the width of the inlet / outlet air passages of the shunt / collector head; the mesh parameters perpendicular to the battery plane include at least the number of meshes along the height of the fuel electrode, electrolyte, air electrode, air passages, and connector.
[0040] During the mesh parameter search process, a set of large-scale mesh configurations that have passed rationality verification and can guarantee the accuracy of multiphysics coupling simulations are first selected as the benchmark mesh configuration, and the simulation results corresponding to this benchmark mesh configuration are used as reference results. Subsequently, while keeping the mesh parameters of other components and directions sufficient, the mesh generation parameters of a specific structural component or spatial direction are gradually reduced, and the multiphysics coupling simulation results obtained before and after the reduction are compared and analyzed.
[0041] Taking a specific single-layer SOEC fuel cell stack as an example, its detailed geometric parameters are shown in Table 1, and Table 2 lists the main mesh configurations tested. First, a sufficiently large mesh configuration for the SOEC stack was determined to be 360,000 meshes. In this embodiment, this mesh configuration, referred to as Mesh5, is used for multi-field simulation of the single-layer stack. It should be noted that the actual mesh configurations tested far exceed the five listed in Table 2; some configurations are omitted here for clarity.
[0042] Table 1
[0043] Table 2
[0044] When the differences in the simulation results in terms of key physical quantity distribution, overall performance indicators, or field variable change trends are within a preset acceptable range, the grid parameters of the structural component or spatial direction are determined to be sufficient; if the differences exceed the acceptable range, the original grid parameter settings are maintained.
[0045] By using the above-mentioned method of progressively reducing and verifying mesh parameters, we can effectively determine the candidate mesh configuration that meets the accuracy requirements of multiphysics coupling simulation without exhaustively testing all possible mesh combinations. This significantly reduces the overall mesh size of the flat-plate SOC stack simulation model and improves the simulation computation efficiency.
[0046] In a specific embodiment, step S6 specifically includes: performing multiphysics coupling simulation on the candidate mesh configuration under at least two different operating states, and comparing the changes of multiple key physical quantities under different candidate mesh configurations; when the changes of multiple key physical quantities are all within a preset range under adjacent candidate mesh configurations, the corresponding candidate mesh configuration is determined to be the optimal mesh configuration.
[0047] Specifically, after completing the search and optimization of mesh parameters, in order to further verify the rationality and reliability of candidate mesh configurations in multiphysics coupling simulation, this embodiment performs a mesh independence verification process on the candidate mesh configurations and determines the optimal mesh configuration accordingly.
[0048] In this embodiment, taking a SOEC single-layer fuel cell stack as an example, multiphysics coupled simulation calculations are performed on multiple candidate mesh configurations under different operating conditions. The operating conditions include at least endothermic and exothermic operating conditions to cover different energy transfer characteristics that the fuel cell stack may exhibit in practical applications. By comparing and analyzing the simulation results under different operating conditions, deviations in verification results caused by a single operating condition are avoided.
[0049] In the process of verifying mesh independence, several key physical quantities that can characterize the coupling properties of multiphysics are selected as evaluation indicators. By comparing the changes of several key physical quantities under different candidate mesh configurations, the sensitivity of simulation results to changes in mesh size is evaluated.
[0050] During the comparative analysis, it was found that some key physical quantities remained relatively stable even when the grid size changed significantly, making them difficult to use as a sole basis for judging grid independence. Therefore, this embodiment further introduces a joint examination of multiple temperature extremes within the electrochemical reaction active region, namely the highest and lowest temperatures. By simultaneously analyzing the changes in different extreme physical quantities under different grid configurations, misjudgments that may arise from relying solely on a single physical quantity are avoided.
[0051] When the differences between multiple key physical quantities under adjacent candidate mesh configurations are all within a preset acceptable range, the corresponding candidate mesh configuration is determined to meet the mesh independence requirement of multiphysics coupled simulation, and this candidate mesh configuration is identified as the optimal mesh configuration. Through this method, while ensuring simulation accuracy and result reliability, the overall mesh size of the simulation model can be effectively reduced, and the computational efficiency of multiphysics coupled simulation can be improved.
[0052] Let's take the single-layer SOEC fuel cell stack as an example for illustration. Figure 4 The results of mesh independence verification are presented. Simulations of multiphysics distribution under endothermic (steam utilization FU=25%) and exothermic (steam utilization FU=75%) conditions are also presented. Similar to the definition of fuel homogeneity in multilayer stacks, for single-layer stacks, fuel homogeneity is defined as the ratio of the minimum fuel mass flow rate through all gas channels to the average fuel mass flow rate through each gas channel. Figure 4 (a) and (b) in the figure show that, despite the significant change in the number of grids, the changes in fuel homogeneity and average voltage, which represent the component transport field and the electrochemical field, are not significant, with only a difference of about 0.1%, which is obviously not enough to determine the optimal grid setting.
[0053] To better assess grid independence, this implementation selects the minimum temperature (T0) of the cell's PEN structure (active region). min ) and maximum temperature (T) max ( ) as the physical quantity that needs to be detected. For example... Figure 4 As shown in (c), based on the results of Mesh5, the T of Mesh1 (110,000 grids) is... max and T min The absolute error ΔT max and ΔT min It could reach several Kelvin. However, for Mesh2 (130,000 grids), ΔT max and ΔT min Reduced to approximately 1K or less. Additionally, ΔT max and ΔT min The change in the number of grid cells did not exhibit a monotonic variation but rather fluctuated, which differs from the typical grid independence verification where the physical quantity to be verified gradually approaches a certain accurate value as the number of grid cells increases. This also reflects, to some extent, the highly nonlinear and complex nature of SOC multi-field coupled simulations. Fortunately, ΔT max and ΔT min For Mesh3 and Mesh4, which contain 180,000 and 230,000 meshes respectively, the temperature deviation is approximately 1K or less. Since a temperature deviation of 1K or less is quite satisfactory for practical applications of SOEC fuel cells, Mesh2 was chosen as the optimal mesh configuration. Mesh configuration details are as follows... Figure 3 As shown.
[0054] The same mesh optimization steps were applied to SOFC stacks, consistent with the case of SOEC stacks. Although the mesh number changed significantly, the changes in fuel homogeneity and average voltage, representing the component transport field and electrochemical field, were not significant, with only about 0.1% difference. Figure 5 As shown in (a) and (b) in the figure. Then, the minimum temperature (T) in the PEN region of the SOFC stack was evaluated. min ) and maximum temperature (T) max The optimal grid configuration was selected, which is consistent with the optimal grid configuration of the SOEC stack. The number of grids is about 130,000, which is a significant optimization compared to the initial 360,000 / 550,000 grids, and can greatly improve the computational efficiency.
[0055] It is worth noting that relying solely on T max or T min As a measure of grid independence, it may be insufficient. For example... Figure 4 (c) and Figure 5 As shown in (c), T max and T min The magnitude relationship between them is uncertain. If only one is relied upon, even if its error is proven not to affect the result, the error of the other may exceed an acceptable range, leading to misleading conclusions. In fact, in mesh independence tests, people often intuitively choose the maximum temperature in the exothermic state or the minimum temperature in the endothermic state as the criterion. However, as... Figure 4 As demonstrated in (c), these two quantities are precisely insensitive to changes in the mesh, potentially providing false verification results. In some papers' mesh independence tests, only one temperature quantity is considered, which is insufficient to determine the optimal mesh. Therefore, in the SOC mesh independence test, both the maximum and minimum temperatures of the PEN structure need to be considered.
[0056] To further verify the accuracy of the model using the Mesh2 mesh configuration in multi-field coupled simulations, this embodiment compares the distribution of key physical quantities of a single-layer SOEC stack using Mesh2 and Mesh5 mesh configurations, respectively. The relevant results are as follows: Figure 6 As shown in (a)-(c). Figure 6 The fuel distribution, current density distribution, and temperature distribution in the active region of the fuel cell stack were compared. It can be seen that the model results using the Mesh2 grid configuration are basically consistent with those using the baseline grid configuration, Mesh5. This strongly demonstrates the efficiency of the Mesh2 grid configuration, significantly reducing computational resources without sacrificing model accuracy.
[0057] Based on the same inventive concept, this application also provides a system for implementing the mesh optimization method for multiphysics coupled simulation of SOC fuel cells as described above. The solution provided by this system is similar to the implementation scheme described in the above method. Therefore, the specific limitations of one or more mesh optimization system embodiments for multiphysics coupled simulation of SOC fuel cells provided below can be found in the limitations of the mesh optimization method for multiphysics coupled simulation of SOC fuel cells described above, and will not be repeated here.
[0058] In one exemplary embodiment, a mesh optimization system for multiphysics coupling simulation of SOC (Solar Charge) stacks is provided, comprising the following modules.
[0059] The model building module is used to build a three-dimensional geometric model of the SOC stack. The three-dimensional geometric model includes the battery cells, connectors, sealing layers, frame, and gas flow channel structure.
[0060] The functional area division module is used to divide the three-dimensional geometric model into multiple functional areas according to the structural composition and operating mechanism of the SOC stack; the functional areas include electrochemical reaction active areas, gas flow areas, and current collection structure areas.
[0061] The sensitivity level determination module is used to determine the computational sensitivity level of each functional region to the grid resolution based on the key physical quantities involved in multiphysics coupling calculations within each functional region.
[0062] The configuration module is used to set the corresponding grid division method and grid parameters in different functional areas according to the computational sensitivity level.
[0063] The differentiated grid division module is used to divide each functional area into a non-uniformly distributed differentiated grid based on the grid division method and the grid parameters.
[0064] The candidate grid configuration determination module is used to search and optimize the grid parameters of the SOC stack based on the differentiated grid to determine the candidate grid configuration.
[0065] The optimal mesh configuration determination module is used to perform multiphysics coupling simulation on the candidate mesh configurations and determine the optimal mesh configuration based on the changes of multiple key physical quantities.
[0066] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments. The computer device may be a server or a terminal. The computer device includes a processor, a memory, an input / output interface (I / O), and a communication interface. The processor, memory, and I / O are connected via a system bus, and the communication interface is connected to the system bus via the I / O interface. The processor of the computer device provides computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device stores data to be processed. The I / O interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with an external terminal via a network connection. When the computer program is executed by the processor, it implements the steps in the above-described method embodiments.
[0067] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0068] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.
[0069] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.
[0070] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).
[0071] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0072] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.
[0073] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A mesh optimization method for multiphysics coupled simulation of SOC (System-on-a-Chip) fuel cell stacks, characterized in that, include: Construct a three-dimensional geometric model of the SOC fuel cell stack; Based on the structural composition and operating mechanism of the SOC stack, the three-dimensional geometric model is divided into multiple functional regions; The functional regions include electrochemical reaction active regions, gas flow regions, and current collection structure regions. Based on the key physical quantities involved in multiphysics coupling calculations within each functional region, the computational sensitivity level of each functional region to grid resolution is determined. Based on the calculated sensitivity level, corresponding grid division methods and grid parameters are set in different functional areas to form a non-uniformly distributed differentiated grid; Based on the differentiated grid, the grid parameters of the SOC stack are searched and optimized to determine candidate grid configurations; Multiphysics coupling simulation is performed on the candidate mesh configurations, and the optimal mesh configuration is determined based on the changes in several key physical quantities.
2. The mesh optimization method for multiphysics coupled simulation of SOC fuel cell stacks according to claim 1, characterized in that, Based on the key physical quantities involved in multiphysics coupling calculations within each functional region, the computational sensitivity level of each functional region to grid resolution is determined, specifically including: Obtain the key physical quantities involved in multiphysics coupling calculations within each functional region; Analyze the spatial distribution characteristics and gradient change properties of the key physical quantities within the corresponding functional areas; The computational sensitivity level of each functional region to grid resolution is determined based on the spatial distribution characteristics and gradient change characteristics; the computational sensitivity level includes high-sensitivity regions, medium-sensitivity regions, and low-sensitivity regions.
3. The mesh optimization method for multiphysics coupled simulation of SOC fuel cell stacks according to claim 1, characterized in that, The key physical quantities are one or more of temperature, current density, reactant concentration, product gas concentration, and potential distribution.
4. The mesh optimization method for multiphysics coupling simulation of SOC fuel cell stacks according to claim 2, characterized in that, Based on the calculated sensitivity level, corresponding meshing methods and mesh parameters are set for different functional areas, specifically including: For highly sensitive areas, set the mesh parameters to be smaller than the preset mesh parameters; For low-sensitivity areas, set the mesh parameters to be greater than the preset mesh parameters; For the medium-sensitive region, a grid division method with different scales along the flow direction and perpendicular to the flow direction is adopted according to the flow direction and gradient change characteristics.
5. The mesh optimization method for multiphysics coupled simulation of SOC fuel cell stacks according to claim 1, characterized in that, Based on the differentiated grid, the grid parameters of the SOC stack are searched and optimized to determine candidate grid configurations, specifically including: A set of mesh configurations that have passed the rationality verification and ensure the accuracy of multiphysics coupling simulation is selected as the benchmark mesh configuration; Based on the differentiated grid, the corresponding grid parameters are gradually reduced for the target structural components or target spatial direction of the SOC stack to generate multiple initial candidate grid configurations; Multiphysics coupling simulation is performed on the initial candidate mesh configuration, and the simulation results are compared with the simulation results of the baseline mesh configuration to determine the candidate mesh configuration.
6. The mesh optimization method for multiphysics coupled simulation of SOC fuel cell stacks according to claim 1, characterized in that, Multiphysics coupled simulations are performed on the candidate mesh configurations, and the optimal mesh configuration is determined based on the changes in several key physical quantities, specifically including: Multiphysics coupling simulations are performed on the candidate mesh configurations under at least two different operating states, and the changes of multiple key physical quantities under different candidate mesh configurations are compared. When the changes of multiple key physical quantities are all within a preset range under adjacent candidate grid configurations, the corresponding candidate grid configuration is determined to be the optimal grid configuration.
7. A mesh optimization system for multiphysics coupled simulation of SOC fuel cell stacks, characterized in that, include: The model building module is used to build a three-dimensional geometric model of the SOC stack; The functional area division module is used to divide the three-dimensional geometric model into multiple functional areas according to the structural composition and operating mechanism of the SOC stack; the functional areas include electrochemical reaction active areas, gas flow areas, and current collection structure areas; The sensitivity level determination module is used to determine the computational sensitivity level of each functional region to the grid resolution based on the key physical quantities involved in multiphysics coupling calculations within each functional region. The differentiated grid forming module is used to set the corresponding grid division method and grid parameters in different functional areas according to the computational sensitivity level, so as to form a non-uniformly distributed differentiated grid. The candidate grid configuration determination module is used to search and optimize the grid parameters of the SOC stack based on the differentiated grid to determine the candidate grid configuration; The optimal mesh configuration determination module is used to perform multiphysics coupling simulation on the candidate mesh configurations and determine the optimal mesh configuration based on the changes of multiple key physical quantities.
8. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the mesh optimization method for multiphysics coupling simulation of SOC stacks according to any one of claims 1-6.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the mesh optimization method for multiphysics coupling simulation of SOC stacks as described in any one of claims 1-6.
10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the mesh optimization method for multiphysics coupling simulation of SOC stacks as described in any one of claims 1-6.