SOFC (solid oxide fuel cell) flow field optimization method and device for multi-physics field coupling

Abstract

The invention belongs to the field of energy conversion and storage, and particularly discloses a multi-physics field coupled SOFC flow field optimization and device, and the method comprises the steps: initializing the parameters of a single solid oxide fuel cell SOFC flow field, and obtaining an initial population corresponding to an extended ridge structure; determining the length of the extended ridge structure of the SOFC, and calculating flow distribution information and temperature distribution information of an SOFC flow field under the extended ridge structure by using a multi-physics field coupling method; calculating the fitness of the initial population according to the flow distribution information and the temperature distribution information to obtain a calculation result; performing iterative optimization on the SOFC flow field based on the calculation result by using a multi-population genetic algorithm so as to obtain the optimally prolonged ridge structure length of the SOFC flow field; according to the invention, the performance and the service life of the battery can be improved.

Description

Technical Field

[0001] The present application belongs to the field of energy conversion and storage, and more specifically, to a multi-physical field coupled SOFC flow field optimization method and device. Background Art

[0002] In the field of energy (hydrogen energy) conversion and storage in renewable energy, the most widely used is the flat-plate single solid oxide fuel cell SOFC, which is composed of batteries, sealing materials, and metal connectors. Usually, the operating temperature of SOFC is 700-800℃. In such a high temperature environment, the local hot spots inside and the resulting thermal stress will be magnified several times. The SOFC battery is a ceramic structure and the connector is a metal material. There is a certain difference in their thermal expansion coefficients. Therefore, poor temperature consistency can easily cause rupture and failure inside the battery, causing the battery performance to be greatly weakened and the service life to be reduced. In addition, the poor flow consistency of SOFC will also lead to a decline in many aspects of SOFC performance, including differences in temperature distribution, uneven concentration of reactants, fatigue and failure of the structure, increased energy and heat loss, and instability in operation.

[0003] Therefore, how to improve the temperature and flow consistency inside the battery and thus improve battery performance is a technical problem that needs to be solved urgently. Summary of the invention

[0004] In view of the defects of the prior art, the purpose of the present application is to provide a multi-physical field coupled SOFC flow field optimization method and device, aiming to solve the problem of poor battery performance caused by poor temperature and flow consistency.

[0005] In a first aspect, the present application provides a multi-physics field coupled SOFC flow field optimization method, comprising:

[0006] Initializing the parameters of the flow field of a single solid oxide fuel cell SOFC to obtain an initial population corresponding to the extended ridge structure;

[0007] Determine the length of the extended ridge structure of SOFC, and use the multi-physics field coupling method to calculate the flow distribution information and temperature distribution information of the SOFC flow field under the extended ridge structure;

[0008] The fitness of the initial population is calculated according to the flow distribution information and the temperature distribution information to obtain the calculation result;

[0009] Iteratively optimizing the SOFC flow field based on the calculation results using a multi-population genetic algorithm to obtain an optimal extended ridge structure length of the SOFC flow field;

[0010] The extended ridge structure adjusts the gas flow rate into each flow channel by changing its length to achieve consistency in flow and temperature.

[0011] Optionally, determining the length of the extended ridge structure of the SOFC, and calculating the flow distribution information and the temperature distribution information of the SOFC flow field under the extended ridge structure by using a multi-physics field coupling method, including:

[0012] Use multi-physics coupling method to calculate the flow distribution and temperature distribution in the battery;

[0013] The flow distribution and temperature distribution are statistically calculated by calculating the average flow velocity in each flow channel to obtain a flow consistency index as flow distribution information, and the average temperature of the battery middle interface is calculated to obtain a temperature consistency index as temperature distribution information.

[0014] Optionally, the fitness of the initial population is calculated according to the flow distribution information and the temperature distribution information to obtain a calculation result, including:

[0015] Extract key flow parameters for flow distribution information and key temperature parameters for temperature distribution information;

[0016] Based on the preset flow parameter weights and temperature parameter weights, weighted fitness calculation is performed in combination with the flow key parameters and the temperature key parameters to obtain the fitness calculation result.

[0017] Optionally, a multi-population genetic algorithm is used to iteratively optimize the SOFC flow field based on the calculation results to obtain an optimal extended ridge structure length of the SOFC flow field, including:

[0018] According to the calculated results of the fitness, hybridization and mutation of the initial population are performed to form a new next generation population;

[0019] In the process of hybridization and mutation to form the next generation population, the population is continuously optimized based on the multi-population genetic algorithm until the maximum number of iterations is reached to obtain the elite population as the optimal extended ridge structure length.

[0020] Optionally, the process of continuously optimizing the population based on the multi-population genetic algorithm includes:

[0021] Divide the next generation population into multiple sub-populations;

[0022] Individuals migrate between each subpopulation, and each subpopulation after migration selects elites according to their fitness to produce elite individuals;

[0023] The elite individuals of each sub-population are combined to obtain the elite population.

[0024] Optionally, it also includes:

[0025] adjusting the flow rate in each flow channel based on the length of the extended ridge structure to change the maximum temperature deviation in different flow field types;

[0026] Among them, the flow field types include Z flow field, U flow field and L flow field; the flow field type is determined according to the flow direction.

[0027] In a second aspect, the present application also provides a multi-physics field coupled SOFC flow field optimization device, comprising:

[0028] An initialization module is used to initialize the parameters of the flow field of the single solid oxide fuel cell SOFC to obtain an initial population corresponding to the extended ridge structure;

[0029] A coupling calculation module is used to determine the length of the extended ridge structure of the SOFC, and calculate the flow distribution information and temperature distribution information of the SOFC flow field under the extended ridge structure by using a multi-physics field coupling method;

[0030] A genetic calculation module is used to calculate the fitness of the initial population according to the flow distribution information and the temperature distribution information to obtain the calculation result;

[0031] An iterative module, for iteratively optimizing the SOFC flow field based on the calculation results by using a multi-population genetic algorithm to obtain an optimal extended ridge structure length of the SOFC flow field;

[0032] The extended ridge structure adjusts the gas flow rate into each flow channel by changing its length to achieve consistency in flow and temperature.

[0033] In a third aspect, the present application also provides an improved SOFC structure, comprising: a plurality of connectors, wherein a cell contact surface of each connector forms a ridge, and grooves are formed between the ridges to form banks for gas flow;

[0034] The length of the ridge can be adjusted to adapt to different air intake arrangements, adjust the gas flow rate and gas pressure entering each flow channel, and improve the temperature distribution of the battery.

[0035] In a fourth aspect, the present application provides an electronic device comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory. When the programs stored in the memory are executed, the processor is used to execute the method described in the first aspect or any possible implementation of the first aspect.

[0036] In a fifth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.

[0037] In a sixth aspect, the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.

[0038] It can be understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0039] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the prior art:

[0040] (1) The present application adjusts the flow rate by changing the length of the extended ridge structure to achieve consistency of flow and temperature in each flow channel of the battery, thereby improving the safety and operational stability of the battery. By adjusting the ridges of different lengths, the gas flow rate into each flow channel can be controlled, which helps to achieve coordination between gas flow and temperature distribution, thereby improving energy conversion efficiency and battery life; by applying a multi-population genetic algorithm for calculation and iterative optimization, it can effectively handle complex optimization problems and find the optimal ridge structure length to quickly determine the optimal solution for the structural length of the SOFC, thereby improving the structural strength of the battery and the battery life.

[0041] (2) This application can obtain flow consistency and temperature consistency indicators inside the battery. Based on the flow consistency and temperature consistency indicators, the design of the solid oxide fuel cell can be evaluated and optimized to ensure more uniform and stable flow and thermal behavior in actual operation, thereby improving the overall performance and reliability of the battery.

[0042] (3) This application uses a multi-population genetic algorithm to efficiently optimize the length of the ridge structure in SOFC and achieve improvements in flow distribution and temperature distribution. It not only demonstrates the advantages of genetic algorithms in complex optimization problems, but also ensures the algorithm's exploration ability and convergence performance through mechanisms such as fitness evaluation and individual migration to find the design solution with the best performance.

[0043] (4) The solid oxide fuel cell of the present application has the characteristics of easy processing and strong thermal regulation ability, is convenient for mass production, and is expected to be widely used. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is one of the flow diagrams of the SOFC flow field optimization method of multi-physical field coupling provided in the embodiment of the present application;

[0045] Figure 2 is a schematic diagram of an extended ridge according to an embodiment of the present application;

[0046] Figure 3 This is the second flow chart of the SOFC flow field optimization method of multi-physical field coupling provided in the embodiment of the present application;

[0047] Figure 4 It is a structural schematic diagram of a multi-physical field coupled SOFC flow field optimization device provided in an embodiment of the present application;

[0048] Figure 5 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0050] The term "and / or" in this article is a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The symbol " / " in this article indicates that the associated objects are in an or relationship, for example, A / B means A or B.

[0051] The terms "first" and "second" in the specification and claims herein are used to distinguish different objects rather than to describe a specific order of the objects. For example, a first response message and a second response message are used to distinguish different response messages rather than to describe a specific order of the response messages.

[0052] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0053] In the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more than two. For example, multiple processing units refer to two or more processing units, etc.; multiple elements refer to two or more elements, etc.

[0054] Next, the technical solutions provided in the embodiments of the present application are introduced.

[0055] Reference Figure 1 The present application provides a multi-physics field coupled SOFC flow field optimization method, comprising:

[0056] S101. Initializing the parameters of the flow field of the single solid oxide fuel cell SOFC to obtain an initial population corresponding to the extended ridge structure;

[0057] S102. Determine the length of the extended ridge structure of the SOFC, and calculate the flow distribution information and temperature distribution information of the SOFC flow field under the extended ridge structure using a multi-physics field coupling method;

[0058] S103. Calculate the fitness of the initial population according to the flow distribution information and the temperature distribution information to obtain a calculation result;

[0059] S104. Iteratively optimizing the SOFC flow field based on the calculation results using a multi-population genetic algorithm to obtain an optimal extended ridge structure length of the SOFC flow field;

[0060] The extended ridge structure adjusts the gas flow rate into each flow channel by changing its length to achieve consistency in flow and temperature.

[0061] First, in step S101, the flow field parameters of the single solid oxide fuel cell (SOFC) are initialized. For example, the flow field parameters may include setting the physical properties of the fluid (such as density, viscosity, etc.), flow boundary conditions (such as the flow rate and pressure of the air inlet, the pressure of the air outlet, etc.) and the geometric model inside the battery. The initial parameters will serve as the basis for subsequent calculations to ensure the true reflection of the model.

[0062] Further, in step S102, the multi-physics field coupling method is applied to calculate the flow distribution information and temperature distribution information in the flow field through numerical simulation software. In this process, the mutual influence of various physical phenomena, such as gas flow, heat conduction and their comprehensive effects on battery performance, is considered. The flow velocity, pressure and temperature distribution of each point in the flow field are obtained, so as to comprehensively evaluate the operating status of the battery.

[0063] In step S103, a multi-population genetic algorithm is applied to calculate the flow distribution information and the temperature distribution information under different conditions of extended ridge structure lengths. Genetic algorithms can efficiently handle complex optimization problems by simulating natural selection and genetic mechanisms. Multiple populations are designed to explore different ridge structure parameters, and the quality of the solution is gradually improved through operations such as crossover and mutation, so as to obtain the best calculation results for flow and temperature distribution.

[0064] After obtaining the calculation results, step S104 performs iterative optimization based on these results to determine the optimal length of the extended ridge structure. This step involves adjusting the length of the ridge, restarting the calculation and evaluating its impact on the consistency of flow and temperature. A feedback mechanism is used to continuously analyze and optimize the design to achieve the ideal flow rate and temperature distribution goals. Continuous optimization not only improves battery performance, but also improves battery stability and safety.

[0065] It should be noted that in the embodiment of the present application, the fluid flow rate is adjusted by changing the length of the ridge, so that the gas flow and temperature distribution of each flow channel are more consistent, thereby improving the overall efficiency.

[0066] Specific reference Figure 2 The contact surface between the connector and the battery is called a ridge, and the grooves for gas circulation between the ridges are called banks. By extending the ridges, the flow rate of the gas entering each flow channel can be adjusted. In different air intake arrangements, the different lengths of each ridge can adjust the inlet and outlet pressures of each flow channel, thereby adjusting the flow rate and improving the temperature distribution of the battery.

[0067] Optionally, in this embodiment, the width of the flow channel groove and the ridge is set to 0.5-2 mm, the flow channel length is set to 50-150 mm, and the intake manifold width is set to 8-20 mm. It should be noted that the parameter settings in this application are only for exemplary reference and do not specifically limit the values ​​of the parameters.

[0068] The present application can obtain relatively accurate flow distribution information and temperature distribution information by initializing and calculating the flow field parameters of a single solid oxide fuel cell (SOFC). By utilizing an extended ridge structure and adjusting ridges of different lengths, the gas flow rate into each flow channel can be accurately controlled, which helps to achieve coordination between gas flow and temperature distribution, thereby improving energy conversion efficiency and battery life. By applying a multi-population genetic algorithm for calculation and iterative optimization, complex optimization problems can be effectively handled and the optimal ridge structure length can be found to quickly determine the optimal solution for the structural length of the SOFC and improve the structural strength of the battery.

[0069] Optionally, determining the length of the extended ridge structure of the SOFC, and calculating the flow distribution information and the temperature distribution information of the SOFC flow field under the extended ridge structure by using a multi-physics field coupling method, including:

[0070] Use multi-physics coupling method to calculate the flow distribution and temperature distribution in the battery;

[0071] The flow distribution and temperature distribution are statistically calculated by calculating the average flow velocity in each flow channel to obtain a flow consistency index as flow distribution information, and the average temperature of the battery middle interface is calculated to obtain a temperature consistency index as temperature distribution information.

[0072] Specifically, in the optimization design of solid oxide fuel cells (SOFCs), this embodiment applies the multi-physics field coupling method to comprehensively calculate the flow distribution and temperature distribution inside the battery. Exemplarily, the process is as follows:

[0073] Use appropriate numerical methods (such as finite element method, finite volume method, etc.) to couple and solve fluid mechanics (flow) and heat conduction (temperature). The calculated results include the flow velocity at different locations (such as the flow velocity in each flow channel) and the temperature distribution (local temperature changes inside the battery).

[0074] In order to evaluate the uniformity or consistency of the flow, the average value of the gas flow velocity in each flow channel is calculated. This calculation process can be divided into the following steps: Extract the flow velocity data in each flow channel from the calculation results, including the local value of the flow velocity. Calculate the flow velocity of each flow channel and calculate its average value. Based on the average flow velocity of each flow channel, the flow consistency index is defined, and statistics such as standard deviation or coefficient of variation can be used to quantify the non-uniformity of the flow.

[0075] Similar to the flow consistency index, the temperature distribution of the middle interface of the battery is used to calculate the average temperature and consistency index. The specific steps are as follows: Extract the temperature data of each position of the battery from the calculation results, especially pay attention to the temperature distribution of the middle interface. Perform statistical calculations on the temperature of each interface to obtain the average temperature. Similarly, the temperature consistency index is provided by the standard deviation or coefficient of variation of the temperature.

[0076] Through this embodiment, the flow consistency and temperature consistency indexes inside the battery can be obtained. According to the flow consistency and temperature consistency indexes, the design of the solid oxide fuel cell can be evaluated and optimized to ensure more uniform and stable flow and thermal behavior in actual operation, thereby improving the overall performance and reliability of the battery.

[0077] Optionally, the fitness of the initial population is calculated according to the flow distribution information and the temperature distribution information to obtain a calculation result, including:

[0078] Extract key flow parameters for flow distribution information and key temperature parameters for temperature distribution information;

[0079] Based on the preset flow parameter weights and temperature parameter weights, weighted fitness calculation is performed in combination with the flow key parameters and the temperature key parameters to obtain the fitness calculation result.

[0080] Further, a multi-population genetic algorithm is used to iteratively optimize the SOFC flow field based on the calculation results to obtain the optimal extended ridge structure length of the SOFC flow field, including:

[0081] According to the calculated results of the fitness, hybridization and mutation of the initial population are performed to form a new next generation population;

[0082] In the process of hybridization and mutation to form the next generation population, the population is continuously optimized based on the multi-population genetic algorithm until the maximum number of iterations is reached to obtain the elite population as the optimal extended ridge structure length.

[0083] Optionally, the process of continuously optimizing the population based on the multi-population genetic algorithm includes:

[0084] Divide the next generation population into multiple sub-populations;

[0085] Individuals migrate between each subpopulation, and each subpopulation after migration selects elites according to their fitness to produce elite individuals;

[0086] The elite individuals of each sub-population are combined to obtain the elite population.

[0087] Specifically, in this embodiment, firstly, a fitness function is designed based on the flow and temperature data of the key parameters. For example, the fitness can be defined as the weighted sum of the flow consistency index and the temperature consistency index. Then, the fitness value is calculated for each individual of the first generation. The higher the fitness, the better the ridge structure length configuration.

[0088] The iterative optimization process is as follows:

[0089] The initial population is selected according to fitness, and individuals with higher fitness are selected for reproduction. A new next generation population is generated through crossover and mutation. Crossover: Select two high-quality individuals and perform gene crossover to generate new individuals, bringing new combinations of ridge structure lengths. Mutation: Randomly change the ridge lengths of some individuals to introduce new features and prevent premature convergence.

[0090] Repeat the multi-physics flow and temperature distribution calculations, extract the key flow and temperature parameters of the new generation of individuals, and calculate the fitness.

[0091] Each generation of population is divided into multiple sub-populations, each of which evolves independently, and individuals migrate between different sub-populations to promote diversity and explore a wider solution space. Furthermore, elite selection is performed within each sub-population, and the individuals with the highest fitness are selected to form elite individuals. Finally, the elite individuals of each sub-population are combined to form a new elite population as the basis for the next round of iteration.

[0092] In this embodiment, the maximum number of iterations needs to be pre-set, and when the number is reached, the calculation is stopped. After the iteration stops, the individual with the highest fitness is selected from the elite population finally generated to obtain the optimal configuration of the extended ridge structure length.

[0093] The embodiment of the present application uses a multi-population genetic algorithm to efficiently optimize the length of the ridge structure in SOFC and achieve improvement in the flow distribution and temperature distribution indicators. It not only demonstrates the advantages of genetic algorithms in complex optimization problems, but also ensures the algorithm's exploration ability and convergence performance through mechanisms such as fitness evaluation and individual migration to find the design solution with the best performance.

[0094] Optionally, it also includes:

[0095] adjusting the flow rate in each flow channel based on the length of the extended ridge structure to change the maximum temperature deviation in different flow field types;

[0096] Among them, the flow field types include Z flow field, U flow field and L flow field; the flow field type is determined according to the flow direction.

[0097] The extended ridges can effectively regulate the flow rate in each flow channel, and the maximum temperature deviation can be reduced by 64.20%, 26.06%, and 23.45% in the Z, U, and L flow fields, respectively.

[0098] Reference Figure 3 , the complete process diagram of this application includes the following steps:

[0099] First, the ridge length is initialized to form the first generation. Then, the multi-physical field flow and temperature distribution calculations are performed on the first generation. The key parameters are extracted for fitness calculation, and then hybridization and mutation are performed to form a new next-generation population. In the process of mutation and hybridization to form the next-generation population, it is divided into multiple populations. Each population will migrate to each other, and elite selection will be performed to produce an elite population. Finally, the number of iterations is determined.

[0100] Reference Figure 4 The present application also provides a multi-physics field coupled SOFC flow field optimization device, comprising:

[0101] Initialization module 410, used to initialize the parameters of the flow field of the single solid oxide fuel cell SOFC to obtain an initial population corresponding to the extended ridge structure;

[0102] A coupling calculation module 420 is used to determine the length of the extended ridge structure of the SOFC, and calculate the flow distribution information and temperature distribution information of the SOFC flow field under the extended ridge structure by using a multi-physics field coupling method;

[0103] Genetic calculation module 430, used to calculate the fitness of the initial population according to the flow distribution information and the temperature distribution information to obtain a calculation result;

[0104] An iterative module 440 is used to iteratively optimize the SOFC flow field based on the calculation results using a multi-population genetic algorithm to obtain an optimal extended ridge structure length of the SOFC flow field;

[0105] The extended ridge structure adjusts the gas flow rate into each flow channel by changing its length to achieve consistency in flow and temperature.

[0106] Optionally, determining the length of the extended ridge structure of the SOFC, and calculating the flow distribution information and the temperature distribution information of the SOFC flow field under the extended ridge structure by using a multi-physics field coupling method, including:

[0107] Use multi-physics coupling method to calculate the flow distribution and temperature distribution in the battery;

[0108] The flow distribution and temperature distribution are statistically calculated by calculating the average flow velocity in each flow channel to obtain a flow consistency index as flow distribution information, and the average temperature of the battery middle interface is calculated to obtain a temperature consistency index as temperature distribution information.

[0109] Optionally, the fitness of the initial population is calculated according to the flow distribution information and the temperature distribution information to obtain a calculation result, including:

[0110] Extract key flow parameters for flow distribution information and key temperature parameters for temperature distribution information;

[0111] Based on the preset flow parameter weights and temperature parameter weights, weighted fitness calculation is performed in combination with the flow key parameters and the temperature key parameters to obtain the fitness calculation result.

[0112] Optionally, a multi-population genetic algorithm is used to iteratively optimize the SOFC flow field based on the calculation results to obtain an optimal extended ridge structure length of the SOFC flow field, including:

[0113] According to the calculated results of the fitness, hybridization and mutation of the initial population are performed to form a new next generation population;

[0114] In the process of hybridization and mutation to form the next generation population, the population is continuously optimized based on the multi-population genetic algorithm until the maximum number of iterations is reached to obtain the elite population as the optimal extended ridge structure length.

[0115] Optionally, the process of continuously optimizing the population based on the multi-population genetic algorithm includes:

[0116] Divide the next generation population into multiple sub-populations;

[0117] Individuals migrate between each subpopulation, and each subpopulation after migration selects elites according to their fitness to produce elite individuals;

[0118] The elite individuals of each sub-population are combined to obtain the elite population.

[0119] Optionally, a regulating module is further included, for:

[0120] adjusting the flow rate in each flow channel based on the length of the extended ridge structure to change the maximum temperature deviation in different flow field types;

[0121] Among them, the flow field types include Z flow field, U flow field and L flow field; the flow field type is determined according to the flow direction.

[0122] It can be understood that the detailed functional implementation of each of the above-mentioned units / modules can be found in the introduction of the aforementioned method embodiment, and will not be repeated here.

[0123] It should be understood that the above-mentioned device is used to execute the method in the above-mentioned embodiment. The implementation principle and technical effect of the corresponding program module in the device are similar to those described in the above-mentioned method. The working process of the device can refer to the corresponding process in the above-mentioned method, which will not be repeated here.

[0124] The present application also provides an improved SOFC structure, comprising: a plurality of connectors, wherein the battery contact surface of each connector forms a ridge, and grooves are formed between the ridges to form banks for gas circulation;

[0125] The length of the ridge can be adjusted to adapt to different air intake arrangements, adjust the gas flow rate and gas pressure entering each flow channel, and improve the temperature distribution of the battery.

[0126] Reference Figure 5 Based on the method in the above embodiment, the embodiment of the present application provides an electronic device, which may include: a processor (processor) 510, a communication interface (CommunicationsInterface) 520, a memory (memory) 530 and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 can call the logic instructions in the memory 530 to execute the method in the above embodiment.

[0127] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.

[0128] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.

[0129] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0130] It is understandable that the processor in the embodiment of the present application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor can be a microprocessor or any conventional processor.

[0131] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0132] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0133] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.

[0134] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A multi-physics field coupled SOFC flow field optimization method, characterized in that: include: Initializing the parameters of the flow field of a single solid oxide fuel cell SOFC to obtain an initial population corresponding to the extended ridge structure; Determine the length of the extended ridge structure of SOFC, and use the multi-physics field coupling method to calculate the flow distribution information and temperature distribution information of the SOFC flow field under the extended ridge structure; The fitness of the initial population is calculated according to the flow distribution information and the temperature distribution information to obtain the calculation result; Iteratively optimizing the SOFC flow field based on the calculation results using a multi-population genetic algorithm to obtain an optimal extended ridge structure length of the SOFC flow field; The extended ridge structure adjusts the gas flow rate into each flow channel by changing its length to achieve consistency in flow and temperature.

2. The SOFC flow field optimization method according to claim 1, characterized in that: Determine the length of the extended ridge structure of SOFC, and use the multi-physics field coupling method to calculate the flow distribution information and temperature distribution information of the SOFC flow field under the extended ridge structure, including: Use multi-physics coupling method to calculate the flow distribution and temperature distribution in the battery; The flow distribution and temperature distribution are statistically calculated by calculating the average flow velocity in each flow channel to obtain a flow consistency index as flow distribution information, and the average temperature of the battery middle interface is calculated to obtain a temperature consistency index as temperature distribution information.

3. The SOFC flow field optimization method according to claim 1, characterized in that: The fitness of the initial population is calculated based on the flow distribution information and the temperature distribution information, and the calculation results are obtained, including: Extract key flow parameters for flow distribution information and key temperature parameters for temperature distribution information; Based on the preset flow parameter weights and temperature parameter weights, weighted fitness calculation is performed in combination with the flow key parameters and the temperature key parameters to obtain the fitness calculation result.

4. The SOFC flow field optimization method according to claim 3, characterized in that: The SOFC flow field is iteratively optimized based on the calculation results using a multi-population genetic algorithm to obtain the optimal extended ridge structure length of the SOFC flow field, including: According to the calculated results of the fitness, hybridization and mutation of the initial population are performed to form a new next generation population; In the process of hybridization and mutation to form the next generation population, the population is continuously optimized based on the multi-population genetic algorithm until the maximum number of iterations is reached to obtain the elite population as the optimal extended ridge structure length.

5. The SOFC flow field optimization method according to claim 1, characterized in that: The process of continuously optimizing the population based on the multi-population genetic algorithm includes: Divide the next generation population into multiple sub-populations; Individuals migrate between each subpopulation, and each subpopulation after migration selects elites according to their fitness to produce elite individuals; The elite individuals of each sub-population are combined to obtain the elite population.

6. The SOFC flow field optimization method according to claim 1, characterized in that: Also includes: adjusting the flow rate in each flow channel based on the length of the extended ridge structure to change the maximum temperature deviation in different flow field types; Among them, the flow field types include Z flow field, U flow field and L flow field; the flow field type is determined according to the flow direction.

7. A multi-physics field coupled SOFC flow field optimization device, characterized in that: include: An initialization module is used to initialize the parameters of the flow field of the single solid oxide fuel cell SOFC to obtain an initial population corresponding to the extended ridge structure; A coupling calculation module is used to determine the length of the extended ridge structure of the SOFC, and calculate the flow distribution information and temperature distribution information of the SOFC flow field under the extended ridge structure by using a multi-physics field coupling method; A genetic calculation module is used to calculate the fitness of the initial population according to the flow distribution information and the temperature distribution information to obtain the calculation result; An iterative module, for iteratively optimizing the SOFC flow field based on the calculation results by using a multi-population genetic algorithm to obtain an optimal extended ridge structure length of the SOFC flow field; The extended ridge structure adjusts the gas flow rate into each flow channel by changing its length to achieve consistency in flow and temperature.

8. An improved SOFC structure, characterized in that: include: A plurality of connectors, each of which has a battery contact surface formed with a ridge, and grooves between the ridges forming banks for gas circulation; The length of the ridge can be adjusted to adapt to different air intake arrangements, adjust the gas flow rate and gas pressure entering each flow channel, and improve the temperature distribution of the battery.

9. An electronic device, characterized in that: include: at least one memory for storing a computer program; At least one processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method according to any one of claims 1 to 6.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program runs on a processor, the processor is caused to execute the method according to any one of claims 1 to 6.

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