Numerical simulation optimization method and system for direct methanol fuel cell

Abstract

The invention belongs to the field of fuel cells, and provides a numerical simulation optimization method and system for a direct methanol fuel cell, and the method comprises the steps: determining a flow field geometric structure which is set as a snakelike topological path formed by continuous multiple sections of turn-back units; and determining the initial methanol permeation current density, and performing dynamic iteration on the initial methanol permeation current density to determine the real-time permeation current density of methanol diffusion in the flow field geometric structure. A dynamic permeation current density iterative algorithm is provided, electrochemical reaction and mass transfer processes are coupled in real time, permeation current density is updated in real time and coupled to cathode overpotential calculation, permeation current prediction errors are reduced to below 0.78%, and compared with the prior art, the precision is improved by more than 6 times.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of fuel cells, and particularly relates to a direct methanol fuel cell numerical simulation optimization method and system. BACKGROUND

[0002] Direct methanol fuel cells (DMFC) have become a research hotspot for portable energy due to their high energy density and low-temperature operation characteristics.

[0003] In traditional DMFC numerical simulation, the methanol permeation current density is often simplified as a fixed value or the dynamic change is ignored, which deviates from the actual electrochemical process and leads to polarization curve prediction deviation; the flow field geometry structure of parallel flow channels easily causes uneven distribution of reactants, reduces fuel utilization and exacerbates methanol permeation, and lacks quantitative optimization driven by a CFD (Computational Fluid Dynamics) model; the CFD model with strong grid dependence has low calculation efficiency, does not realize dynamic coupling of the permeation current, and does not solve the grid dependence problem. SUMMARY

[0004] To solve the above problems, the application provides a direct methanol fuel cell numerical simulation optimization method, which comprises the following steps: determining a flow field geometry structure, wherein the flow field geometry structure is set as a serpentine topology path composed of continuous multiple segments of return units; determining an initial methanol permeation current density, and dynamically iterating the initial methanol permeation current density to determine a real-time permeation current density of methanol diffusion in the flow field geometry structure.

[0005] Further, the process of determining the flow field geometry structure and the real-time permeation current density of methanol diffusion in the flow field geometry structure further comprises verifying the independence of the developed network.

[0006] Further, the determination of the initial methanol permeation current density comprises: determining a total methanol flux according to the electro-osmotic drag force and the concentration diffusion force of methanol molecules; determining the initial methanol permeation current density according to the total methanol flux.

[0007] Further, the dynamic iteration of the initial methanol permeation current density to determine the real-time permeation current density of methanol diffusion in the flow field geometry structure comprises: initializing the initial methanol permeation current density; solving a fluid equation to determine a converged velocity field and pressure field; updating the species distribution according to the converged velocity field and pressure field to determine the global species concentration distribution; The cathode overpotential is corrected according to the global species concentration and the current methanol permeation current density; The corrected cathode overpotential is updated according to the battery voltage and iteratively converged to determine the real-time permeation current density.

[0008] Further, the irrelevance verification of the development network includes: According to the specific ratio encryption base grid; Comparing the current density relative error of the base grid and the specific ratio encryption grid at the set voltage point; Setting an error threshold, step by step verifying the comparison value of the current density relative error and the error threshold until the convergence condition is met, and terminating the encryption.

[0009] The present application also provides a direct methanol fuel cell numerical simulation optimization system, the system includes a flow channel determination unit and a current density determination unit, The flow channel determination unit is used to determine the flow field geometry, and the flow field geometry is arranged as a serpentine topology path composed of a plurality of continuous segments of return units; The current density determination unit is in communication connection with the flow channel determination unit, and is used to determine the initial methanol permeation current density, and dynamically iteratively determine the real-time permeation current density of the methanol diffusion in the flow field geometry.

[0010] Further, the system also includes a verification unit for verifying the irrelevance of the development network in the process of determining the flow field geometry and determining the real-time permeation current density of the methanol diffusion in the flow field geometry.

[0011] Further, the current density determination unit includes a flux determination module and a first determination module, The flux determination module is used to determine the total methanol flux according to the methanol molecule electro-osmotic drag force and concentration diffusion force; The first determination module is used to determine the initial methanol permeation current density according to the total methanol flux.

[0012] Further, the current density determination unit also includes an initialization module, a solving module, a concentration determination module, a correction module and an iteration module, The initialization module is used to initialize the initial methanol permeation current density; The solving module is used to solve the fluid equation to determine the converged velocity field and pressure field; The concentration determination module is used to update the species distribution according to the converged velocity field and pressure field, and determine the global species concentration distribution; The correction module is used to correct the cathode overpotential according to the global species concentration and the current methanol permeation current density; An iteration module is configured to update and iteratively converge the corrected cathode overpotential to determine a real-time permeation current density according to a battery voltage.

[0013] Further, the verification unit comprises an encryption module, a comparison module and a verification module, The encryption module is configured to encrypt a basic grid according to a specific scale; The comparison module is configured to compare a current density relative error of the basic grid and the specific scale encrypted grid at a set voltage point; The verification module is configured to set an error threshold, and gradually verify a comparison value of the current density relative error and the error threshold until a convergence condition is met, and terminate the encryption.

[0014] The present application also provides an electronic device, comprising at least one processor and at least one memory, wherein the processor and the memory are data-connected, and wherein the memory stores instructions executable by the at least one processor. The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method of the present application.

[0015] The present application also provides a computer storage medium storing one or more instructions, which, when executed by one or more computers, cause the one or more computers to implement the method of the present application.

[0016] The direct methanol fuel cell numerical simulation optimization method and system of the present application propose a dynamic permeation current density iteration algorithm, real-time coupling of electrochemical reaction and mass transfer process, real-time updating of permeation current density and coupling to cathode overpotential calculation, which reduces the permeation current prediction error to below 0.78%, and improves the precision by more than 6 times compared with the prior art; the serpentine flow channel design of the present application significantly improves the reactant distribution, improves the anode methanol consumption rate and the cathode oxygen utilization rate, while reducing the permeation current density, expanding the reaction active area to make the temperature distribution more uniform; the present application proposes a grid independence verification method, and the current density difference between the basic grid and the encrypted grid is only 0.13%, which greatly compresses the single-point calculation time, improves the efficiency, and enables the complete polarization curve simulation to be completed in minutes, making real-time optimization of the flow channel possible.

[0017] Other features and advantages of the present application will be set forth in the specification, and in part will become apparent to those skilled in the art, and will be learned from practice of the present application. The objectives and other advantages of the present application will be realized and attained by the structures particularly pointed out in the specification, claims and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required by the embodiments or prior art description. Obviously, the drawings described below are only some embodiments of the present application, and the ordinary skilled in the art can obtain other drawings according to these drawings without creative effort.

[0019] Figure 1 A flow chart of the direct methanol fuel cell numerical simulation optimization method in the embodiment of the present application is shown. Figure 2 A structure diagram of the direct methanol fuel cell numerical simulation optimization system in the embodiment of the present application is shown. Figure 3 A serpentine topology path structure diagram of the flow field geometry in the embodiment of the present application is shown. DETAILED DESCRIPTION

[0020] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will combine the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by the ordinary skilled in the art without creative effort are within the protection scope of the present application.

[0021] In view of the defects in the prior art, the present application provides a direct methanol fuel cell numerical simulation optimization method, Figure 1 A flow chart of the direct methanol fuel cell numerical simulation optimization method in the embodiment of the present application is shown, Figure 1 In the method, the flow field geometry is determined, the flow field geometry is set as a serpentine topology path composed of continuous multiple turn-back units, the initial methanol permeation current density is determined, and the real-time permeation current density of methanol diffusion in the flow field geometry is determined by dynamically iterating the initial methanol permeation current density.

[0022] Specifically, in the embodiment of the present application, the process of determining the flow field geometry and the real-time permeation current density of methanol diffusion in the flow field geometry further includes verifying the irrelevance of the development network.

[0023] The following will describe in detail the specific process of the direct methanol fuel cell numerical simulation optimization method in the embodiment of the present application: In order to solve the problem that parallel flow channels easily lead to uneven distribution of reactants and high temperature localization, the present application provides a serpentine flow channel geometry optimization design, which forcibly enhances convection and turbulence by setting a serpentine topology path composed of continuous multiple turn-back units, thereby improving mass transfer efficiency,Figure 3 A serpentine topology path structure diagram of a flow field geometry in an embodiment of the present application is shown, Figure 3 In the embodiment, in order to balance the pressure drop and the reaction area, avoid the formation of a slow flow area, the flow channel size is set to 1.5*1.5 mm, three turns are made, and the inlet and outlet of the flow channel are arranged on the same side. In the embodiment of the present application, a specific embodiment is also provided to explain in detail the improvement of mass transfer efficiency of the serpentine topology path in the embodiment of the present application.

[0024] Embodiment one The traditional parallel flow channel is taken as a control group for experiments, under the same grid division and the same working condition, the distribution of reactants such as anode methanol concentration and cathode oxygen concentration, temperature field, permeation current density and catalytic layer current density distribution and other indexes are detected.

[0025] In the embodiment of the present application, the working condition is set to a temperature of 353 K, an anode methanol flow rate of 12.5 m / s, a cathode air flow rate of 25 m / s, and a current density of 3700 .

[0026] Technical effects: (1) Comparison of reactant distribution Under the peak working condition of a current density of 3700 , the serpentine flow channel of the present application significantly improves the transport of reactants: the boundary layer is destroyed by Dean vortex flow, and the methanol enrichment phenomenon at the outlet of the parallel flow channel is eliminated, so that the anode methanol consumption rate is increased from 68% of the parallel flow channel to 90%; the oxygen concentration gradient at the outlet of the serpentine flow channel is almost eliminated, and the cathode oxygen utilization rate is increased from 72% to 87%, while there is a significant low-oxygen "dead zone" at the outlet of the parallel flow channel.

[0027] Experimental evidence: According to the species distribution cloud map, the spatial utilization rates of fuel and oxidant in the serpentine flow channel are increased by 22% and 15% respectively, directly proving the enhancement effect of the flow channel turning design on mass transfer.

[0028] (2) Comparison of temperature field The three-turn design of the serpentine flow channel of the present application realizes a breakthrough in heat management, the maximum temperature is reduced from 358 K of the parallel flow channel to 355 K, and the global temperature standard deviation is compressed from 2.8 K to 0.5 K, and the temperature field uniformity is improved.

[0029] (3) Comparison of permeation current density The serpentine flow channel of the present application fundamentally suppresses methanol permeation by optimizing the distribution of reactants, and the permeation current density is reduced from 51.2 of the parallel flow channel to 30.6 ; permeability from 1.38% compression to 0.78%, all working conditions are stable in 0.76-0.78% interval. Uniform methanol distribution reduces the anode concentration gradient, which reduces the transmembrane methanol flux by 45%, directly weakening the parasitic current generation.

[0030] (4) Comparison of current density distribution of catalytic layer The flow channel of the present application enhances turbulent mixing by forced convection, avoids heat accumulation in the inlet area of the parallel flow channel, balances the reaction intensity of the whole flow channel, and makes the reaction heat uniformly diffuse to the whole catalytic layer, so that the area ratio of the catalytic layer active area with current density >1000 The active area of the catalytic layer is expanded from 52% to 82%.

[0031] In the embodiment of the present application, the dynamic permeation current density iterative process is described in detail: Step one: determine the initial methanol permeation current density In the embodiment of the present application, determining the initial methanol permeation current density comprises: determining the total methanol flux according to the methanol molecule electro-osmotic drag force and concentration diffusion force; determining the initial methanol permeation current density according to the total methanol flux. The following is a detailed description of the specific process: Step 1.1: Determine the methanol molecule electro-osmotic drag force: methanol crosses the proton exchange membrane from the anode to the cathode, and protons When migrating from the anode to the cathode, the methanol molecule is dragged, and the flux is proportional to the current density I and the methanol concentration , and the methanol molecule electro-osmotic drag force is expressed as: (1) In formula (1), represents the electro-osmotic drag force, represents the methanol electro-osmotic drag coefficient, I represents the current density, F represents the Faraday constant.

[0032] Step 1.2: Determine the concentration diffusion force: the diffusion force represents the methanol concentration gradient between the anode and the cathode, which causes diffusion, and the flux obeys Fick's law, and the concentration diffusion force is expressed as: (2) In formula (2), represents the concentration diffusion force, represents the porosity of the proton exchange membrane, represents the methanol diffusion coefficient in the membrane, represents the methanol concentration in the membrane, x represents the coordinate along the membrane thickness direction.

[0033] The total methanol flux is expressed as: (3) Step 1.3: determining an initial methanol permeation current density according to the total methanol flux, methanol reaching the cathode is oxidized, oxygen is consumed and a parasitic current is generated, the permeation current is converted according to Faraday's law, the initial methanol permeation current density is expressed as: (4) Step 2: determining a real-time permeation current density In the embodiment of the present application, the initial methanol permeation current density is fed back to the cathode overpotential to affect the cell voltage, dynamic coupling is realized, and the initial methanol permeation current density is dynamically iterated to determine the real-time permeation current density of methanol diffusion in the flow field geometry, which includes: initializing the initial methanol permeation current density; solving the fluid equation to determine the converged velocity field and pressure field; updating the species distribution according to the converged velocity field and pressure field to determine the global species concentration distribution; correcting the cathode overpotential according to the global species concentration and the current methanol permeation current density; updating and iteratively converging the corrected cathode overpotential according to the cell voltage to determine the real-time permeation current density.

[0034] Step 2.1: initializing the initial methanol permeation current density: setting the initial iteration value ; Step 2.2: solving the fluid equation to determine the converged velocity field and pressure field: Step 2.2.1: solving the continuity equation to obtain the initial velocity field : (5) In formula (5), ▽ represents the Hamiltonian operator, p represents the fluid density, and represents the velocity vector field.

[0035] Step 2.2.2: solving the momentum equation to predict the pressure field : (6) In formula (6), represents the velocity tensor, p represents the fluid pressure, p represents the dynamic viscosity, and ▽ represents the velocity gradient tensor.

[0036] Step 2.2.3: constructing the pressure Poisson equation for pressure correction: (7) In formula (7), ∇ 2 represents Laplacian operator, represents pressure correction, represents predicted velocity field.

[0037] Step 2.2.4: update velocity field and pressure field: , (8) In formula (8), represents corrected pressure field, represents velocity field satisfying mass conservation, iterated until momentum equation residual is less than first threshold value, and output converged velocity field and pressure field In the embodiment of the present application, the first threshold value is set to .

[0038] Step 2.3: update species distribution according to converged velocity field and pressure field, and determine global species concentration distribution: Step 2.3.1: update species distribution: (9) In formula (9), represents binary diffusion coefficient of species i and j , T represents temperature, M i represents molar mass of species i , M j represents molar mass of species j , p represents fluid pressure, V i represents diffusion volume of species i , V j represents diffusion volume of species j , in the embodiment of the present application, the volume of species diffusion is determined by velocity field.

[0039] Step 2.3.2: calculate effective diffusion coefficient according to species distribution: (10) In formula (10), D i represents diffusion coefficient of species i in mixture, x i represents molar fraction of species i , x j represents molar fraction of species j .

[0040] Step 2.3.3: Solve species transport equation: (11) In equation (11), denotes fluid density, denotes velocity vector field, y i denotes mass fraction of species i , denotes effective diffusion coefficient, denotes mass fraction gradient, denotes source term of species (generated / consumed by reactions).

[0041] Step 2.3.4: Output global species concentration distribution C i : (12) In equation (12), y i denotes mass fraction of species i , p denotes fluid pressure, M i denotes molar mass of species i , in the embodiment of the present application, the key output is the methanol concentration in membrane .

[0042] Step 2.4: Modify cathode overpotential according to global species concentration and current methanol permeation current density; In the embodiment of the present application, at each iteration, the permeation current density k of the first iteration is used to modify the cathode overpotential : (13) In equation (13), R denotes universal gas constant, T denotes temperature, denotes cathode charge transfer coefficient, F denotes Faraday constant, I denotes current density, denotes standard state reference oxygen concentration, denotes cathode exchange current density, denotes local oxygen concentration at cathode.

[0043] Step 2.5: Update and iterate convergence of the modified cathode overpotential to determine real-time permeation current density: The modified cathode overpotential η c is input into the cell voltage equation: (14) In equation (14), Vcell represents a battery output voltage, E0 represents a standard electrode potential, represents a corrected cathode overpotential, represents an anode overpotential, represents an ohmic overpotential, the current density is updated based on the new voltage, and the new generation of permeation current density is determined using the updated current density and concentration , when iteration is terminated.

[0044] In the embodiment of the application, a specific embodiment is also provided to specifically describe the dynamic iteration determination of the permeation current density prediction error in the embodiment of the application.

[0045] Embodiment two The conventional fixed permeation current static model is taken as a control group for experiments, and the permeation current prediction error is statistically analyzed under the same verification benchmark and the same working condition.

[0046] In the embodiment of the application, the verification benchmark is the calculation result of a million-level encryption grid (1,076,416 units), and the error is less than 0.1%. The working condition is set as follows: the temperature is 353 K, the anode methanol flow rate is 12.5 m / s, the cathode air flow rate is 25 m / s, and the current density scanning covers the low / middle / high full working condition, and the numerical value is 0-3700 . The relative error is represented as: (15) In formula (15), represents the methanol permeation current prediction value, represents the methanol permeation current true value, and the methanol permeation current true value is the encryption grid result. In the embodiment of the application, the full working condition error statistics are described in detail in Table 1: Table 1 Full working condition error statistics

[0047] After comparison, the dynamic model in the embodiment of the application maintains an error of less than 0.78% in the full current range, and the precision is improved by 6.4 times compared with the static model.

[0048] In the embodiment of the present application, in order to solve the technical problem that the CFD model in the prior art discretizes the fluid region into grid units, and the distribution of physical quantities such as velocity, pressure and temperature is predicted by solving discrete equations by computer, which requires more than one million grids to ensure accuracy and has low calculation efficiency, a grid independence verification method is proposed. Specifically, the grid independence verification of the development network in the embodiment of the present application includes: encrypting the basic grid according to a certain proportion; comparing the current density relative error of the basic grid and the certain proportion encryption grid at the set voltage point; setting an error threshold, and gradually verifying the comparison value of the current density relative error and the error threshold until the convergence condition is met, and terminating encryption.

[0049] In the embodiment of the present application, before the basic grid is encrypted by a certain proportion, the step of determining the polarization curve according to the basic grid is further included.

[0050] In the embodiment of the present application, the current density difference between the basic grid (134,552 units) and the encrypted grid (1,076,416 units) is verified by Richardson extrapolation. In the process of the specific embodiment of the present application, the density difference is determined by encrypting the grid to 351,000 units to monitor the current density change. Specifically, the current density change threshold in the embodiment of the present application is 0.13%, that is, the convergence condition criterion is that when the grid is encrypted to 351,000 units, the current density change is <0.13%. The following will be described in detail with respect to the specific steps of the grid independence verification of the development network of the present application: Step 3.1: Calculate the polarization curve of the basic grid The basic grid (134,552 control volumes) is used to divide the 9-layer structure of the DMFC. The 9-layer structure of the DMFC in the embodiment of the present application includes structures such as bipolar plate, flow channel and diffusion layer. Boundary conditions such as inlet flow rate, concentration and temperature are set. Specifically, the anode methanol flow rate is set to 12.5 m / s, the cathode air flow rate is set to 25 m / s, and the temperature is set to 353 K. Seven scanning voltage points are set with a span of In the embodiment of the present application, the step size is set to 0.1V to determine the voltage point.

[0051] By executing the dynamic iteration algorithm for each voltage point, the current density, the permeation current density and the polarization curve of the basic grid are output. The time consumption of the complete polarization curve (7 voltage points) is reduced from 39.5 hours to 23.1 minutes, saving 98% of time. It should be noted that the dynamic iteration process for the voltage point in the embodiment of the present application is a routine technical choice for those skilled in the art, which will not be described here.

[0052] Step 3.2: Encrypt the basic grid according to a certain proportion In the embodiment of the present application, the encryption ratio is 20%, 40%, 60%, 80% and 100%, and on the basis of the grid, the number of grid nodes in each axial (x, y, z) is increased according to the ratio, for example, 20% encryption means that the number of x / y / z axis grid is increased to 1.2 times, and the total number of grids is determined after encryption, in the encryption process of the embodiment of the present application, the tool is used to cut the grid unit at a certain ratio, and the boundary conditions of the encrypted grid are controlled to be consistent with the basic grid, in the embodiment of the present application, the cutting tool is refineMesh of OpenFOAM, it should be noted that the selection of the tool is only an example, and in the specific implementation process, the specific cutting tool is not limited. The number of control volumes after encryption is represented as: Table 2 Total number of grids after encryption

[0053] Step 3.3: Compare the current density relative error of the basic grid and the specific ratio encryption grid at the set voltage point In the embodiment of the present application, the calculation results of the 100% encryption grid are taken as the benchmark, the relative error of each voltage point and the encryption ratio is calculated, and the discrete error of the 100% encryption grid is controlled to be less than 0.1%, and the error formula is represented as: (16) In formula (16), represents the relative error of the grid current density at the voltage under the encryption ratio d, represents the current density calculated by the grid at the voltage under the encryption ratio d, represents the current density reference value of the reference grid at the voltage V i represents the first i scanning voltage point, and d represents the grid encryption ratio. The total voltage point error statistics is represented as: (17) In the embodiment of the present application, the maximum error of 7 voltage points is taken as the representative value of the encryption ratio.

[0054] Step 3.4: Set the error threshold, and gradually verify the comparison value of the current density relative error and the error threshold until the convergence condition is met, and terminate the encryption.

[0055] In the embodiment of the present application, when the statistical error statistical value is less than 0.13%, it is considered that the grid independence is achieved, and the gradual verification process includes: Step 3.4.1: The error statistical value of the 20% encryption grid (232,000 units) is 0.28%;​ Step 3.4.2: Calculate the error statistics of the 40% encryption grid (368,000 units) as 0.15%; Step 3.4.3: Calculate the error statistics of the 60% encryption grid (550,000 units) as 0.12%; Step 3.4.4: Since 0.12% < 0.13%, the convergence condition is met, and the encryption is terminated.

[0056] Basic grid verification conclusion: The error statistics of the basic grid (0% encryption) is 0.13%, and it can be used for engineering optimization without encryption.

[0057] In the embodiment of the application, a specific embodiment is also provided to specifically describe the grid independence verification experiment process in the embodiment of the application.

[0058] Embodiment three In the embodiment of the application, the experiment is set from hardware, software, encryption mode, working condition, etc. The hardware selects N-Quad server, the software selects OpenFOAM V4.0, the encryption mode increases the grid node ratio in x / y / z three axes synchronously, and the ratio is , the fixed voltage is 0.55 V, the temperature is 353 K, the anode methanol flow rate is 12.5 m / s, and the cathode air flow rate is 25 m / s. In the embodiment of the application, the total CPU time from initialization to dynamic iteration convergence (residual ) is the total CPU time from initialization to dynamic iteration convergence (residual Table 3: Grid encryption and calculation time consumption data

[0059] In table 3, the single-point simulation time is compressed from 336.4 minutes of 100% encryption to 3.3 minutes of the basic grid, with an efficiency improvement of 101.8 times. “3-minute simulation instead of 5 hours”, taking the optimization of the serpentine flow channel design in the embodiment of the application as an example, the traditional method needs 200 simulations (about 1,000 CPU hours), and the embodiment of the application only needs 16.5 CPU hours, accelerating the design iteration by 60 times.

[0060] In table 3, represents the multiple of the speed-up ratio, for example: the speed-up ratio of the basic grid (0% encryption) is , which means that the calculation speed is 101.8 times that of the 100% encryption grid (benchmark), that is, the calculation time consumption is only of the benchmark.

[0061] In the embodiment of the application, the current density of the 100% encryption grid (1,076,416 units) at 0.55 V is 593.449 (error Error data precision verification is performed as a reference, and the gradually encrypted error comparison is represented as: Table 4 Gradually encrypted error comparison

[0062] Through the gradually encrypted error comparison, the relative error of the basic grid (0% encryption) is only 0.0216%, which is much lower than the threshold of 0.13%; the error of the 60% encrypted grid is 0.016%, which meets the accuracy requirement of <0.13%. The accuracy of the basic grid (134,552 units) has reached the engineering standard, and there is no need for further encryption.

[0063] In the embodiment of the application, error verification is performed on the full voltage points of the basic grid and the reference grid, the reference grid is selected to be 100% encrypted, and the full voltage point error verification is represented as: Table 5 Full voltage point error verification

[0064] In the embodiment of the application, the maximum error is the relative error value of 0.13% of the 0.85V voltage and the 0.35V voltage, which meets the preset threshold. The maximum error of the basic grid under full working condition is 0.13%, which is consistent with the result of the million grid; the prediction error of the permeation current density .

[0065] The dynamic permeation current iterative algorithm provides a high-precision electrochemical coupling core for the entire system, and the real-time permeation current data (I xover ) output by the algorithm directly drives the flow channel design optimization; the serpentine flow channel geometry Relying on the algorithm feedback, the reactant distribution is improved by enhancing turbulent mixing, the permeation current is suppressed to 0.78%, and the algorithm iteration complexity is reduced; the grid independence verification method provides efficiency guarantee for the former two, and under the premise of ensuring the accuracy loss , the single-point simulation time is compressed to 3.3 minutes, so that the flow channel parameter optimization and dynamic algorithm verification can be completed within the engineering practical time scale.

[0066] The dynamic algorithm ensures accuracy, the serpentine flow channel improves performance, and the grid verification releases efficiency, and the three work together to break through the technical bottleneck that precision-efficiency-design optimization cannot be achieved in the DMFC simulation field.

[0067] In the embodiment of the application, a direct methanol fuel cell numerical simulation optimization system is also provided, Figure 2 shows a direct methanol fuel cell numerical simulation optimization system structure schematic diagram in the embodiment of the application, Figure 2In the system, the system comprises a flow channel determining unit and a current density determining unit, the flow channel determining unit is configured to determine a flow field geometry, and the flow field geometry is configured as a serpentine topology path composed of a plurality of continuous segments of a turnback unit; the current density determining unit is in communication connection with the flow channel determining unit, and is configured to determine an initial methanol permeation current density, and dynamically iteratively determine a real-time permeation current density of methanol diffusion in the flow field geometry based on the initial methanol permeation current density.

[0068] Specifically, the system further comprises a verification unit configured to verify the irrelevance of the development network in the process of determining the flow field geometry and determining the real-time permeation current density of methanol diffusion in the flow field geometry.

[0069] Specifically, the current density determining unit comprises a flux determining module and a first determining module, the flux determining module is configured to determine a total methanol flux based on an electro-osmotic drag force of methanol molecules and a concentration diffusion force; and the first determining module is configured to determine the initial methanol permeation current density based on the total methanol flux.

[0070] Specifically, the current density determining unit further comprises an initialization module, a solving module, a concentration determining module, a correction module and an iteration module, the initialization module is configured to initialize the initial methanol permeation current density; the solving module is configured to solve a fluid equation to determine a converged velocity field and a pressure field; the concentration determining module is configured to update a species distribution based on the converged velocity field and the pressure field, and determine a global species concentration distribution; the correction module is configured to correct a cathode overpotential based on the global species concentration and a current methanol permeation current density; the iteration module is configured to update and iteratively converge the corrected cathode overpotential to determine a real-time permeation current density based on a battery voltage.

[0071] Specifically, the verification unit comprises an encryption module, a comparison module and a verification module, the encryption module is configured to encrypt a basic grid based on a specific ratio; the comparison module is configured to compare a current density relative error of the basic grid and a specific-ratio-encrypted grid at a set voltage point; the verification module is configured to set an error threshold, and gradually verify a comparison value of the current density relative error and the error threshold until a convergence condition is met, and terminate the encryption.

[0072] The embodiment of the present application also provides an electronic device, the electronic device comprising at least one processor and at least one memory, the processor and the memory being connected with each other, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the direct methanol fuel cell numerical simulation optimization method in the embodiment of the present application.

[0073] The embodiment of the present application also provides a computer storage medium, the computer storage medium storing one or more instructions, the instructions enabling one or more computers to implement the direct methanol fuel cell numerical simulation optimization method in the embodiment of the present application when the one or more instructions are executed by the one or more computers.

[0074] The direct methanol fuel cell numerical simulation optimization method and system of the present application propose a dynamic permeation current density iterative algorithm, real-time coupling of electrochemical reaction and mass transfer process, real-time updating of permeation current density and coupling to cathode overpotential calculation, reduction of permeation current prediction error to below 0.78%, and improvement of precision by more than 6 times compared with prior art; the serpentine flow channel design of the present application significantly improves reactant distribution, improves anode methanol consumption rate and cathode oxygen utilization rate, reduces permeation current density, expands reaction active area, and makes temperature distribution more uniform; the present application proposes a grid independence verification method, the current density difference between the basic grid and the encrypted grid is only 0.13%, greatly compressing single-point calculation time, improving efficiency, enabling complete polarization curve simulation to be completed in minutes, and providing the possibility for real-time optimization of the flow channel.

[0075] Although the present application is described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for numerical simulation optimization of a direct methanol fuel cell, characterized in that, The method comprises: determining a flow field geometry, the flow field geometry being set as a serpentine topology path composed of continuous multiple segments of turn-around units; determining an initial methanol permeation current density, and dynamically and iteratively determining a real-time permeation current density of methanol diffusion in the flow field geometry based on the initial methanol permeation current density.

2. The direct methanol fuel cell numerical simulation optimization method according to claim 1, characterized in that, The determining of the flow field geometry and the real-time permeation current density of methanol diffusion in the flow field geometry further comprises verifying the irrelevance of a development network.

3. The direct methanol fuel cell numerical simulation optimization method according to claim 1 or 2, characterized in that, The determining of the initial methanol permeation current density comprises: determining a total methanol flux based on an electro-osmotic drag force and a concentration diffusion force of methanol molecules; and determining the initial methanol permeation current density based on the total methanol flux.

4. The direct methanol fuel cell numerical simulation optimization method according to claim 3, characterized in that, The dynamically and iteratively determining of the real-time permeation current density of methanol diffusion in the flow field geometry based on the initial methanol permeation current density comprises: initializing the initial methanol permeation current density; solving a fluid equation to determine a converged velocity field and a pressure field; updating a species distribution based on the converged velocity field and the pressure field to determine a global species concentration distribution; correcting a cathode overpotential based on the global species concentration and a current methanol permeation current density; and updating and iteratively converging the corrected cathode overpotential based on a cell voltage to determine the real-time permeation current density.

5. The direct methanol fuel cell numerical simulation optimization method according to claim 2, wherein, The verifying of the irrelevance of the development network comprises: encrypting a basic grid according to a specific ratio; comparing a current density relative error of the basic grid and the specific ratio encrypted grid at a set voltage point; setting an error threshold, and gradually verifying a comparison value of the current density relative error and the error threshold until a convergence condition is met, and terminating the encryption.

6. A direct methanol fuel cell numerical simulation optimization system, characterized in that, The system comprises a flow channel determining unit and a current density determining unit, the flow channel determining unit is configured to determine a flow field geometry, the flow field geometry being set as a serpentine topology path composed of continuous multiple segments of turn-around units; the current density determining unit is in communication connection with the flow channel determining unit, and is configured to determine an initial methanol permeation current density, and dynamically and iteratively determine a real-time permeation current density of methanol diffusion in the flow field geometry based on the initial methanol permeation current density.

7. The direct methanol fuel cell numerical simulation optimization system of claim 6, wherein, The system further comprises a verifying unit configured to verify the irrelevance of a development network in the process of determining the flow field geometry and the real-time permeation current density of methanol diffusion in the flow field geometry.

8. The direct methanol fuel cell numerical simulation optimization system according to claim 6 or 7, characterized in that, The current density determining unit comprises a flux determining module and a first determining module, the flux determining module is configured to determine a total methanol flux based on an electro-osmotic drag force and a concentration diffusion force of methanol molecules; and the first determining module is configured to determine the initial methanol permeation current density based on the total methanol flux.

9. The direct methanol fuel cell numerical simulation optimization system of claim 8, wherein, The current density determining unit further comprises an initializing module, a solving module, a concentration determining module, a correcting module and an iterating module, the initializing module is configured to initialize the initial methanol permeation current density; the solving module is configured to solve a fluid equation to determine a converged velocity field and a pressure field; the concentration determining module is configured to update a species distribution based on the converged velocity field and the pressure field to determine a global species concentration distribution; the correcting module is configured to correct a cathode overpotential based on the global species concentration and a current methanol permeation current density; and the iterating module is configured to update and iteratively converge the corrected cathode overpotential based on a cell voltage to determine the real-time permeation current density. An iteration module is configured to update and iteratively converge the corrected cathode overpotential to determine a real-time permeation current density according to a battery voltage.

10. The direct methanol fuel cell numerical simulation optimization system of claim 7, wherein, The verification unit comprises an encryption module, a comparison module and a verification module, The encryption module is configured to encrypt a basic grid according to a specific scale; The comparison module is configured to compare a current density relative error of the basic grid and the specific scale encrypted grid at a set voltage point; The verification module is configured to set an error threshold, and gradually verify a comparison value of the current density relative error and the error threshold until a convergence condition is met, and terminate the encryption.

11. An electronic device, comprising: The electronic device comprises at least one processor and at least one memory, and the processor and the memory are data connected, wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method of any one of claims 1-5.

12. A computer storage medium, characterized in that The computer storage medium stores one or more instructions, which when executed by one or more computers, cause the one or more computers to implement the method of any one of claims 1-5.

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