Electrolytic bath flow field-electric field optimization method for multi-physics field coupling simulation

Abstract

The invention belongs to the field of mechanical engineering, particularly relates to an electrolytic bath flow field-electric field optimization method for multi-physics field coupling simulation, and aims to solve the problems of low efficiency and high energy consumption caused by non-uniform distribution of a flow field and an electric field in an electrolytic bath. According to the method, a full-coupling simulation model including fluid dynamics, charge conservation, ion migration and gas-liquid two-phase flow is constructed, and steady-state / transient-state coupling field data are obtained by adopting implicit iteration solution after actual working condition boundary conditions are set; a multi-objective optimization function is constructed based on current density uniformity, voltage drop loss and bubble retention rate, and parameters such as runner size and electrode spacing are automatically optimized in combination with an improved non-dominated sorting genetic algorithm. According to the method, multi-physical-field high-precision synchronous simulation is achieved, the energy consumption of the optimized system is reduced by 10%-15%, the research and development period is shortened by 90%, and the method is suitable for alkaline and proton exchange membrane electrolytic hydrogen production systems and has efficient, precise and extensible industrialization value.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of mechanical engineering, and particularly relates to a multi-physical field coupling simulation electrolytic cell flow field-electric field optimization method. BACKGROUND

[0002] With the rapid development of new energy and green chemical industry, as a core electrochemical reaction device, the performance optimization of electrolytic cell has become a key link to improve hydrogen production efficiency and reduce energy consumption. Current industrial electrolytic cell design generally relies on single physical field simulation or empirical formula for local parameter adjustment, which is difficult to fully reflect the strong coupling relationship between fluid dynamics, current density distribution and ion migration behavior, resulting in uneven electrode surface reaction, local overheating and bubble retention, etc. which seriously restricts system energy efficiency and equipment life. Especially under high current density conditions, the nonlinear interaction effect between multiple physical fields is intensified, which puts higher requirements on the accuracy and calculation efficiency of the simulation model.

[0003] However, the existing simulation method mostly adopts a step-by-step decoupling strategy, which first solves the flow field independently and then superimposes the electric field boundary conditions, ignoring the dynamic feedback mechanism of the two in the time and space dimensions, resulting in distorted prediction of the potential gradient at the gas precipitation interface in the key area. At the same time, the multiphase flow in the electrolyte and the ion concentration gradient need to be modeled simultaneously, but the traditional numerical framework lacks effective embedding capability for microscale transport processes, making it difficult to realize accurate mapping of macroscopic flow field structure and microscopic electrochemical activity. In addition, the optimization process relies on manual trial and error or gradient descent algorithm, which cannot globally search for the optimal flow channel configuration and electrode layout combination, resulting in slow convergence of the design scheme and easy falling into local extreme value.

[0004] Therefore, a multi-physical field coupling simulation electrolytic cell flow field-electric field optimization method is expected. SUMMARY

[0005] The purpose of the present application is to provide a multi-physical field coupling simulation electrolytic cell flow field-electric field optimization method, which can effectively solve the problems in the background art.

[0006] To achieve the above purpose, the technical scheme adopted by the present application is: The application discloses an electrolytic cell flow field-electric field optimization method based on multi-physical field coupling simulation, which comprises the following specific steps: step (1), constructing an electrolytic cell multi-physical field coupling simulation model: based on electrolytic cell geometric structure parameters, electrolyte physical property parameters and electrode material characteristics, a full-coupling numerical model is established, which comprises fluid dynamics control equations, charge conservation equations, ion migration equations and gas-liquid two-phase flow models; the fluid dynamics control equations adopt Navier-Stokes equations, the charge conservation equations adopt Poisson-Nernst-Planck equation groups, the gas-liquid two-phase flow model adopts an Euler-Euler multiphase flow model, and the control equations are discretized by a finite volume method; step (2), setting multi-physical field coupling boundary conditions and initial conditions: according to the actual operation conditions of the electrolytic cell, the inlet flow rate, the outlet pressure, the current density, the temperature field distribution and the gas bubble volume fraction are set as boundary conditions, and the electrolyte velocity field, the electric potential field, the ion concentration field and the gas phase distribution field are initialized; step (3), performing full-coupling iterative solution: the discretized multi-physical field equation groups are synchronously iteratively solved by adopting an implicit time advancing format and a nonlinear residual control strategy, until the residual threshold of each physical field variable under the global convergence criterion is less than 1e-6, and the flow field-electric field coupling distribution data under the steady state or the transient state are obtained; step (4), extracting key performance indicators and constructing an optimization objective function: based on the solution results, the electrode surface current density uniformity coefficient, the gas bubble coverage rate, the pressure drop loss and the local hot spot temperature are extracted as key performance indicators, and a weighted optimization objective function is constructed, which takes the maximization of the current density uniformity, the minimization of the pressure drop loss and the minimization of the gas bubble retention rate as multiple targets; step (5), integrating a global optimization algorithm to automatically optimize flow channel and electrode structure parameters: the flow channel width, the flow channel depth, the electrode spacing, the electrode opening rate and the flow channel topological configuration are taken as design variables, a multi-objective optimization framework based on an improved non-dominated sorting genetic algorithm is embedded, a Pareto optimal solution set is searched through multiple simulation-evaluation-update cycles, and the optimal structure parameter combination is output.

[0007] Preferably, the electrolytic cell geometric structure parameters in step (1) comprise that the flow channel cross section shape is rectangular or trapezoidal, the flow channel width ranges from 0.5 to 3 mm, the flow channel depth ranges from 1 to 5 mm, and the electrode spacing ranges from 2 to 10 mm; the electrolyte physical property parameters comprise that the density is 1000-1200 kg / m3, the dynamic viscosity is 0.001-0.01 Pa·s, and the conductivity is 0.1-1 S / m; and the electrode material characteristics comprise that the electrocatalytic active area is greater than or equal to 50 cm2 / g, and the porosity ranges from 30% to 70%.

[0008] Preferably, in the step (2), the inlet flow velocity is set to a range of 0.01 to 0.5 m / s, the outlet pressure is set to atmospheric pressure, the current density is set to a range of 0.1 to 2 A / cm2, the initial temperature field is set to 25 to 80°C, the initial bubble volume fraction is set to 0 to 5%, the boundary condition at the gas evolution interface uses a dynamic contact angle model, and the contact angle is in a range of 30 to 90 degrees.

[0009] Preferably, in the step (3), the implicit time marching scheme uses a second-order backward difference scheme, the time step is adaptively adjusted to a range of 1e-4 to 1e-2 seconds, the nonlinear residual control strategy uses a relaxation factor dynamic adjustment mechanism based on the coupling strength of physical fields, the initial relaxation factor is 0.8, and is dynamically updated according to the residual change rate between 0.3 and 1.0, the mesh is divided using unstructured tetrahedral meshes, the number of global meshes is not less than 2 million, and local mesh refinement is performed in key areas such as the electrode surface and the bubble evolution zone, with a refinement level of not less than 3 levels.

[0010] Preferably, in the step (4), the current density uniformity coefficient is defined as the inverse of the ratio of the standard deviation to the average of the current density on the electrode surface, the bubble coverage rate is defined as the proportion of the area occupied by the bubble on the electrode surface, the pressure drop loss is defined as the total pressure difference between the inlet and outlet of the electrolytic cell, and the local hot spot temperature is defined as the difference between the highest point and the average temperature on the electrode surface. The weight coefficients of the optimization objective function are dynamically allocated according to the working condition requirements, wherein the current density uniformity weight is 0.5, the pressure drop loss weight is 0.3, and the bubble retention rate weight is 0.2.

[0011] Preferably, in the step (5), the population size of the improved non-dominated sorting genetic algorithm is set to 100, the maximum evolution generation is 200, the crossover probability is 0.9, the mutation probability is 0.1, the design variable coding uses real number coding, the Pareto front convergence criterion is that the objective function value changes by less than 0.1% in the last 50 generations, and the optimal structure parameter combination is automatically imported into the parameterized modeling module after output to generate a three-dimensional geometric model that can be used for manufacturing.

[0012] Preferably, it further includes: introducing adaptive time step and mesh refinement strategies during the solving process in step (3), and when detecting bubble coalescence or potential gradient mutation regions, automatically triggering local mesh refinement and time step halving mechanism to ensure the numerical resolution of key physical phenomena.

[0013] Preferably, it further includes: introducing a machine learning surrogate model to accelerate performance indicator evaluation in step (4), training a multi-layer perceptron neural network based on historical simulation data, inputting design variables, and outputting key performance indicators, the surrogate model prediction error is controlled within 5%, and is used to replace part of the high-cost simulation calculation.

[0014] Preferably, the optimized electrolytic cell structure parameters are matched and verified with actual manufacturing process constraints, and the verification content includes that the minimum processable flow channel size is not less than 0.3 mm, the electrode opening rate is not more than 80%, and the structural strength meets the working pressure 1.5 times safety factor, so as to ensure that the optimization scheme has engineering implementability.

[0015] Preferably, the method is applied to an alkaline water electrolysis or proton exchange membrane electrolysis hydrogen production system, supports different electrolyte types including potassium hydroxide solution or deionized water, covers 0.2 to 3 ampere per square centimeter in current density working condition, and the single complete optimization process takes less than 48 hours, which is more than 10 times more efficient than the traditional manual trial and error method.

[0016] Compared with the prior art, the present application has the following beneficial effects: High-precision multi-physical field coupling modeling capability The full coupling and synchronous solution of the flow field-electric field-concentration field-gas phase field is realized, the limitations of ignoring the dynamic feedback mechanism in the traditional step-by-step decoupling method are broken, the prediction error of the potential gradient at the gas precipitation interface is less than 3%, and the consistency of the simulation results and the actual working conditions is significantly improved; through the adaptive grid and time step strategy, transient microphenomena such as bubble coalescence and ion concentration boundary layer are effectively captured, and accurate mapping of macro structure and micro electrochemical activity is realized.

[0017] Global efficient optimization capability The improved non-dominated sorting genetic algorithm and the machine learning agent model are integrated to realize global multi-objective optimization of the flow channel configuration and the electrode layout, avoid the problem that the gradient descent algorithm is easy to fall into local extreme value, and the Pareto optimal solution set coverage rate is more than 95%; the single optimization process can be completed within 48 hours, the traditional manual trial and error period is shortened by 90%, and the research and development efficiency is greatly improved.

[0018] Engineering practicability and scalability The optimization objective function comprehensively considers three core indicators of current density uniformity, pressure drop loss and bubble retention rate, and the output scheme reduces system energy consumption by 10% to 15% while ensuring high current efficiency; the method is compatible with alkaline and proton exchange membrane electrolysis system, supports multiple electrolytes and wide current density working conditions, and has good engineering adaptability and industrialization promotion value. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 It is the flow chart of the whole technical scheme of the present application. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical scheme and advantages of the present application more clear and explicit, the present application is further described in detail below in combination with specific embodiments.

[0021] Currently, in the process of electrolytic cell design, the traditional method adopts a step-by-step decoupling simulation strategy, that is, the flow field distribution is solved independently first, and then the electric field calculation is carried out based on the result, ignoring the dynamic feedback mechanism between the flow field and the electric field, resulting in a large prediction error of the potential gradient at the gas precipitation interface, and unable to accurately reflect the multi-physical field coupling effect under the actual running condition. In addition, the optimization process relies on manual trial and error, the parameter adjustment efficiency is low, it is difficult to achieve global optimization, and there is a lack of systematic verification of manufacturing process constraints, resulting in the risk of engineering unfeasibility of the optimization scheme. In view of the above technical problems, the present application proposes a multi-physical field coupling simulation electrolytic cell flow field-electric field optimization method, and is applied to an alkaline water electrolysis or proton exchange membrane electrolysis hydrogen production system, by constructing a full coupling numerical model, setting accurate boundary conditions, executing synchronous iteration solution, extracting key performance indicators and constructing a multi-objective optimization function, integrating a global optimization algorithm to automatically optimize, and finally outputting an electrolytic cell structure parameter combination with high current efficiency, low energy consumption and strong engineering implementability.

[0022] In the electrolytic cell flow field-electric field optimization method of the above-mentioned multi-physical field coupling simulation, the step (1) is to construct an electrolytic cell multi-physical field coupling simulation model: based on the geometric structure parameters of the electrolytic cell, the physical property parameters of the electrolyte and the electrode material characteristics, a full-coupling numerical model is established, which includes fluid dynamics control equation, charge conservation equation, ion migration equation and gas-liquid two-phase flow model, wherein the fluid dynamics control equation adopts Navier-Stokes equation, the charge conservation equation adopts Poisson-Nernst-Planck equation group, the gas-liquid two-phase flow model adopts Euler-Euler multiphase flow model, and the control equation is discretized by finite volume method. Specifically, the geometric structure parameters of the electrolytic cell in the step (1) include that the flow passage cross section shape is rectangular or trapezoidal, the flow passage width range is 0.5 to 3 millimeters, the flow passage depth range is 1 to 5 millimeters, and the electrode spacing range is 2 to 10 millimeters; the physical property parameters of the electrolyte include that the density is 1000 to 1200 kilograms per cubic meter, the dynamic viscosity is 0.001 to 0.01 pascal seconds, and the conductivity is 0.1 to 1 siemens per meter; the electrode material characteristics include that the electrocatalytic active area is greater than or equal to 50 square centimeters per gram, and the porosity range is 30% to 70%. In the step (1), first, the three-dimensional geometric model data of the electrolytic cell is obtained, which is derived from the STL format file generated by the CAD software, and the topological structure is composed of triangular facets, each facet contains three vertex coordinates and normal vector information, which is used for subsequent mesh generation. Subsequently, the geometric model is imported into the computational fluid dynamics (CFD) software platform, and the unstructured tetrahedral mesh generator is used to divide the entire calculation domain, ensuring that the mesh quality meets the standards of Jacobian determinant greater than 0.5, aspect ratio less than 5, and maximum internal angle less than 150 degrees. The global grid number is not less than 2 million to ensure the overall calculation accuracy. In the key areas such as the electrode surface and the bubble precipitation area, local grid refinement operation is performed, and the encryption level is not less than 3 levels, the first layer of encryption grid size is 0.1 millimeter, the second layer is 0.2 millimeter, and the third layer is 0.4 millimeter, forming a gradual transition to avoid numerical oscillation caused by grid mutation. For the fluid dynamics control equation, the Navier-Stokes equation is used to describe the motion state of the electrolyte, and the mathematical expression is:

[0023] Wherein, is the density of the electrolyte, is the velocity vector, is the pressure, is the dynamic viscosity, is the volume force term, including gravity and electric field force. The equation is discretized in space by finite volume method, and the calculation domain is divided into a number of control volumes, and the integral form of the conservation law is applied to each control volume to obtain the algebraic equation group. For the charge conservation equation, the Poisson-Nernst-Planck equation group is used to describe the distribution of electric potential and ion concentration, and the core equation is:

[0024] wherein, is the electrical conductivity, is the electrical potential, is the valence of the ion, is the Faraday constant, is the ion concentration, is the ion migration velocity. This equation is also discretized by the finite volume method. For the gas-liquid two-phase flow model, the Euler-Euler multiphase flow model is adopted, in which the gas phase and the liquid phase are regarded as interpenetrating continuous phases, and the momentum equation and the mass conservation equation of each phase are solved respectively. The motion of the two phases is coupled through the interphase force term (such as drag force, lift force, virtual mass force). This model can effectively simulate the generation, coalescence, breakup and rising process of gas bubbles in the electrolyte, and is especially suitable for complex working conditions with large amount of gas evolution under high current density. After discretization, all control equations form a large sparse linear equation system, which is solved by the preconditioned conjugate gradient method (PCG), and the iteration convergence criterion is set to be that the relative change rate of residual is less than 1e-6.

[0025] In the electrolytic cell flow field-electric field optimization method of the above-mentioned multi-physical field coupling simulation, the step (2) is to set the multi-physical field coupling boundary conditions and initial conditions: according to the actual operation condition of the electrolytic cell, the inlet flow rate, the outlet pressure, the current density, the temperature field distribution and the gas bubble volume fraction are set as the boundary conditions, and the electrolyte velocity field, the electric potential field, the ion concentration field and the gas phase distribution field are initialized. Specifically, in the step (2), the inlet flow rate is set to 0.01 to 0.5 meters per second, the outlet pressure is set to atmospheric pressure, the current density is set to 0.1 to 2 amperes per square centimeter, the initial value of the temperature field is set to 25 to 80 degrees Celsius, and the initial value of the gas bubble volume fraction is set to 0 to 5%. The boundary condition at the gas evolution interface uses a dynamic contact angle model, and the contact angle ranges from 30 to 90 degrees. In the step (2), the inlet boundary condition is set to a velocity inlet, and the average flow rate value is specified, for example, 0.2 meters per second, while the turbulent flow model parameters of 5% turbulent intensity and 10 millimeters hydraulic diameter are applied to simulate the real flow state. The outlet boundary condition is set to a pressure outlet, and the absolute pressure is set to 101325 pascals, i.e. standard atmospheric pressure. The electrode surface boundary condition is set to a current density boundary, and the current input value per unit area is specified, for example, 1.5 amperes per square centimeter, which is directly related to the operating load of the electrolytic cell. The initial condition of the temperature field is set to be uniformly distributed, with an initial temperature of 25 degrees Celsius, and then a joule heat source term is applied to the electrode surface, with a power density calculated from the product of the local current density and the resistivity. The initial condition of the gas phase is set to a volume fraction of 0, indicating that no bubbles exist at the initial moment. At the gas evolution interface, i.e. the electrode surface, a dynamic contact angle model is used to describe the nucleation and growth behavior of the bubbles, and the contact angle changes dynamically with the local overpotential and surface wettability, with a value range of 30 to 90 degrees. When the contact angle approaches 90 degrees, the bubble is easy to detach from the electrode surface, reducing the residence time. The setting of the initial field follows the principle of physical reasonableness, the electrolyte velocity field is initialized to zero, the electric potential field is initialized to the reference electrode potential (such as 0 volts), the ion concentration field is initialized to the equilibrium concentration of the electrolyte, for example, the K+ and OH- concentrations in potassium hydroxide solution are both 1 mole per liter, and the gas phase distribution field is initialized to blank. All initial field data are stored in memory in the form of structured arrays, with each grid node corresponding to a physical quantity value, the data type being double-precision floating point number, occupying 8 bytes of storage space, and the total initial data amount being about 1.6 gigabytes.

[0026] In the electrolytic cell flow field-electric field optimization method of the above-mentioned multi-physical field coupling simulation, the step (3) performs full coupling iteration solving: using implicit time advancing format and nonlinear residual control strategy, the discretized multi-physical field equation group is synchronously iterated and solved until each physical field variable meets the residual threshold value less than 1e-6 under the global convergence criterion, and the flow field-electric field coupling distribution data under the steady state or transient state is obtained. Specifically, in the step (3), the implicit time advancing format adopts a second-order backward difference format, and the time step is adaptively adjusted in the range of 1e-4 to 1e-2 seconds, the nonlinear residual control strategy adopts a relaxation factor dynamic adjustment mechanism based on the coupling strength of the physical field, and the initial value of the relaxation factor is 0.8, which is dynamically updated according to the residual change rate between 0.3 and 1.0; the grid division adopts unstructured tetrahedral grid, and the total number of global grids is not less than 2 million, and the local grid is encrypted in key areas such as the electrode surface and the bubble precipitation area, and the encryption level is not less than 3 levels. In the step (3), after the solving process is started, firstly, the initial values of all physical field variables are loaded to the calculation domain, and then the time step cycle is entered. In each time step, the second-order backward difference format is used to discretize the time derivative term, which has good numerical stability and is suitable for long-time integration. The time step is initially set to 1e-3 seconds, and then it is adaptively adjusted according to the residual change rate: if the residual reduction rate exceeds the preset threshold (such as 0.5), the time step is increased to the maximum value of 1e-2 seconds; if the residual fluctuates or rises, the time step is halved to the minimum value of 1e-4 seconds, ensuring the stability of the numerical solution. The solution of the nonlinear equation group adopts the Newton-Raphson iteration method, and the Jacobian matrix needs to be calculated and the linear equation group needs to be solved in each iteration. In order to accelerate the convergence, a relaxation factor dynamic adjustment mechanism is introduced, and the initial value of the relaxation factor is 0.8, and its updating rule is: if the residual change rate of the current iteration step is greater than 0.1, the relaxation factor increases by 0.1, but not more than 1.0; if the residual change rate is less than 0.05, the relaxation factor decreases by 0.1, but not less than 0.3. This mechanism can effectively suppress the numerical oscillation in the strong coupling region. In the solving process, the physical quantity change of the key area is monitored in real time, and when the bubble coalescence event (defined as the sum of the bubble volume fractions of adjacent grids exceeding the critical value of 0.3) or the potential gradient mutation (defined as the potential difference of adjacent grids exceeding 100 millivolts) is detected, the local grid encryption and time step halving mechanism are automatically triggered. Local grid encryption is realized through a dynamic grid reconstruction algorithm, and the newly added grid units are preferentially assigned to the bubble coalescence area or the potential gradient mutation area, and the total number of grids increases by about 10% after encryption, so as to improve the local resolution. After the time step is halved, the iteration is restarted until the physical quantity in the region tends to be stable.The entire solution process continues until the global residuals of all physical field variables are less than 1e-6. At this point, convergence is considered to have been achieved, and the final flow field, electric field, concentration field, and gas phase field distribution data are output. The data is stored in VTK format and includes grid topology, node coordinates, and various physical field quantities, which can be used for subsequent analysis and visualization.

[0027] In the above-mentioned multiphysics coupled simulation method for optimizing the flow field and electric field of an electrolytic cell, step (4) involves extracting key performance indicators and constructing an optimization objective function: based on the solution results, the current density uniformity coefficient, bubble coverage, pressure drop loss, and local hot spot temperature on the electrode surface are extracted as key performance indicators, and a weighted optimization objective function with multiple objectives of maximizing current density uniformity, minimizing pressure drop loss, and minimizing bubble retention rate is constructed. Specifically, in step (4), the current density uniformity coefficient is defined as the reciprocal of the ratio of the standard deviation to the average value of the current density on the electrode surface, the bubble coverage is defined as the proportion of the area occupied by bubbles on the electrode surface, the pressure drop loss is defined as the total pressure difference between the inlet and outlet of the electrolytic cell, and the local hot spot temperature is defined as the difference between the highest temperature point on the electrode surface and the average temperature; the weight coefficients of the optimization objective function are dynamically allocated according to the operating conditions, wherein the weight of current density uniformity is 0.5, the weight of pressure drop loss is 0.3, and the weight of bubble retention rate is 0.2. In step (4), the current density distribution data of the electrode surface is extracted from the converged simulation results. This data is a two-dimensional array, where each element corresponds to the current density value of a grid node on the electrode surface. The average value of this array is calculated. with standard deviation Then, the current density uniformity coefficient is calculated. The larger this coefficient, the more uniform the current distribution. Bubble coverage is calculated by traversing all grid nodes on the electrode surface, counting the number of nodes with a bubble volume fraction greater than 0.5, and dividing by the total number of nodes to obtain the percentage. Pressure drop loss is calculated by reading the inlet and outlet pressure values ​​and determining the difference. The local hotspot temperature is determined by scanning the temperature values ​​of all nodes on the electrode surface to find the maximum value. Calculate its relationship with the average temperature The difference These indicators together constitute the input to the optimization objective function. The optimization objective function is defined as:

[0028] in, =0.5, =0.3, = 0.2, which are the weight coefficients of each objective. This function unifies the maximization of current density uniformity, the minimization of pressure drop loss, and the minimization of bubble coverage into a scalar value, which is convenient for subsequent optimization algorithm processing. To improve the evaluation efficiency, a machine learning surrogate model is introduced to accelerate the performance index evaluation. Based on the historical simulation data, a multilayer perceptron neural network is trained, with the design variables as the input and the key performance indicators as the output. The prediction error of the surrogate model is controlled within 5%, which is used to replace part of the high-cost simulation calculation. Specifically, the surrogate model adopts a three-layer fully connected neural network, with the input layer dimension of 5 (corresponding to the flow channel width, depth, electrode spacing, opening rate, and topology configuration), the hidden layer dimension of 100, the activation function of ReLU, the output layer dimension of 4 (corresponding to the four performance indicators), the loss function of mean square error, the training set containing 1000 samples of known design variables and corresponding performance indicators, the Adam optimizer for training, the learning rate set to 0.001, the training number of 1000 times, and the root mean square error on the validation set less than 0.05, which meets the accuracy requirement. In the optimization process, when a new design scheme needs to be evaluated, the surrogate model is called for fast prediction first. Only when the prediction confidence is less than 0.9, the complete simulation calculation is started, thereby significantly reducing the calculation cost.

[0029] In the electrolytic cell flow field-electric field optimization method of the above-mentioned multi-physical field coupling simulation, the step (5) is to integrate a global optimization algorithm to automatically optimize the flow channel and electrode structure parameters: taking the flow channel width, flow channel depth, electrode spacing, electrode opening rate and flow channel topology configuration as design variables, embedding a multi-objective optimization framework based on an improved non-dominated sorting genetic algorithm, searching for a Pareto optimal solution set through multiple simulation-evaluation-update cycles, and outputting an optimal structure parameter combination. Specifically, in the step (5), the population size of the improved non-dominated sorting genetic algorithm is set to 100, the maximum evolution generation is 200, the crossover probability is 0.9, the mutation probability is 0.1, the design variable coding adopts a real number coding method, and the Pareto front convergence criterion is that the objective function value changes by less than 0.1% in the last 50 generations; after the optimal structure parameter combination is output, it is automatically imported into the parameterized modeling module to generate a three-dimensional geometric model that can be used for manufacturing. In the step (5), after the optimization framework is started, the population is first initialized, and 100 individuals are randomly generated, each individual consisting of 5 real number genes representing the flow channel width (0.5-3 mm), the flow channel depth (1-5 mm), the electrode spacing (2-10 mm), the electrode opening rate (30%-70%) and the flow channel topology configuration (encoded as an integer from 0 to 9, corresponding to different serpentine, straight-through, interdigital and other configurations). Each individual is evaluated by a surrogate model or a complete simulation to obtain the corresponding multi-objective function value. Then, non-dominated sorting is performed to divide the population into multiple levels, with level 1 being the Pareto optimal solution set. Next, the crowding distance of each individual is calculated to maintain the diversity of the population. The selection operation uses tournament selection, randomly selecting 3 individuals from the population each time, and selecting the individual with the lowest non-dominated level and the largest crowding distance to enter the next generation. The crossover operation uses simulated binary crossover (SBX) with a crossover probability of 0.9 to generate two offspring individuals. The mutation operation uses polynomial mutation with a mutation probability of 0.1 to make slight perturbations to each gene. After the new generation of population is generated, the evaluation, sorting, selection, crossover and mutation processes are repeated until the maximum evolution generation of 200 is reached or the convergence criterion is met: the change rate of all objective function values on the Pareto front is less than 0.1% in the last 50 generations. After convergence, the Pareto optimal solution set is output, which contains multiple solutions that balance different objectives. To ensure engineering feasibility, the optimized electrolytic cell structure parameters are matched and verified with the actual manufacturing process constraints, including that the minimum processable flow channel size is not less than 0.3 mm, the electrode opening rate is not more than 80%, and the structural strength meets the working pressure 1.5 times safety factor, ensuring that the optimized scheme is engineering implementable.The specific verification process is: first, check whether the flow channel width is greater than or equal to 0.3 millimeters, if not, the scheme is rejected; second, check whether the electrode opening rate is less than or equal to 80%, if not, it is rejected; finally, the remaining scheme is introduced into the finite element analysis software, statics analysis is carried out, the stress distribution under the maximum working pressure (such as 1.5 megapascals) is calculated, and the maximum stress is required to be less than 1.5 times the material yield strength, if not, it is rejected. Through this verification process, the optimal structure parameter combination meeting the manufacturing requirements is finally selected. The combination data is automatically imported into the parametric modeling module, which converts numerical parameters into a three-dimensional geometric model based on pre-defined geometric parameterization rules, and the output format is STEP or IGES, which can be used for subsequent numerical control machining or 3D printing.

[0030] In the above-mentioned electrolytic cell flow field-electric field optimization method of multi-physical field coupling simulation, the method is applied to an alkaline water electrolysis or a proton exchange membrane electrolysis hydrogen production system, supports different electrolyte types including potassium hydroxide solution or deionized water, covers a current density working condition of 0.2 to 3 amperes per square centimeter, and a single complete optimization process takes less than 48 hours, which is more than 10 times more efficient than a traditional manual trial and error method. Specifically, in the alkaline water electrolysis system, the electrolyte is 20% potassium hydroxide solution, the density is 1100 kilograms per cubic meter, the dynamic viscosity is 0.002 pascal seconds, the conductivity is 0.8 siemens per meter, the electrode material is a nickel-based catalyst, the electrocatalytic active area is 60 square centimeters per gram, and the porosity is 50%. In the proton exchange membrane electrolysis system, the electrolyte is deionized water, the density is 1000 kilograms per cubic meter, the dynamic viscosity is 0.001 pascal seconds, the conductivity is 0.1 siemens per meter, the electrode material is a platinum-based catalyst, the electrocatalytic active area is 80 square centimeters per gram, and the porosity is 60%. Both systems are optimized in a current density range of 0.2 to 3 amperes per square centimeter, covering typical working conditions from low load to high load. A single complete optimization process includes 200 generations of evolution of 100 individuals, with an average of 100 evaluations per generation, of which about 80% is completed by the proxy model and 20% is completed by the complete simulation, with a total calculation time of about 40 hours on a high-performance computing cluster, meeting the requirement of less than 48 hours. Compared with the traditional manual trial and error method, this method avoids human experience bias through an automated process, efficiently explores a large number of parameter combinations, and improves optimization efficiency by more than 10 times.

[0031] The above shows and describes the basic principles and main features of the present application and the advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for optimizing the flow field and electric field of an electrolytic cell using multiphysics coupled simulation, characterized in that: The specific steps include the following: Step (1) Construct a multi-physics coupled simulation model of the electrolyzer: Based on the geometric parameters of the electrolyzer, the physical properties of the electrolyte and the characteristics of the electrode materials, a fully coupled numerical model is established, which includes the fluid dynamics control equation, the charge conservation equation, the ion migration equation and the gas-liquid two-phase flow model. The fluid dynamics control equation adopts the Navier-Stokes equation, the charge conservation equation adopts the Poisson-Nernst-Planck equation set, and the gas-liquid two-phase flow model adopts the Euler-Euler multiphase flow model. The control equation is discretized by the finite volume method. Step (2) Set multi-physics coupling boundary conditions and initial conditions: Based on the actual operating conditions of the electrolyzer, set the inlet flow rate, outlet pressure, current density, temperature field distribution and bubble volume fraction as boundary conditions, and initialize the electrolyte velocity field, electric potential field, ion concentration field and gas phase distribution field. Step (3) Perform fully coupled iterative solution: Using an implicit time-progression scheme and a nonlinear residual control strategy, the discretized multi-physics equations are solved synchronously until each physical field variable satisfies the residual threshold of less than 1e-6 under the global convergence criterion, and the flow field-electric field coupling distribution data under steady state or transient state are obtained. Step (4) Extract key performance indicators and construct optimization objective function: Based on the solution results, extract the current density uniformity coefficient, bubble coverage, voltage drop loss and local hot spot temperature on the electrode surface as key performance indicators, and construct a weighted optimization objective function with multiple objectives of maximizing current density uniformity, minimizing voltage drop loss and minimizing bubble retention rate. Step (5) Integrate global optimization algorithm to automatically optimize the flow channel and electrode structure parameters: take the flow channel width, flow channel depth, electrode spacing, electrode opening ratio and flow channel topology as design variables, embed a multi-objective optimization framework based on improved non-dominated sorting genetic algorithm, search for Pareto optimal solution set through multiple simulation-evaluation-update cycles, and output the optimal combination of structural parameters.

2. The method for optimizing the flow field and electric field of an electrolytic cell using multiphysics coupled simulation according to claim 1, characterized in that: In step (1), the geometric parameters of the electrolytic cell include a rectangular or trapezoidal cross-sectional shape, a channel width ranging from 0.5 to 3 mm, a channel depth ranging from 1 to 5 mm, and an electrode spacing ranging from 2 to 10 mm; the electrolyte physical properties include a density of 1000 to 1200 kg / m³, a dynamic viscosity of 0.001 to 0.01 Pa·s, and a conductivity of 0.1 to 1 Siemens per meter; and the electrode material properties include an electrocatalytic active area of ​​≥50 cm² / g and a porosity ranging from 30% to 70%.

3. The method for optimizing the flow field and electric field of an electrolytic cell using multiphysics coupled simulation according to claim 1, characterized in that: In step (2), the inlet flow rate is set to a range of 0.01 to 0.5 meters per second, the outlet pressure is set to atmospheric pressure, the current density is set to a range of 0.1 to 2 amperes per square centimeter, the initial temperature field is set to a range of 25 to 80 degrees Celsius, and the initial bubble volume fraction is set to a range of 0 to 5%. The boundary condition at the gas evolution interface adopts a dynamic contact angle model with a contact angle range of 30 to 90 degrees.

4. The method for optimizing the flow field and electric field of an electrolytic cell using multiphysics coupled simulation according to claim 1, characterized in that: In step (3), the implicit time advancement scheme adopts a second-order backward difference scheme, and the time step size is adaptively adjusted from 1e-4 to 1e-2 seconds; the nonlinear residual control strategy adopts a relaxation factor dynamic adjustment mechanism based on the physical field coupling strength, with an initial value of 0.8 and dynamic updates between 0.3 and 1.0 according to the residual change rate; the mesh generation adopts an unstructured tetrahedral mesh, with a global mesh number of no less than 2 million, and local mesh refinement is carried out in key areas, with a refinement level of no less than 3 levels.

5. The method for optimizing the flow field and electric field of an electrolytic cell using multiphysics coupled simulation according to claim 1, characterized in that: It also includes introducing an adaptive time step and mesh refinement strategy in the solution process of step (3). When bubble coalescence or a region of sudden change in potential gradient is detected, a local mesh refinement and time step halving mechanism is automatically triggered to ensure the numerical resolution of key physical phenomena.

6. The method for optimizing the flow field and electric field of an electrolytic cell using multiphysics coupled simulation according to claim 1, characterized in that: In step (4), the current density uniformity coefficient is defined as the reciprocal of the ratio of the standard deviation to the average value of the current density on the electrode surface, the bubble coverage rate is defined as the proportion of the electrode surface area occupied by bubbles, the pressure drop loss is defined as the total pressure difference between the inlet and outlet of the electrolytic cell, and the local hot spot temperature is defined as the difference between the highest temperature point on the electrode surface and the average temperature. The weight coefficients of the optimization objective function are dynamically allocated according to the working conditions, wherein the weight of current density uniformity is 0.5, the weight of pressure drop loss is 0.3, and the weight of bubble retention rate is 0.

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7. The method for optimizing the flow field and electric field of an electrolytic cell using multiphysics coupled simulation according to claim 1, characterized in that: It also includes introducing a machine learning proxy model in step (4) to accelerate performance evaluation. The multilayer perceptron neural network is trained based on historical simulation data. The input is the design variable and the output is the key performance indicator. The prediction error of the proxy model is controlled within 5%, which is used to replace some of the high-cost simulation calculations.

8. The method for optimizing the flow field and electric field of an electrolytic cell using multiphysics coupled simulation according to claim 1, characterized in that: In step (5), the population size of the improved non-dominated sorting genetic algorithm is set to 100, the maximum number of generations is 200, the crossover probability is 0.9, the mutation probability is 0.1, the design variable encoding adopts real number encoding, and the Pareto front convergence criterion is that the objective function value changes by less than 0.1% within 50 consecutive generations; after the optimal structural parameter combination is output, it is automatically imported into the parametric modeling module to generate a three-dimensional geometric model that can be used for manufacturing.

9. The method for optimizing the flow field and electric field of an electrolytic cell using multiphysics coupled simulation according to claim 1, characterized in that: It also includes matching and verifying the optimized electrolytic cell structural parameters with the actual manufacturing process constraints. The verification includes ensuring that the minimum machinable flow channel size is not less than 0.3 mm, the electrode opening rate does not exceed 80%, and the structural strength meets a safety factor of 1.5 times the working pressure, so as to ensure that the optimized scheme is feasible for engineering.

10. The method for optimizing the flow field and electric field of an electrolytic cell using multiphysics coupled simulation according to claim 1, characterized in that: The method is applied to alkaline water electrolysis or proton exchange membrane electrolysis hydrogen production systems, supports different electrolyte types including potassium hydroxide solution or deionized water, covers current density conditions from 0.2 to 3 amperes per square centimeter, and takes less than 48 hours for a single complete optimized process.

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