A multi-physics coupling optimization method and related products for a co-electrolysis system

Abstract

The embodiment of the present application relates to the field of new energy technology, and provides a multi-physics field coupling optimization method and related products of a co-electrolysis system, the method comprising: obtaining a coupling mathematical model and a fractal porous medium permeability model during the operation of the co-electrolysis system; obtaining the corrected control parameters of the co-electrolysis system based on the physical field parameters output by the electrochemical kinetics calculation model, the non-Darcy seepage simulation model, and the thermal stress field calculation model; obtaining the optimized permeability by adjusting the fractal parameters of the fractal porous medium permeability model; providing the optimized permeability to the non-Darcy seepage simulation model to update the flow field pressure distribution; performing multi-objective optimization on the coupling mathematical model based on the physical field parameters updated in real time by the electrochemical kinetics calculation model, the non-Darcy seepage simulation model, and the thermal stress field calculation model, to obtain the optimized coupling mathematical model. The embodiment of the present application can optimize the multi-physics field coupling of the co-electrolysis system and is suitable for complex working condition regulation.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of new energy technology, and in particular to a multi-physical field coupling optimization method for a co-electrolysis system and related products. Background Art

[0002] A co-electrolysis system is an electrochemical device that can simultaneously electrolyze two or more substances, such as a solid oxide electrolysis cell (SOEC) and a proton exchange membrane electrolysis cell (PEMEC). It usually involves the coupling of multiple physical fields of electricity, heat, fluid and force.

[0003] However, in the related technologies of multi-physical field coupling of co-electrolysis systems, the uniform flow channel design usually adopted will lead to local thermal stress concentration, and based on the single-field simplified modeling used, it is easy to independently optimize objectives such as efficiency, thermal stress, and pressure drop, resulting in the fragmentation of multi-objective optimization, which is difficult to apply to regulation under complex working conditions. Summary of the Invention

[0004] The embodiments of the present application provide a multi-physical field coupling optimization method and related products for a co-electrolysis system, which can optimize the multi-physical field coupling of the co-electrolysis system and is suitable for complex working condition regulation.

[0005] In one aspect, an embodiment of the present application provides a multi-physics field coupling optimization method for a co-electrolysis system, wherein the co-electrolysis system has a structural design of a fractal flow channel, the method comprising:

[0006] During the operation of the co-electrolysis system, a coupling mathematical model constructed based on a multi-physical field coupling relationship and a fractal porous medium permeability model for the fractal flow channel are obtained; the coupling mathematical model includes a micro-scale electrochemical kinetics calculation model, a meso-scale non-Darcy seepage simulation model, and a macro-scale thermal stress field calculation model, wherein the electrochemical kinetics calculation model is used to describe the three-phase boundary reaction in electrochemistry, the non-Darcy seepage simulation model is used to couple the electrochemical reaction mass source term of the electrochemical kinetics calculation model and the flow field pressure distribution of the fractal porous medium permeability model, and the thermal stress field calculation model is used to couple the convection term of the non-Darcy seepage simulation model and the mass source term of the electrochemical kinetics calculation model;

[0007] Based on the physical field parameters output by the electrochemical kinetics calculation model, the non-Darcy seepage simulation model, and the thermal stress field calculation model, a corrected control parameter of the co-electrolysis system is obtained; the corrected control parameter is used to feed back to the electrochemical kinetics calculation model and the non-Darcy seepage simulation model;

[0008] By adjusting the fractal parameters of the fractal porous media permeability model, an optimized permeability is obtained; the optimized permeability is provided to the non-Darcy seepage simulation model to update the flow field pressure distribution;

[0009] Based on the real-time updated physical field parameters of the electrochemical kinetics calculation model, the non-Darcy seepage simulation model and the thermal stress field calculation model, the coupled mathematical model is multi-objective optimized to obtain an optimized coupled mathematical model.

[0010] On the other hand, an embodiment of the present application provides a multi-physics field coupling optimization device for a co-electrolysis system, wherein the co-electrolysis system has a fractal flow channel structural design, and the device includes:

[0011] A model acquisition module is used to obtain a coupling mathematical model constructed based on a multi-physical field coupling relationship and a fractal porous medium permeability model for the fractal flow channel during the operation of the co-electrolysis system; the coupling mathematical model includes a microscale electrochemical kinetics calculation model, a mesoscale non-Darcy seepage simulation model, and a macroscale thermal stress field calculation model, wherein the electrochemical kinetics calculation model is used to describe the three-phase boundary reaction in electrochemistry, the non-Darcy seepage simulation model is used to couple the electrochemical reaction mass source term of the electrochemical kinetics calculation model and the flow field pressure distribution of the fractal porous medium permeability model, and the thermal stress field calculation model is used to couple the convection term of the non-Darcy seepage simulation model and the mass source term of the electrochemical kinetics calculation model;

[0012] a control parameter correction module, configured to obtain corrected control parameters of the co-electrolysis system based on the physical field parameters output by the electrochemical kinetics calculation model, the non-Darcy seepage simulation model, and the thermal stress field calculation model; wherein the corrected control parameters are fed back to the electrochemical kinetics calculation model and the non-Darcy seepage simulation model;

[0013] a permeability optimization module, configured to obtain an optimized permeability by adjusting the fractal parameters of the fractal porous media permeability model; and providing the optimized permeability to the non-Darcy seepage simulation model to update the flow field pressure distribution;

[0014] The mathematical model optimization module is used to perform multi-objective optimization on the coupled mathematical model based on the physical field parameters updated in real time by the electrochemical kinetics calculation model, the non-Darcy seepage simulation model and the thermal stress field calculation model to obtain an optimized coupled mathematical model.

[0015] On the other hand, an embodiment of the present application also provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, any one of the multi-physical field coupling optimization methods of the co-electrolysis system is implemented.

[0016] On the other hand, an embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, any one of the multi-physical field coupling optimization methods for the co-electrolysis system is implemented.

[0017] On the other hand, an embodiment of the present application further provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the multi-physics coupling optimization method for the co-electrolysis system described in the above aspects.

[0018] The multi-physics field coupling optimization method and related products of the co-electrolysis system provided in the embodiment of the present application, the coupling mathematical model constructed based on the multi-physics field coupling relationship includes a micro-scale electrochemical kinetics calculation model, a meso-scale non-Darcy seepage simulation model coupled with the electrochemical kinetics calculation model and the fractal porous medium permeability model, and a macro-scale thermal stress field calculation model coupled with the non-Darcy seepage simulation model and the electrochemical kinetics calculation model. The co-electrolysis system adopts a fractal flow channel structural design. During the operation of the co-electrolysis system, the output of the electrochemical kinetics calculation model, the non-Darcy seepage simulation model and the thermal stress field calculation model is used. The physical field parameters are used to obtain the corrected control parameters of the co-electrolysis system, and the optimized permeability is obtained by adjusting the fractal parameters of the fractal porous media permeability model. The corrected control parameters can be used to feed back to the electrochemical kinetics calculation model and the non-Darcy seepage simulation model, and the optimized permeability can be provided to the non-Darcy seepage simulation model to update the flow field pressure distribution, thereby realizing closed-loop feedback between the coupled mathematical models. Furthermore, based on the real-time updated physical field parameters of the electrochemical kinetics calculation model, the non-Darcy seepage simulation model, and the thermal stress field calculation model, the coupled mathematical model is multi-objective optimized to obtain the optimized coupled mathematical model. When the co-electrolysis system adopts a fractal flow channel structural design to improve the uniformity of the thermal field, a coupled mathematical model is established based on multi-scale coupling modeling and intelligent optimization algorithms by integrating the principles of electrochemical kinetics, heat transfer, fluid mechanics, and thermoelasticity. The multi-objective optimization of the coupled mathematical model is then used to achieve global optimization of the coupled mathematical model, which can optimize the multi-physical field coupling of the co-electrolysis system and make the optimized co-electrolysis system suitable for the regulation of complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flowchart of the steps of a multi-physics field coupling optimization method for a co-electrolysis system provided in an embodiment of the present application;

[0020] Figure 2 1 is a process diagram of the multi-physics field coupling optimization method according to an embodiment of the present application;

[0021] Figure 3 This is a structural block diagram of a multi-physics field coupling optimization device for a co-electrolysis system according to an embodiment of the present application;

[0022] Figure 4 This is a structural block diagram of an electronic device provided in an embodiment of the present application;

[0023] Figure 5 This is a structural block diagram of a computer-readable storage medium provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0025] Co-electrolysis systems usually involve the coupling of multiple physical fields such as electricity, heat, fluid and force. However, there is a contradiction between the efficiency and life of the current co-electrolysis system under the coupling of multiple physical fields such as electricity, heat, fluid and force. The multi-physical field coupling has the following limitations: (1) Single-field simplified modeling defects: In related technologies, linearized electrochemical models are used, ignoring the nonlinear coupling of concentration polarization and temperature gradient, resulting in a prediction error of more than 20% under actual working conditions. For example, experiments show that when the current density is >1 A / cm², the traditional Butler-Volmer equation does not take into account the local gas starvation effect (β i Correction item), the current distribution calculation deviation can reach 15% to 30%; (2) Failure of static boundary control: The PID (Proportional-Integral-Derivative) control algorithm is used in related technologies. The aforementioned control algorithm cannot adapt to the dynamic start and stop of the electrolyzer and load fluctuations. For example, measured data show that when the renewable energy power input fluctuates by ±30%, the temperature overshoot of the traditional method can reach 50K, which will accelerate the electrode sintering failure; (3) Dependence on structural design experience: Related technologies use a uniform flow channel design. The aforementioned design will lead to local thermal stress concentration. For example, micro-CT (Computed Tomography) Tomography (computed tomography) observations found that the probability of microcracks in conventional serpentine flow channels in high temperature areas (>800°C) is 4 times higher than that of fractal flow channels; (4) Multi-objective optimization fragmentation: Existing technologies optimize efficiency, thermal stress, pressure drop and other objectives independently, and fail to establish a collaborative mapping relationship of the Pareto frontier (ParetoFrontier refers to the set of all Pareto optimal solutions (non-dominated solutions) in a multi-objective optimization problem), which will lead to a system-level energy efficiency loss of 12% to 18%.

[0026] The embodiment of the present application is based on multi-scale coupling modeling and intelligent optimization algorithm. By integrating the principles of electrochemical dynamics, heat transfer, fluid mechanics and thermoelasticity, a coupling mathematical model is established, and an improved intelligent optimization algorithm is used to realize the global optimization of the coupling mathematical model. It can optimize the multi-physical field coupling of the co-electrolysis system, so that the optimized co-electrolysis system is suitable for the regulation of complex working conditions. Specifically, the co-electrolysis system adopts a fractal flow channel structure design. The fractal flow channel can optimize the mass distribution through the self-similar structure, reduce the flow resistance and reduce the pressure drop, thereby improving the thermal field uniformity and realizing the optimization of the flow channel structure. Among them, the tree-like fractal structure can enhance convective heat transfer, reduce the temperature gradient, and provide support for subsequent global optimization. When the co-electrolysis system adopts a fractal flow channel structure design to improve the thermal field uniformity, a coupling mathematical model is constructed based on the multi-physical field coupling relationship to avoid the defects of single-field simplified modeling, and the micro-scale electrochemical kinetics calculation model, the meso-scale non-Darcy seepage simulation model, the macro-scale thermal stress field calculation model and The fractal porous media permeability models of fractal flow channels are coupled with each other, and a collaborative mapping relationship of the Pareto front is established, and the coupled mathematical models are optimized for multiple objectives to avoid the phenomenon of multi-objective optimization fragmentation; and based on the physical field parameters output by the electrochemical kinetics calculation model, the non-Darcy seepage simulation model and the thermal stress field calculation model, the corrected control parameters of the co-electrolysis system are obtained. The corrected control parameters are used to feed back to the electrochemical kinetics calculation model and the non-Darcy seepage simulation model to achieve adaptive adjustment of the dynamic boundary, so as to correct control parameters such as voltage and flow in real time, which can be applicable to operating conditions such as gas concentration fluctuations and sudden temperature gradient changes.

[0027] Reference Figure 1 , shows a flowchart of the steps of a multi-physics field coupling optimization method for a co-electrolysis system provided in an embodiment of the present application, which may specifically include the following steps:

[0028] Step S101 : during the operation of the co-electrolysis system, a coupling mathematical model constructed based on a multi-physical field coupling relationship and a fractal porous medium permeability model for a fractal flow channel are obtained.

[0029] The coupled mathematical model constructed based on the multi-physical field coupling relationship can be a multi-scale dynamic coupling model, which can include a micro-scale electrochemical kinetics calculation model, a meso-scale non-Darcy seepage simulation model, and a macro-scale thermal stress field calculation model. Among them, the micro-scale can refer to the electrode pore scale, the meso-scale can refer to the flow channel unit scale, and the macro-scale can refer to the battery stack as a whole. The embodiments of the present application are not limited to this.

[0030] In some embodiments of the present application, the co-electrolysis system adopts a fractal flow channel structural design. The fractal flow channel can optimize mass distribution through self-similar structure, reduce flow resistance and reduce pressure drop, thereby improving thermal field uniformity and optimizing the flow channel structure. Among them, the tree-like fractal structure can enhance convective heat transfer, reduce temperature gradient, and provide support for subsequent global optimization.

[0031] Specifically, the mathematical models at various scales are coupled with each other, as well as with the fractal porous media permeability model for the fractal flow channel. Specifically, the mesoscale non-Darcy flow simulation model is coupled with the electrochemical kinetics calculation model and the fractal porous media permeability model, and the macroscale thermal stress field calculation model is coupled with the mesoscale non-Darcy flow simulation model and the microscale electrochemical kinetics calculation model. For example, the electrochemical reaction mass source term of the electrochemical kinetics calculation model and the flow field pressure distribution of the fractal porous media permeability model can be coupled to the non-Darcy flow simulation model, and the convection term of the non-Darcy flow simulation model and the mass source term of the electrochemical kinetics calculation model can be coupled to the thermal stress field calculation model.

[0032] The physical field parameters between the mutually coupled models can form a closed-loop control. For example, the physical field parameters output by the electrochemical kinetics calculation model can participate in the step of dynamic boundary adjustment, affecting the control parameters of the co-electrolysis system, forming a closed-loop optimization; the physical field parameters output by the thermal stress field calculation model can participate in the step of dynamic boundary adjustment, affecting the control parameters of the co-electrolysis system, forming a closed-loop optimization; the control parameters of the co-electrolysis system can be fed back to the electrochemical kinetics calculation model and the non-Darcy seepage simulation model, affecting the physical field parameters output by them, forming a closed-loop feedback; the permeability of the fractal porous media permeability model can be fed back to the non-Darcy seepage simulation model to update the flow field pressure distribution; the physical field parameters output by the thermal stress field calculation model can also participate in the step of multi-objective optimization, forming a closed-loop optimization; the physical field parameters of the coupled mathematical model after multi-objective optimization can be fed back to the model parameters of each model to complete the global iteration.

[0033] In practical applications, the coupled mathematical model can be applied during the operation of the co-electrolysis system.

[0034] Optionally, in the coupled mathematical model, the electrochemical kinetics calculation model can be used to describe the three-phase boundary reaction in electrochemistry; the non-Darcy seepage simulation model can couple the electrochemical reaction mass source term of the electrochemical kinetics calculation model and the flow field pressure distribution of the fractal porous media permeability model to reveal the influence of the gas concentration gradient on the flow velocity field, and provide convection term input for the subsequent thermal-mechanical coupling analysis performed by the thermal stress field calculation model; the thermal stress field calculation model can couple the convection term of the non-Darcy seepage simulation model and the mass source term of the electrochemical kinetics calculation model to realize the coupling of the three physical fields of electricity, heat and force, convert the heat generated by the current density into a temperature field, and then calculate the thermal stress distribution, providing key input parameters for flow channel structure optimization and multi-objective optimization.

[0035] Step S102 , obtaining corrected control parameters of the co-electrolysis system based on the physical field parameters output by the electrochemical kinetics calculation model, the non-Darcy seepage simulation model, and the thermal stress field calculation model.

[0036] In some embodiments of the present application, the physical field parameters output by the electrochemical kinetics calculation model can participate in the step of dynamic boundary adjustment, affect the control parameters of the co-electrolysis system, and form a closed-loop optimization; the physical field parameters output by the thermal stress field calculation model can participate in the step of dynamic boundary adjustment, affect the control parameters of the co-electrolysis system, and form a closed-loop optimization; the control parameters of the co-electrolysis system can be fed back to the electrochemical kinetics calculation model and the non-Darcy seepage simulation model, affect the physical field parameters output by them, and form a closed-loop feedback.

[0037] Specifically, based on the physical field parameters output by the electrochemical kinetics calculation model, the non-Darcy seepage simulation model and the thermal stress field calculation model, the corrected control parameters of the co-electrolysis system can be obtained. The corrected control parameters are used to feed back to the electrochemical kinetics calculation model and the non-Darcy seepage simulation model to achieve adaptive adjustment of the dynamic boundary, so as to correct the control parameters such as voltage and flow in real time, which can be applicable to changes in operating conditions such as gas concentration fluctuations and sudden changes in temperature gradients.

[0038] Optionally, the feedback mechanism formed by the closed-loop control can be expressed as "prediction-measurement-correction", which ensures that the co-electrolysis system maintains the balance of multi-physical field coupling during dynamic changes based on the feedback mechanism.

[0039] Optionally, the feedback mechanism can be implemented with the help of preset state-space equations and preset observation equations. The preset state-space equations are used to describe the system state of the co-electrolysis system, such as the dynamic process of temperature, pressure, concentration, stress, etc. evolving over time, including the control input of physical field parameters and the dynamic process of noise interference; the preset observation equations can map the system state to actual measurable physical quantities, such as infrared temperature measurement data, pressure sensor data, etc., to take into account the influence of measurement noise.

[0040] In some embodiments of the present application, a state vector can be first constructed based on the physical field parameters output by the electrochemical kinetics calculation model, the non-Darcy seepage simulation model and the thermal stress field calculation model; then, the state vector can be used as the initial condition of the preset state-space equation, and the predicted physical parameters of the co-electrolysis system at the next operating moment can be predicted by the preset state-space equation. At this time, the actual sensor data of the co-electrolysis system at the next operating moment can be obtained, and the predicted physical parameters of the co-electrolysis system at the next operating moment and the actual sensor data of the co-electrolysis system at the next operating moment are substituted into the preset observation equation, and then the control parameters of the co-electrolysis system are corrected in real time to obtain the corrected control parameters of the co-electrolysis system.

[0041] For example, in order to achieve noise suppression during the prediction process, the preset state space equation can adopt a UKF (Unscented Kalman Filter) filtering state equation to eliminate the interference of noise and observation noise in the dynamic change process. This embodiment of the present application is not limited to this.

[0042] Step S103 : obtaining optimized permeability by adjusting the fractal parameters of the fractal porous medium permeability model.

[0043] In some examples of the present application, the co-electrolysis system adopts a fractal flow channel structural design. The fractal flow channel can optimize mass distribution through self-similar structure, reduce flow resistance and reduce pressure drop, thereby improving thermal field uniformity and optimizing the flow channel structure.

[0044] The tree-like fractal structure can enhance convective heat transfer, reduce temperature gradients, and provide support for subsequent global optimization. The fractal laws of the fractal flow channel can be referenced to the fractal laws of biological vascular systems in nature, such as plant veins and animal blood vessels. The radius ratios of the fractal flow channel branches can be optimized using the principle of conservation of mass flow to ensure uniform distribution of the fluid in each level of the flow channel, reducing flow resistance and ensuring that the fluid distribution complies with the principle of minimum energy consumption, such as the optimal transport law in nature. This is not limited in the present embodiment of the application.

[0045] Specifically, the optimization of flow channel structure can be linked to the mass transfer characteristics of porous media through fractal geometry theory.

[0046] The specific geometric parameters of the fractal flow channel structure, such as the radius of each level and the bifurcation angle, can directly determine the fractal parameters of the fractal porous media permeability model, and thus affect the calculation of the permeability of the fractal porous media permeability model.

[0047] The non-Darcy flow simulation model can be coupled with a fractal porous media permeability model. The permeability of the fractal porous media permeability model can be fed back to the non-Darcy flow simulation model to update the flow field pressure distribution. To support global optimization of the coupled mathematical model, embodiments of the present application can adjust the fractal parameters of the fractal porous media permeability model to obtain an optimized permeability. This optimized permeability can then be fed back to the non-Darcy flow simulation model to update the flow field pressure distribution, forming a closed loop of multi-physics field coupled optimization.

[0048] Optionally, the fractal parameters of the fractal porous medium permeability model may be adjusted with the goal of minimizing a preset coupling objective function to obtain optimized permeability, which is not limited in the embodiments of the present application.

[0049] Step S104 , based on the real-time updated physical field parameters of the electrochemical kinetics calculation model, the non-Darcy seepage simulation model, and the thermal stress field calculation model, multi-objective optimization is performed on the coupled mathematical model to obtain an optimized coupled mathematical model.

[0050] In some examples of the present application, the physical field parameters output by the thermal stress field calculation model can participate in the multi-objective optimization steps to form a closed-loop optimization; the physical field parameters of the coupled mathematical model after multi-objective optimization can be fed back to the model parameters of each model to complete the global iteration.

[0051] Specifically, in order to resolve conflicts among multiple objectives such as electricity, heat, and force, for example, reducing the temperature gradient may aggravate the problem of uneven current distribution, a collaborative mapping relationship of the Pareto frontier can be established, and multi-objective optimization can be performed on the coupled mathematical model to obtain the Pareto optimal solution set, thereby avoiding the phenomenon of multi-objective optimization fragmentation.

[0052] Optionally, the collaborative mapping relationship can be implemented by constructing a multi-objective optimization function based on the real-time updated physical field parameters of the electrochemical kinetics calculation model, the non-Darcy flow simulation model, and the thermal stress field calculation model. In this case, the multi-objective optimization function can be solved to obtain a Pareto optimal solution set. A Pareto optimal solution set is a solution set in which one objective cannot be further optimized without sacrificing other objectives. Specifically, it satisfies the requirement that reducing the value of any objective function (such as temperature gradient, current error, or thermal stress) inevitably leads to a deterioration in the value of at least one other objective function.

[0053] In some embodiments of the present application, the Pareto optimal solution set obtained by the solution may include physical field parameters after multi-objective optimization, and the physical field parameters after multi-objective optimization may be fed back to the coupled mathematical model to obtain an optimized coupled mathematical model, thereby achieving global iteration of the coupled mathematical model. That is, the optimal equilibrium value of a global parameter may be reached through iteration of the Pareto optimal solution set, and then the relevant physical field parameters when the optimal equilibrium value is reached may be fed back to the aforementioned steps to modify each working condition, thereby achieving a new coupled mathematical model. In short, step S104 may be used to achieve optimal adjustment of each input parameter in this step under a certain complex working condition, and these input parameters are also the basic components of the model mentioned in the aforementioned steps.

[0054] It should be noted that the physical field parameters after multi-objective optimization may include but are not limited to the optimal flow channel radius, operating voltage range, etc. The physical field parameters after the aforementioned target optimization can be fed back to the aforementioned model, for example, fed back to the electrochemical kinetics calculation model to correct parameters such as the exchange current density and concentration correction index, and fed back to the flow channel structure optimization to update the flow channel geometric parameters. Through the closed-loop feedback mechanism, the coordinated optimization of the electrochemical-flow field-thermal stress field is achieved through the Pareto solution set, thereby maximizing the energy conversion efficiency while ensuring system stability.

[0055] In some embodiments of the present application, in order for those skilled in the art to further understand the multi-physics field coupling optimization method provided in the embodiments of the present application, Figure 2 The specific model formula is described as follows:

[0056] Reference Figure 2 , showing a process diagram of the multi-physics field coupling optimization method of an embodiment of the present application.

[0057] The coupled mathematical model constructed based on the multi-physical field coupling relationship can include a micro-scale electrochemical kinetics calculation model, a meso-scale non-Darcy percolation simulation model, and a macro-scale thermal stress field calculation model.

[0058] Step S201: The electrochemical kinetics calculation model performs micro-scale electrochemical kinetics calculations.

[0059] The electrochemical kinetics calculation model can be mainly used to describe the three-phase boundary reaction in electrochemistry. The three-phase boundary reaction refers to the interface area where the gas phase, liquid phase / ionic phase and solid phase contact each other in electrochemistry. This area is the active site where the electrochemical reaction occurs and can directly affect the device performance.

[0060] For example, the electrochemical kinetics calculation model may adopt the modified Butler-Volmer equation, and the specific formula may be as follows:

[0061]

[0062] in, is the local current density (unit: A / m 2 ), which serves as the output; is the exchange current density (unit: A / m 2 ), which can be used to indicate the equilibrium reaction rate, and this value can be obtained through experimental measurement; is the anode transfer coefficient, is the cathode transfer coefficient, which can be obtained through experimental calibration, such as polarization curve fitting; is the Faraday constant, the specific value can be 96485 C / mol, which is a physical constant; is the gas constant, the specific value can be 8.314J / (mol·K), which is a physical constant; is the temperature field (in K), which is used as an input quantity and its specific value can be determined based on the global temperature field; is the gas concentration (unit is mol / m 3 ), which as an input can be the concentration of gases such as H2O / CO2 / H2 based on the flow field model or experimental measurement; is the reference concentration (unit: mol / m 3 ), that is, the concentration under standard working conditions, which belongs to the theoretical definition; is a concentration correction index, which can be used to indicate the reaction level. The specific value can be obtained through experimental calibration or molecular dynamics simulation. i is used to indicate the gas component. The specific value depends on the electrolysis type. For example, for high-temperature electrolysis SOEC (Solid Oxide Electrolysis Cell), i=1 indicates H2O, and i=2 indicates CO2. For low-temperature electrolysis PEMEC (Proton Exchange Membrane Electrolysis Cell), i=1 indicates H2O, and i=2 indicates O2. is the overpotential (in V), which can be used to indicate the additional potential difference required to drive the reaction. As an input, it can be calculated from the electrode potential difference. The specific calculation formula can be shown as follows:

[0063]

[0064] in, is the equilibrium potential (calculated by Nernst equation); is the electric potential field input of the zinc electrode, specifically a negative value; is the electric potential field input on the solution side, which takes a positive value.

[0065] In step S201, the input of the electrochemical kinetics calculation model is the initial gas concentration and temperature field The initial value can be obtained by measurement; the output of the electrochemical kinetics calculation model is the local current density . Optionally, local current density It can be used to calculate the reaction rate. The calculated reaction rate can be used as the electrochemical reaction mass source term to provide the non-Darcy flow simulation model in step S202 for non-Darcy flow simulation; local current density It can also be provided as a mass source term to the thermal stress field calculation model in step S203, so that the thermal stress field calculation model can calculate the Joule heat; the local current density It can also be used as a boundary condition to participate in the adaptive adjustment of the dynamic boundary in step S204 to form a closed-loop optimization.

[0066] Step S202: The non-Darcy seepage simulation model performs a mesoscopic-scale non-Darcy seepage simulation.

[0067] The non-Darcy seepage simulation model can couple the electrochemical reaction mass source term of the electrochemical kinetics calculation model and the flow field pressure distribution of the fractal porous media permeability model, revealing the influence of the gas concentration gradient on the flow velocity field, and providing convection term input for the subsequent thermal-mechanical coupling analysis performed by the thermal stress field calculation model.

[0068] Optionally, the electrochemical reaction mass source term of the electrochemical kinetics calculation model may be a reaction rate. The reaction rate may be calculated based on the local current density output by the electrochemical kinetics calculation model. For example, the specific formula may be as follows:

[0069]

[0070] in, is the reaction rate; is the local current density; is the reaction area; is the number of electron transfers, taking CO2 electrolysis as an example, =2; is the Faraday constant, the specific value of which can be 96485 C / mol, which is a physical constant.

[0071] The flow field pressure distribution of the fractal porous media permeability model can be based on the permeability output by the fractal porous media permeability model in step S205. It should be noted that the multi-physics field coupling process is an overall closed-loop feedback optimization. The permeability output in the following seepage equation can be the permeability obtained in the previous round of optimization of the fractal porous media permeability model, and this embodiment of the present application is not limited to this.

[0072] For example, the seepage equation of the non-Darcy seepage simulation model can be as follows:

[0073]

[0074]

[0075] in, is the permeability (unit: m 2 ), which is the effective permeability, can be used to indicate the effective permeability tensor, and the specific value can be calculated based on the geometric parameters of the fractal flow channel; is the dynamic viscosity of the fluid (in Pa·s), the specific value can be obtained through experimental measurement or temperature table lookup; is the fluid pressure field (unit: Pa), and its specific value is obtained by solving the variable output; is the pressure gradient, and its specific value can be calculated based on the fluid pressure field; The reaction rate calculated by the microscale model (in kg / (m³·s)) can be used as the electrochemical reaction mass source term to indicate the distribution of the mass source term at each reaction point. The specific value can be calculated based on step S201; is the total source term input of the mesoscopic flow field, which represents the sum of the mass source terms generated by all microscopic electrochemical reaction units in the mesoscopic scale model. Specifically, the total source term input of the mesoscopic flow field can be obtained by integrating the mass source terms of all microscopic reaction units; Indicates the divergence operation of the vector field in the brackets; is the velocity field component, where the subscript d represents the spatial direction of the velocity field component, that is, the dimensional direction of the spatial coordinate system usually corresponds to the xyz direction in the rectangular coordinate system. For example, when the subscript d=1, it corresponds to the x-axis direction, when the subscript d=2, it corresponds to the y-axis direction, and when the subscript d=3, it corresponds to the z-axis direction.

[0076] It should be noted that the above seepage equation describes the non-Darcy seepage behavior of fluid flow in porous media, which is different from the traditional Darcy law. In the embodiment of this application, the effective permeability is introduced. , effective penetration rate Specifically, it is calculated based on step S205, which can integrate the influence of flow channel geometry (such as fractal structure) and pore scale effect on flow; the total source term input in the seepage equation is Based on mass source terms Get, and the mass source term It represents the change in gas mass produced by the electrode reaction. The above percolation equation can couple the microscopic electrochemical process with the mesoscopic flow.

[0077] In step S202, the input of the non-Darcy seepage simulation model is the local current density output from step S201. Calculated reaction rate and the permeability output by the fractal porous media permeability model in step S205 ; The output of the non-Darcy flow simulation model is the pressure gradient and velocity field components . Optional, velocity field component It can be provided as a convection term to the thermal stress field calculation model in step S203 for thermal-mechanical coupling solution.

[0078] Step S203: The thermal stress field calculation model performs a macro-scale thermal-mechanical coupling solution, which is specifically performed as a thermal stress field calculation.

[0079] The thermal stress field calculation model can couple the convection term of the non-Darcy seepage simulation model and the mass source term of the electrochemical kinetics calculation model to realize the coupling of the three physical fields of electricity, heat and force, convert the heat generated by the current density into a temperature field, and then calculate the thermal stress distribution, providing key input parameters for flow channel structure optimization and multi-objective optimization.

[0080] Optionally, the convection term of the non-Darcy flow simulation model may be the flow rate output by the non-Darcy flow simulation model, and the mass source term of the electrochemical kinetics calculation model may be the Joule heat source term generated by the current density of the electrochemical kinetics calculation model. For example, the specific formula may be as follows:

[0081]

[0082] in, is the Joule heat source term, is the local current density, is the conductivity.

[0083] The first model in the thermal stress field calculation model can be used to indicate the coupling effect of fluid convection and heat conduction, which can be specifically reflected by the heat conduction-convection equation. For example, the first model can be shown as follows:

[0084]

[0085] in, is the velocity field component, where the subscript d represents the spatial direction of the velocity field component, that is, the dimensional direction of the spatial coordinate system usually corresponds to the xyz direction in the rectangular coordinate system. For example, when the subscript d=1, it corresponds to the x-axis direction, when the subscript d=2, it corresponds to the y-axis direction, and when the subscript d=3, it corresponds to the z-axis direction. is the temperature field (in K), the specific value can be obtained through global iteration input or initial conditions; is the equivalent thermal conductivity (unit: W / (m·K)), which includes material anisotropy. The specific value can be obtained from the material database or experimental measurement; is the component of the spatial coordinate, which represents the component of the spatial coordinate in the three-dimensional rectangular coordinate system, where subscript d=1 corresponds to the x-axis direction, subscript d=2 corresponds to the y-axis direction, and subscript d=3 corresponds to the z-axis direction; is the Joule heat source term (unit: W / m 3 ), which can be calculated based on the current density output by the electrochemical kinetics calculation model in step S201.

[0086] It should be noted that in the above heat conduction-convection equation, the left side of the equation is the convection effect of the fluid on the temperature field, and the right side is the balance between heat conduction and the heat source.

[0087] The second model in the thermal stress field calculation model can be used to characterize the thermal stress distribution caused by the temperature gradient. Specifically, it can be represented by the generalized thermoelastic equation. For example, the second model can be as follows:

[0088]

[0089] in, Indicates the divergence operation of thermal stress distribution; is the thermal stress distribution (in Pa), which indicates the stress tensor as an output quantity; is the material density (in kg / m³), the specific value can be obtained from the material parameter library; is the body force (unit: N / kg), like gravity, which is a physical constant; is the stiffness tensor (unit: Pa), the specific value of which can be obtained through material mechanics testing or microscopic model calculation; is the thermal expansion coefficient tensor (unit: 1 / K), the specific value of which can be obtained through experimental calibration or thermal expansion model; is the temperature gradient, as the output.

[0090] It should be noted that in the above generalized thermoelastic equation, the thermal expansion coefficient can be The temperature field is coupled with the stress field. The first term on the right side of the equation is the external force, and the second term is the thermal strain contribution.

[0091] In step S203, the input of the thermal stress field calculation model is the local current density output in step S201. , temperature field , Joule heat and the flow rate output in step S202 ;The output of the thermal stress field calculation model is the temperature gradient and thermal stress distribution . Optional, temperature gradient It can be input to step S204 for dynamic boundary adjustment, and can also be input to step S206 for multi-objective optimization of multi-objective optimization function; thermal stress distribution This can be input into step S206 to perform stress constraint analysis on the multi-objective optimization function.

[0092] Step S204: Adaptive adjustment of dynamic boundaries.

[0093] Specifically, based on the physical field parameters output from step S201 to step S202, the corrected control parameters of the co-electrolysis system can be obtained. The corrected control parameters are used to feed back to the electrochemical kinetics calculation model in step S201 and the non-Darcy seepage simulation model in step S202 to achieve adaptive adjustment of the dynamic boundary, so as to correct the control parameters such as voltage and flow in real time, which can be applicable to changes in operating conditions such as gas concentration fluctuations and sudden changes in temperature gradients.

[0094] Optionally, the feedback mechanism formed by closed-loop control can be expressed as "prediction-measurement-correction," ensuring that the co-electrolysis system maintains a balanced multi-physics coupling during dynamic changes. This feedback mechanism can be implemented using pre-set state-space equations and pre-set observation equations.

[0095] For example, in order to achieve noise suppression during the prediction process, the preset state space equation may adopt the UKF filtering state equation, and the specific formula may be as follows:

[0096]

[0097] in, is the state vector of the co-electrolysis system, , is the temperature field, is the fluid pressure field, is the gas concentration, i is used to indicate the gas composition. For solid oxide electrolysis cells with high temperature electrolysis, i=1 indicates H2O, and i=2 indicates CO2; for proton exchange membrane electrolysis cells with low temperature electrolysis, i=1 indicates H2O, and i=2 indicates O2; is the thermal stress distribution, T represents the linear transformation matrix, and the state vector As the initial condition of the UKF filter state equation, the system state at the next moment can be predicted by the above preset state space equation ; t is the operating time of the co-electrolysis system; is the state transfer matrix, which can be obtained through experimental data or offline simulation; The input control matrix can be obtained through experimental data or offline simulation; is the control parameter, which can be a control input, such as voltage (in V) / flow (in m³ / s), etc., as the output; is the process noise distribution matrix, which can be obtained through experimental data or offline simulation; is the process noise vector, which can be obtained by assuming Gaussian white noise.

[0098] For example, the preset observation equation can be as follows:

[0099]

[0100] in, is the observation vector of the co-electrolysis system at the k-th operating time, which can be measured by sensors, such as temperature, pressure, etc. is the observation matrix, which can be obtained through experimental data or offline simulation; is the predicted state vector of the co-electrolysis system at the k-th operating time, k is the current operating time of the co-electrolysis system, and k-1 is the previous operating time of the co-electrolysis system; The observation noise vector can be obtained through the sensor error model. The observation noise vector is one of the reasons for the residual error. The estimation of the observation noise vector can be further adjusted by analyzing the residual error. The residual error can be compared with the actual sensor data to calculate the residual error. to correct the state estimate.

[0101] In step S204, the input of the dynamic boundary adaptive adjustment is the state vector x, and the output is the modified control parameter , including the corrected voltage / flow. Optionally, the corrected voltage can be input into the Butler-Volmer equation in step S201, and the corrected flow can be input into the seepage equation in step S202, forming a closed-loop feedback.

[0102] Step S205: Optimizing the flow channel structure. Specifically, a fractal flow channel structure design is adopted to optimize mass distribution through self-similar structure, reduce flow resistance and pressure drop, thereby improving thermal field uniformity and optimizing the flow channel structure.

[0103] Optionally, with the goal of minimizing a preset coupling objective function, the fractal parameters of the fractal porous media permeability model are adjusted to obtain an optimized permeability; the optimized permeability can be provided to the non-Darcy seepage simulation model in step S202 to update the flow field pressure distribution, forming a closed loop of multi-physical field coupling optimization.

[0104] For example, the fractal law of the fractal flow channel can be expressed as Murray's fractal law, which can be specifically shown as follows:

[0105]

[0106] in, is the radius of the qth-level fractal flow channel (in m), is the mth branch radius of the q+1th branch channel (in meters).

[0107] Specifically, the optimization of flow channel structure can be linked to the mass transfer characteristics of porous media through fractal geometry theory. The specific geometric parameters of the fractal flow channel structure, such as the radius of each level , bifurcation angle, etc., can directly determine the fractal parameters of the fractal porous media permeability model, and thus affect the calculation of permeability by the fractal porous media permeability model.

[0108] The preset coupling objective function can be reflected by the flow-heat coupling objective function. The specific formula can be shown as follows:

[0109]

[0110]

[0111] in, and is the weight coefficient, dimensionless, and can be set according to system requirements; is the maximum temperature gradient in the flow channel (in K); is the dynamic viscosity of the fluid; is the velocity field component; is the permeability; is the total pressure drop in the flow channel (in Pa), and is related to the permeability Strong correlation, specifically Darcy's law ,in is the dynamic viscosity of the fluid; l is the integral path along the seepage, specifically refers to the infinitesimal displacement vector along the seepage path, and dl is the displacement vector along the seepage path.

[0112] Optionally, fractal parameters may include porosity, tortuosity, fractal dimension, and flow channel radius classification of the fractal flow channel. The flow channel radius classification of the fractal flow channel can be used to determine the maximum pore radius and porosity; the fractal dimension can be used to describe the spatial filling complexity of the flow channel or porous media structure, which can be used to describe the geometric characteristics of the fractal flow channel, such as pore distribution and tortuosity.

[0113] For example, the fractal porous media permeability model can be as follows:

[0114]

[0115] in, is the permeability; is the porosity, which can be determined by the fractal flow channel radius classification, e.g. ; is the maximum pore radius; is the tortuosity, when the number of forks in the fractal network is =2 when the tortuosity is minimized; is the fractal dimension, which can be calculated by box dimension method or image analysis.

[0116] For example, combining fractal geometry with seepage mechanics theory, porosity The specific calculation formula can be shown as follows:

[0117]

[0118] in, is the radius of the qth fractal flow channel, which can be specifically determined by Murray's law Sure; is the length of the qth stage flow channel, which is usually proportional to the radius: ; is the unit cell cross-sectional area, which can be determined by the flow channel arrangement. The value of q is 1, 2, ..., N, where N is the maximum level of the fractal flow channel radius classification; is the flow channel height, which can be used to reflect the proportion of effective flow space in the flow channel and directly affect the permeability , which is a design parameter; π is the pi.

[0119] Maximum pore radius r max The calculation formula can be shown as follows:

[0120] Among them, the radius of the largest level in the fractal flow channel can be directly taken (Main channel radius), fractal classification If the flow channel is a tree-like fractal, With fractal initial conditions Related, specifically:

[0121] in, is the total flow, is the total pressure drop in the flow channel, is the dynamic viscosity of the fluid, π is the circumference of the circle, is the radius of the qth-level fractal flow channel, is the length of the qth level flow channel, and N is the maximum level of fractal flow channel radius classification.

[0122] The calculation formula of tortuosity τ can be shown as follows:

[0123] in, is the actual path length of the fluid, which can be obtained by weighting the total branch length of the fractal flow channel; is the straight-line distance of the flow channel, specifically the unit cell size; is the fractal dimension, which can be used to characterize the degree of curvature of the fluid path. The fractal structure can significantly reduce τ through bifurcation optimization (such as tree fractal τ≈1.2~1.5).

[0124] As an example, the calculation method of fractal dimension D can be obtained by using the box dimension method, that is, through experiments / image analysis. The specific calculation formula can be shown as follows:

[0125] in, The number of boxes with side length ε required to cover the flow path image.

[0126] As another example, the fractal dimension D can be calculated based on a theoretical formula of fractal generation rules. For example, for a tree-like fractal flow channel, the specific calculation formula can be as follows:

[0127] in, is the number of forks, for example, when there is a two-fork ; is the flow channel radius contraction ratio, specifically, .

[0128] It should be noted that the number of bifurcations of the fractal flow channel is , flow channel radius classification , fractal dimension Together they determine the pore structure, which in turn affects For example, the fractal dimension The higher it is, the better the pore connectivity is. The larger the porosity Grading with fractal radius The dependence relationship can be verified by 3D reconstruction of pores through CT scanning; the tortuosity τ and fractal dimension The dependence relationship can be verified by microfluidic particle tracking experiments; fractal dimension and the number of forks and shrinkage ratio The dependency relationship can be verified specifically through image processing algorithms (such as the MATLAB fractal toolbox), and the embodiments of the present application are not limited to this.

[0129] In step S205, the input of the flow channel structure optimization is the velocity field component output in step S202. and the temperature gradient output in step S203 ; The output of the channel structure optimization is the optimized channel radius and optimized permeability .Optional, optimized permeability It can be provided to the non-Darcy seepage simulation model in step S202 to update the flow field pressure distribution.

[0130] Step S206: Multi-objective collaborative optimization. Specifically, a collaborative mapping relationship of the Pareto frontier can be established, and multi-objective optimization can be performed on the coupled mathematical model to obtain the Pareto optimal solution set, thereby avoiding the phenomenon of multi-objective optimization fragmentation.

[0131] Optionally, a multi-objective optimization function may be constructed and solved to obtain a Pareto optimal solution set.

[0132] For example, the multi-objective optimization function may be a Pareto frontier objective function, and the specific formula may be as follows:

[0133]

[0134] in, is the integral of the square of the temperature gradient (unit is ), which can be used to characterize the uniformity of temperature distribution, can be specifically calculated based on the temperature field gradient output in step S203; is the temperature field gradient (unit is ), specifically the calculation result of the heat conduction-convection equation in step S203; is the battery stack volume; is the total deviation between the measured current and the current predicted by the model (unit: ), which can be used to characterize the accuracy of the electrochemical reaction, can be specifically obtained based on the comparison between the experimental measurement and the model current output in step S201; The current measured for the experiment; is the model current; is the maximum von Mises thermal stress (in Pa), which can be used to characterize the safety of the mechanical structure and can be calculated based on the thermal stress distribution output in step 203; is the von Mises stress (unit: Pa), which can be used to synthesize the equivalent strength index of normal stress and shear stress. Specifically, it is the calculation result of the generalized thermoelastic equation in step S203, that is, the thermal stress distribution .

[0135] In the above multi-objective optimization function, for the square integral of the temperature gradient The calculation of the temperature field gradient in step S203 can be used as the input. , specifically the volume of the battery stack After integration, the discretized expression can be shown as follows:

[0136]

[0137] in, is the number of finite element mesh elements, is the unit volume.

[0138] The total deviation between the measured current and the model predicted current The input can be the current measured experimentally. and model current , model current It can be calculated based on step S201.

[0139] For example, the model current It is a theoretical prediction value obtained by coupling the microscopic electrochemical reaction kinetics with the macroscopic transport process. The core computing logic model can be shown as follows:

[0140] First, the microscopic current density calculation can be performed, which can be specifically expressed as quantifying the local current density from step S201 based on the modified Butler-Volmer equation. Then, the mesoscopic current integration can be performed, which is specifically expressed as the local current density Active area of ​​the electrode Integrate the above to get the single channel current , for example, the specific formula can be as follows:

[0141] Among them, the area correction can be expressed as taking into account the electrode porosity and three-phase boundary length , effective area ( is the thickness of the reaction layer).

[0142] After performing the mesoscopic current integration, the macroscopic stack current synthesis can be performed. Specifically, it can be expressed as summarizing all channel currents according to the series-parallel structure of the electrolytic stack. For example, the specific formula can be as follows:

[0143]

[0144] in, is the number of units in series, which can be used to increase the voltage. The number of parallel channels, mainly used to increase current; It represents the current output value of the pth parallel flow channel (or the pth independent reaction unit) in the electrolytic stack, reflecting the intensity of the local electrochemical reaction.

[0145] After summing up the currents of all channels, the absolute deviation can be calculated point by point and accumulated to obtain the total deviation between the measured current and the model predicted current. :

[0146]

[0147] in, It represents the experimentally measured current of the pth parallel flow channel (or the pth independent reaction unit) in the electrolytic stack; It represents the model current of the pth parallel flow channel (or the pth independent reaction unit) in the electrolytic stack.

[0148] For the maximum von Mises thermal stress The calculation of the thermal stress distribution in step S203 can be used as input. , which can be used as the von Mises stress field participate Specifically, it can traverse the von Mises thermal stress of all nodes or elements. , at this time we can take the maximum value to get the maximum von Mises thermal stress , the specific formula can be shown as follows:

[0149]

[0150] In step S206, the input of multi-objective collaborative optimization is all physical field parameters, including but not limited to 、 、 The output of multi-objective collaborative optimization is the Pareto optimal solution set. The f1, f2, and f3 calculated by the Pareto frontier objective function can reach the optimal equilibrium value of a global parameter, which can be obtained by integrating the square of the global temperature gradient. , minimize the unevenness of temperature distribution and prevent local overheating; ensure the accuracy of the electrochemical model by minimizing the cumulative error f2 between the measured current and the model predicted current; and ensure the accuracy of the electrochemical model by limiting the maximum thermal stress value. , to avoid material failure due to stress concentration.

[0151] Optionally, the relevant physical field parameters when the optimal equilibrium value is reached can be fed back to the aforementioned steps to modify each working condition and complete the global iteration to achieve a new coupled mathematical model. Feedback to the microscopic calculation in step S201 to correct the electrode reaction rate and optimize the local current density and gas consumption ; The updated Feedback to the seepage simulation in step S202 to pass the fractal flow channel radius Recalculate permeability, optimize flow field distribution and pressure drop ; Set the voltage ,flow Feedback to the boundary adjustment in step S204 to dynamically adjust the input control amount , respond to working condition fluctuations (such as sudden temperature changes) in real time; Feedback to the flow channel structure optimization in step S205 to iteratively update the fractal flow channel topology and reduce the temperature gradient and heat stress .

[0152] In the embodiment of the present application, through a closed-loop feedback mechanism, the coordinated optimization of the electrochemical-flow field-thermal stress field is achieved through the Pareto solution set, maximizing the energy conversion efficiency while ensuring system stability.

[0153] The multi-physics field coupling optimization in the embodiment of the present application can solve the electric-thermal-mechanical multi-field coupling problem of the co-electrolysis system through the physical field parameter transmission and feedback closed loop. Compared with the traditional single-objective optimization method, the system efficiency is improved. The complete optimization process in the embodiment of the present application can realize the efficient and stable operation of the co-electrolysis system under complex working conditions. Its core role is reflected in: (1) Improvement of multi-field coupling performance: Through multi-scale dynamic modeling and fractal flow channel design, the temperature gradient (41%) and thermal stress peak (33%) are significantly reduced, and the electrode degradation problem caused by uneven gas concentration distribution is reduced, and the overall efficiency of the system is improved. Increased by 19%; (2) Optimization of dynamic response capability: The adaptive boundary algorithm based on the improved UKF filter shortens the operating condition response time to 10 seconds, overcoming the lag of the traditional static model and adapting to the fluctuating renewable energy input demand; (3) Enhanced engineering application value: The integrated digital twin platform realizes real-time electrical-thermal-mechanical parameter monitoring and multi-objective collaborative optimization, reducing operation and maintenance costs while extending the life of the electrolyzer. In practical applications, the solution provided in the embodiment of this application can improve the industrial feasibility of high-temperature co-electrolysis hydrogen / syngas production and provide high-robustness coupling technology support for renewable energy storage and chemical production. The embodiment of this application is not limited to this.

[0154] It should be noted that the coupled mathematical model obtained by the multi-physics field coupling optimization provided in the embodiment of the present application can be applied to a variety of scenarios. As an example, the coupled mathematical model can be applied to real-time working condition monitoring and dynamic adjustment. Its application process can be manifested as follows: during the operation of the co-electrolysis system, the coupled mathematical model can collect temperature field, current density, gas concentration and other data in real time through the embedded sensor network, and input it into the digital twin platform. The digital twin platform can automatically analyze the current system's electric-thermal-fluid-force state based on the preset multi-physics field coupling relationship. For example, when it is detected that the local temperature exceeds the threshold, the coupled mathematical model can trigger the dynamic boundary control algorithm to adjust the inlet gas flow or electrolysis voltage to avoid thermal runaway. This process is completed with a response speed of seconds, which can ensure that the co-electrolysis system is under fluctuating power input (such as wind and solar power generation). ) under stable operation; as another example, the coupled mathematical model can also be applied to fault diagnosis and life prediction. Its application process can be manifested as the coupled mathematical model identifies potential fault modes by comparing the deviation between real-time data and theoretical prediction values. If the current density in a certain area is continuously lower than the model calculated value, it may indicate electrode pore blockage or electrolyte stratification. At this time, historical data and machine learning can be combined. The model can quantify the material degradation rate, predict the remaining life of key components (such as electrodes and sealing layers), and plan maintenance cycles in advance; as another example, the coupled mathematical model can also be used as a reference for design parameter optimization. Its application process is manifested as in the new product development stage, the coupled model can be used for virtual testing of different flow channel structures The combined effect of structure, electrode material or operating parameters can be quickly screened out through batch simulation, with a pressure drop of less than 5kPa and uniform thermal stress, thereby reducing the number of physical prototype trials; as another example, the coupled mathematical model can also be applied to renewable energy coupled hydrogen production systems. Specifically, when the wind and solar power generation power fluctuations cause the electrolyzer input voltage to change frequently, the coupled mathematical model can be called to coordinate the current density and temperature field distribution in real time. For example, when the offshore wind power supporting hydrogen production project encounters typhoon weather, the coupled mathematical model can automatically switch the operating mode to a low-load insulation state, thereby avoiding thermal stress cracks caused by sudden shutdown; as another example, the coupled mathematical model can also be applied to exhaust field Specifically, in the co-electrolysis of power plant tail gas (containing CO2) to produce synthesis gas, fluctuations in the feed gas composition (such as a sudden drop in CO2 concentration from 30% to 15%) will significantly affect the reaction equilibrium. In this case, the coupled mathematical model can dynamically adjust the H2O / CO2 feed ratio and electrolysis temperature to maintain the target H2 / CO output ratio, thereby ensuring the stability of the feedstock for downstream Fischer-Tropsch synthesis. As another example, the coupled mathematical model can also be applied to extreme environment adaptability verification. Specifically, in order to simulate the impact of low gravity and high radiation environments on multi-field coupling, the coupled mathematical model can predict the electrolysis efficiency attenuation curve under extreme conditions by introducing a gravity acceleration correction term and a material radiation damage coefficient to guide protection design.As another example, coupled mathematical models can also be applied to standard compliance testing. Specifically, when a newly enacted energy efficiency standard requires that the carbon footprint of an electrolysis system over its entire lifecycle be below a certain threshold, the coupled mathematical model can be used to quantify the energy consumption and emissions of different operating strategies and generate a compliance report. This embodiment of the present application does not limit other application scenarios of the coupled mathematical model.

[0155] In the embodiment of the present application, based on multi-scale coupling modeling and intelligent optimization algorithm, a coupling mathematical model is established by integrating the principles of electrochemical dynamics, heat transfer, fluid mechanics and thermoelasticity, and an improved intelligent optimization algorithm is used to realize the global optimization of the coupling mathematical model, which can optimize the multi-physical field coupling of the co-electrolysis system, so that the optimized co-electrolysis system is suitable for the regulation of complex working conditions. Specifically, the co-electrolysis system adopts a fractal flow channel structural design. The fractal flow channel can optimize the mass distribution through the self-similar structure, reduce the flow resistance and reduce the pressure drop, thereby improving the thermal field uniformity and realizing the optimization of the flow channel structure. Among them, the tree-like fractal structure can enhance convective heat transfer, reduce the temperature gradient, and provide support for subsequent global optimization. When the co-electrolysis system adopts a fractal flow channel structural design to improve the thermal field uniformity, a coupling mathematical model is constructed based on the multi-physical field coupling relationship to avoid the defects of single-field simplified modeling, and the micro-scale electrochemical kinetics calculation model, the meso-scale non-Darcy seepage simulation model, the macro-scale thermal stress field calculation model and The fractal porous media permeability models of fractal flow channels are coupled with each other, and a collaborative mapping relationship of the Pareto front is established, and the coupled mathematical models are optimized for multiple objectives to avoid the phenomenon of multi-objective optimization fragmentation; and based on the physical field parameters output by the electrochemical kinetics calculation model, the non-Darcy seepage simulation model and the thermal stress field calculation model, the corrected control parameters of the co-electrolysis system are obtained. The corrected control parameters are used to feed back to the electrochemical kinetics calculation model and the non-Darcy seepage simulation model to achieve adaptive adjustment of the dynamic boundary, so as to correct control parameters such as voltage and flow in real time, which can be applicable to operating conditions such as gas concentration fluctuations and sudden temperature gradient changes.

[0156] It should be noted that for the method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present application are not limited by the order of the actions described, because according to the embodiments of the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present application.

[0157] Reference Figure 3, shows a structural block diagram of a multi-physics field coupling optimization device for a co-electrolysis system provided in an embodiment of the present application. The co-electrolysis system has a fractal flow channel structural design and may specifically include the following modules:

[0158] A model acquisition module 301 is used to acquire a coupled mathematical model constructed based on a multi-physical field coupling relationship and a fractal porous media permeability model for a fractal flow channel during operation of the co-electrolysis system; the coupled mathematical model includes a microscale electrochemical kinetics calculation model, a mesoscale non-Darcy seepage simulation model, and a macroscale thermal stress field calculation model, wherein the electrochemical kinetics calculation model is used to describe the three-phase boundary reaction in electrochemistry, the non-Darcy seepage simulation model is used to couple the electrochemical reaction mass source term of the electrochemical kinetics calculation model with the flow field pressure distribution of the fractal porous media permeability model, and the thermal stress field calculation model is used to couple the convection term of the non-Darcy seepage simulation model with the mass source term of the electrochemical kinetics calculation model;

[0159] A control parameter correction module 302 is configured to obtain corrected control parameters for the co-electrolysis system based on the physical field parameters output by the electrochemical kinetics calculation model, the non-Darcy flow simulation model, and the thermal stress field calculation model; the corrected control parameters are fed back to the electrochemical kinetics calculation model and the non-Darcy flow simulation model;

[0160] The permeability optimization module 303 is used to obtain an optimized permeability by adjusting the fractal parameters of the fractal porous media permeability model; the optimized permeability is provided to the non-Darcy flow simulation model to update the flow field pressure distribution;

[0161] The mathematical model optimization module 304 is used to perform multi-objective optimization on the coupled mathematical model based on the real-time updated physical field parameters of the electrochemical kinetics calculation model, the non-Darcy seepage simulation model and the thermal stress field calculation model to obtain an optimized coupled mathematical model.

[0162] In some embodiments of the present application, the control parameter correction module 302 may include the following submodules:

[0163] The control parameter correction submodule is used to construct a state vector based on the physical field parameters output by the electrochemical kinetics calculation model, the non-Darcy seepage simulation model and the thermal stress field calculation model; use the state vector as the initial condition of the preset state space equation, and predict the predicted physical parameters of the co-electrolysis system at the next operating moment through the preset state space equation; obtain the actual sensor data of the co-electrolysis system at the next operating moment; substitute the predicted physical parameters of the co-electrolysis system at the next operating moment and the actual sensor data of the co-electrolysis system at the next operating moment into the preset observation equation, correct the control parameters of the co-electrolysis system in real time, and obtain the corrected control parameters of the co-electrolysis system.

[0164] In some embodiments of the present application, the permeability optimization module 303 may include the following submodules:

[0165] The permeability optimization submodule is used to adjust the fractal parameters of the fractal porous media permeability model with the goal of minimizing the preset coupling objective function to obtain the optimized permeability.

[0166] In some embodiments of the present application, the mathematical model optimization module 304 may include the following submodules:

[0167] The mathematical model optimization submodule is used to construct a multi-objective optimization function based on the physical field parameters updated in real time by the electrochemical kinetics calculation model, the non-Darcy seepage simulation model, and the thermal stress field calculation model; solve the multi-objective optimization function to obtain the Pareto optimal solution set; the Pareto optimal solution set includes the physical field parameters after multi-objective optimization; the physical field parameters after multi-objective optimization are fed back to the coupled mathematical model to obtain the optimized coupled mathematical model; the optimized coupled mathematical model is the coupled mathematical model after global iteration.

[0168] In the embodiment of the present application, the coupling mathematical model constructed based on the multi-physical field coupling relationship includes a micro-scale electrochemical kinetics calculation model, a mesoscopic non-Darcy seepage simulation model coupled with the electrochemical kinetics calculation model and the fractal porous medium permeability model, and a macro-scale thermal stress field calculation model coupled with the non-Darcy seepage simulation model and the electrochemical kinetics calculation model. The co-electrolysis system adopts a fractal flow channel structural design. During the operation of the co-electrolysis system, the physical field parameters outputted by the electrochemical kinetics calculation model, the non-Darcy seepage simulation model and the thermal stress field calculation model are obtained. The system corrects the control parameters and obtains the optimized permeability by adjusting the fractal parameters of the fractal porous media permeability model. The corrected control parameters can be used to feed back to the electrochemical kinetics calculation model and the non-Darcy seepage simulation model. The optimized permeability can be provided to the non-Darcy seepage simulation model to update the flow field pressure distribution, thus realizing closed-loop feedback between the coupled mathematical models. Furthermore, based on the real-time updated physical field parameters of the electrochemical kinetics calculation model, the non-Darcy seepage simulation model, and the thermal stress field calculation model, the coupled mathematical model is multi-objective optimized to obtain the optimized coupled mathematical model. In the case where the co-electrolysis system adopts a fractal flow channel structural design to improve the uniformity of the thermal field, a coupled mathematical model is established based on multi-scale coupling modeling and intelligent optimization algorithms by integrating the principles of electrochemical kinetics, heat transfer, fluid mechanics, and thermoelasticity. The coupled mathematical model is then globally optimized by multi-objective optimization of the coupled mathematical model, which can optimize the multi-physical field coupling of the co-electrolysis system and make the optimized co-electrolysis system suitable for the regulation of complex working conditions.

[0169] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0170] The present application also provides an electronic device, Figure 4 The provided electronic device 400 includes a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and capable of running on the processor 420. When the computer program 411 is executed by the processor, the various processes of the above-mentioned multi-physical field coupling optimization method embodiment of the co-electrolysis system are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0171] The present application also provides a computer-readable storage medium. Figure 5 The computer-readable storage medium 500 provided stores a computer program 411. When the computer program 411 is executed by the processor, the various processes of the multi-physical field coupling optimization method embodiment of the above-mentioned co-electrolysis system are implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0172] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0173] It should be noted that the terms "first", "second", etc. in the description and claims of the embodiments of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate so that the embodiments described herein can be implemented in an order other than that shown or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or modules is not necessarily limited to those steps or modules clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. The division of modules that appears in the embodiments of the present application is only a logical division. In actual applications, there may be other division methods. For example, multiple modules can be combined into or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, and the indirect coupling or communication connection between modules can be electrical or other similar forms, which are not limited in the embodiments of the present application. Moreover, the modules or sub-modules described as separate components may or may not be physically separated, may or may not be physical modules, or may be distributed into multiple circuit modules, and some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiment of the present application.

[0174] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0175] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0176] In the several embodiments provided in the embodiments of the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules, which can be electrical, mechanical or other forms.

[0177] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of the present embodiment according to actual needs.

[0178] In addition, the functional modules in each embodiment of the present application can be integrated into a processing module, or each module can exist physically separately, or two or more modules can be integrated into a module. The above-mentioned integrated modules can be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0179] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0180] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in accordance with the embodiments of the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium that can be stored on a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, hard disk, or magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)).

[0181] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0182] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded into a computer or other programmable data processing terminal device, so that a series of operation steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for implementing the process in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0183] Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they become aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0184] Finally, it should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0185] The above is a detailed introduction to the technical solutions provided in the embodiments of the present application. Specific examples are used in the embodiments of the present application to illustrate the principles and implementation methods of the embodiments of the present application. The description of the above embodiments is only used to help understand the methods and core ideas of the embodiments of the present application. At the same time, for those skilled in the art, according to the ideas of the embodiments of the present application, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the embodiments of the present application.

Claims

1. A multi-physics field coupling optimization method for a co-electrolysis system, characterized in that: The co-electrolysis system has a structural design of a fractal flow channel, and the method includes: During the operation of the co-electrolysis system, a coupling mathematical model constructed based on a multi-physical field coupling relationship and a fractal porous medium permeability model for the fractal flow channel are obtained; the coupling mathematical model includes a micro-scale electrochemical kinetics calculation model, a meso-scale non-Darcy seepage simulation model, and a macro-scale thermal stress field calculation model, wherein the electrochemical kinetics calculation model is used to describe the three-phase boundary reaction in electrochemistry, the non-Darcy seepage simulation model is used to couple the electrochemical reaction mass source term of the electrochemical kinetics calculation model and the flow field pressure distribution of the fractal porous medium permeability model, and the thermal stress field calculation model is used to couple the convection term of the non-Darcy seepage simulation model and the mass source term of the electrochemical kinetics calculation model; Based on the physical field parameters output by the electrochemical kinetics calculation model, the non-Darcy seepage simulation model, and the thermal stress field calculation model, a corrected control parameter of the co-electrolysis system is obtained; the corrected control parameter is used to feed back to the electrochemical kinetics calculation model and the non-Darcy seepage simulation model; By adjusting the fractal parameters of the fractal porous media permeability model, an optimized permeability is obtained; the optimized permeability is provided to the non-Darcy seepage simulation model to update the flow field pressure distribution; Based on the real-time updated physical field parameters of the electrochemical kinetics calculation model, the non-Darcy seepage simulation model and the thermal stress field calculation model, the coupled mathematical model is multi-objective optimized to obtain an optimized coupled mathematical model.

2. The method according to claim 1, characterized in that The electrochemical kinetics calculation model is: in, is the local current density, is the exchange current density, is the anode transfer coefficient, is the cathode transfer coefficient, is the Faraday constant, is the overpotential, is the gas constant, is the temperature field, is the gas concentration, is the reference concentration, is the concentration correction index, i is used to indicate the gas component, where for the solid oxide electrolyzer of high temperature electrolysis, i=1 represents H2O, and i=2 represents CO2; for the proton exchange membrane electrolyzer of low temperature electrolysis, i=1 represents H2O, and i=2 represents O2.

3. The method according to claim 1, characterized in that The electrochemical reaction mass source term of the electrochemical kinetics calculation model is the reaction rate, which is calculated based on the local current density output by the electrochemical kinetics calculation model; the flow field pressure distribution of the fractal porous medium permeability model is based on the permeability output by the fractal porous medium permeability model; The non-Darcy seepage simulation model is: in, is the permeability, is the dynamic viscosity of the fluid, is the fluid pressure field, is the pressure gradient, is the reaction rate, is the total source term input of the mesoscopic flow field, Indicates the divergence operation of the vector field in the brackets; is the velocity field component, where the subscript d represents the spatial direction of the velocity field component.

4. The method according to claim 1, wherein The convection term of the non-Darcy flow simulation model is the flow rate output by the non-Darcy flow simulation model, and the mass source term of the electrochemical kinetics calculation model is the Joule heat source term generated by the current density of the electrochemical kinetics calculation model. The first model in the thermal stress field calculation model is used to indicate the coupling effect of fluid convection and heat conduction, and the first model is: in, is the velocity field component, is the temperature field, is the equivalent thermal conductivity, are the components of the spatial coordinates, is the Joule heat source term, where the subscript d represents the spatial direction of the velocity field component; The second model in the thermal stress field calculation model is used to characterize the thermal stress distribution caused by temperature gradient. The second model is: in, Indicates the divergence operation of thermal stress distribution; is the thermal stress distribution, is the material density, is the body force, is the stiffness tensor, is the thermal expansion coefficient tensor, is the temperature gradient.

5. The method according to claim 1, wherein The physical field parameters outputted by the electrochemical kinetics calculation model, the non-Darcy flow simulation model, and the thermal stress field calculation model are used to obtain the corrected control parameters of the co-electrolysis system, including: Constructing a state vector based on the physical field parameters output by the electrochemical kinetics calculation model, the non-Darcy seepage simulation model, and the thermal stress field calculation model; Using the state vector as an initial condition of a preset state-space equation, and predicting the predicted physical parameters of the co-electrolysis system at the next operating moment through the preset state-space equation; Acquiring actual sensor data of the co-electrolysis system at a next operating moment; The predicted physical parameters of the co-electrolysis system at the next operating moment and the actual sensor data of the co-electrolysis system at the next operating moment are substituted into the preset observation equation, and the control parameters of the co-electrolysis system are corrected in real time to obtain the corrected control parameters of the co-electrolysis system.

6. The method according to claim 5, characterized in that The preset state space equation is: in, is the state vector of the co-electrolysis system, , is the temperature field, is the fluid pressure field, is the gas concentration, and i is used to indicate the gas composition. For solid oxide electrolysis cells with high temperature electrolysis, i=1 indicates H2O, and i=2 indicates CO2; for proton exchange membrane electrolysis cells with low temperature electrolysis, i=1 indicates H2O, and i=2 indicates O2; is the thermal stress distribution, T represents the linear transformation matrix; t is the operating time of the co-electrolysis system; is the state transition matrix, is the input control matrix, is the control parameter, is the process noise distribution matrix, is the process noise vector.

7. The method according to claim 5, characterized in that The preset observation equation is: in, is the observation vector of the co-electrolysis system at the k-th running time, is the observation matrix, is the predicted state vector of the co-electrolysis system at the k-th operating time, k is the next operating time of the co-electrolysis system, k-1 is the previous operating time of the co-electrolysis system, is the observation noise vector, which is used to correct the state estimation.

8. The method according to claim 1, characterized in that The optimized permeability is obtained by adjusting the fractal parameters of the fractal porous medium permeability model, including: With the goal of minimizing a preset coupling objective function, the fractal parameters of the fractal porous media permeability model are adjusted to obtain an optimized permeability; The preset coupling objective function is: in, and is the weight coefficient, is the total pressure drop in the flow channel, is the maximum temperature gradient in the flow channel, is the dynamic viscosity of the fluid, is the velocity field component, is the permeability, l is the integral path along the seepage, and dl is the displacement vector along the seepage path; The fractal parameters include porosity, tortuosity, fractal dimension, and flow channel radius classification of the fractal flow channel. The flow channel radius classification of the fractal flow channel is used to determine the maximum pore radius and the porosity. The fractal porous media permeability model is: in, is the permeability, is the porosity, is the maximum pore radius, is the tortuosity, is the fractal dimension.

9. The method according to any one of claims 1 to 8, characterized in that The physical field parameters updated in real time based on the electrochemical kinetics calculation model, the non-Darcy seepage simulation model, and the thermal stress field calculation model are used to perform multi-objective optimization on the coupled mathematical model to obtain an optimized coupled mathematical model, including: Constructing a multi-objective optimization function based on the real-time updated physical field parameters of the electrochemical kinetics calculation model, the non-Darcy seepage simulation model, and the thermal stress field calculation model; Solving the multi-objective optimization function to obtain a Pareto optimal solution set; the Pareto optimal solution set includes physical field parameters after multi-objective optimization; The physical field parameters after the multi-objective optimization are fed back to the coupling mathematical model to obtain an optimized coupling mathematical model; the optimized coupling mathematical model is a coupling mathematical model after global iteration.

10. The method according to claim 9, characterized in that The multi-objective optimization function is: in, is the square integral of the temperature gradient, is the temperature field gradient, is the battery stack volume; is the total deviation between the measured current and the current predicted by the model, is the current measured experimentally, is the model current; is the maximum von Mises thermal stress, is the von Mises stress.

11. A multi-physics field coupling optimization device for a co-electrolysis system, characterized in that: The co-electrolysis system has a fractal flow channel structure design, and the device includes: A model acquisition module is used to obtain a coupling mathematical model constructed based on a multi-physical field coupling relationship and a fractal porous medium permeability model for the fractal flow channel during the operation of the co-electrolysis system; the coupling mathematical model includes a microscale electrochemical kinetics calculation model, a mesoscale non-Darcy seepage simulation model, and a macroscale thermal stress field calculation model, wherein the electrochemical kinetics calculation model is used to describe the three-phase boundary reaction in electrochemistry, the non-Darcy seepage simulation model is used to couple the electrochemical reaction mass source term of the electrochemical kinetics calculation model and the flow field pressure distribution of the fractal porous medium permeability model, and the thermal stress field calculation model is used to couple the convection term of the non-Darcy seepage simulation model and the mass source term of the electrochemical kinetics calculation model; a control parameter correction module, configured to obtain corrected control parameters of the co-electrolysis system based on the physical field parameters output by the electrochemical kinetics calculation model, the non-Darcy seepage simulation model, and the thermal stress field calculation model; wherein the corrected control parameters are fed back to the electrochemical kinetics calculation model and the non-Darcy seepage simulation model; a permeability optimization module, configured to obtain an optimized permeability by adjusting the fractal parameters of the fractal porous media permeability model; and providing the optimized permeability to the non-Darcy seepage simulation model to update the flow field pressure distribution; The mathematical model optimization module is used to perform multi-objective optimization on the coupled mathematical model based on the physical field parameters updated in real time by the electrochemical kinetics calculation model, the non-Darcy seepage simulation model and the thermal stress field calculation model to obtain an optimized coupled mathematical model.

12. An electronic device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein when the computer program is executed by the processor, the multi-physical field coupling optimization method of the co-electrolysis system according to any one of claims 1 to 10 is implemented.

13. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the multi-physical field coupling optimization method for the co-electrolysis system according to any one of claims 1 to 10.

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