Three-dimensional temperature field reconstruction method and system for electrolytic cell based on multi-physical field coupling

Abstract

The application provides a three-dimensional temperature field reconstruction method and system of an electrolytic cell based on multi-physical field coupling, and belongs to the technical field of thermal management of energy equipment. The reconstruction method is applied to a reconstruction system comprising an acquisition module, a first processing module, a second processing module, a third processing module, a fourth processing module and a rendering module, and specifically comprises the following steps: layering a three-dimensional model of the electrolytic cell according to the Z-axis direction, establishing a layered database and setting a reference layer; generating an initial temperature field distribution of the reference layer based on an anisotropic radial basis function interpolation algorithm; establishing an electro-thermal-fluid coupling equation and solving according to the layered database to obtain the temperature field distribution of the reference layer, mapping the temperature field distribution to a three-dimensional space, correcting the temperature field distribution in combination with interlayer temperature attenuation and lateral thermal diffusion effect, obtaining a target temperature field distribution and performing visual processing on the target temperature field distribution. The application can completely reproduce the nonlinear temperature distribution of the electrolytic cell and guarantee the accuracy of the three-dimensional temperature field reconstruction.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy equipment thermal management, in particular to an electrolytic cell three-dimensional temperature field reconstruction method and system based on multi-physical field coupling. BACKGROUND

[0002] Due to the influence of the closed structure of the electrolytic cell, it is not possible to implement large-scale deployment of sensors inside the electrolytic cell, and usually only a few discrete sensors can be used to identify the temperature distribution inside the electrolytic cell to implement subsequent online thermal management or fault diagnosis. However, this temperature distribution identification method relying on a few discrete sensors cannot capture the details of the temperature field distribution inside the electrolytic cell, and the lack of such thermal distribution details not only accelerates the proton membrane aging of the electrolytic cell due to abnormal flow channel heat accumulation, but also causes catalyst activity attenuation, and further increases the false alarm rate of the electrolytic cell, affecting the operation safety of the energy equipment such as hydrogen energy equipment and fuel cells where the electrolytic cell is located.

[0003] Under the condition of limited sensor configuration, the temperature field distribution of the electrolytic cell is usually reconstructed to capture the thermal distribution details inside the electrolytic cell to ensure the operation safety of the energy equipment where the electrolytic cell is located. However, as a closed three-dimensional layered equipment, the electrolytic cell contains multiple components such as bipolar plates, membrane electrodes, and flow channels, and there is strong coupling of multiple physical fields such as electricity, heat, flow, and chemical reactions inside the electrolytic cell. Under the action of multi-physical field coupling, the electrolytic cell inside will show nonlinear temperature distribution characteristics. However, the current mainstream electrolytic cell temperature field reconstruction methods, such as linear interpolation methods based on uniform grids, traditional inversion models, and Kriging interpolation algorithms, cannot adapt to the high-precision temperature field distribution reconstruction requirements of the electrolytic cell in such a three-dimensional scenario, and easily ignore the nonlinear temperature distribution characteristics shown by the electrolytic cell inside, resulting in low accuracy of temperature distribution identification inside the electrolytic cell. SUMMARY

[0004] The present application aims to overcome the shortcomings in the prior art that easily ignore the nonlinear temperature distribution characteristics shown by the electrolytic cell inside under the action of multi-physical field coupling, and provide an electrolytic cell three-dimensional temperature field reconstruction method and system based on multi-physical field coupling, which focuses on the temperature field of different components through Z-axis layered modeling, and combines anisotropic radial basis function interpolation to adapt to the complex flow channel heat transfer characteristics, and further solves the electro-thermal-flow coupling equation based on the sampling data to incorporate the multi-physical field mechanism, reduces the flow channel temperature rise deviation and key area temperature estimation error, and then maps the reference layer temperature field to the three-dimensional space layer by layer, and combines the interlayer temperature attenuation and transverse thermal diffusion effect correction to completely reproduce the nonlinear temperature distribution, ensuring the accuracy of the electrolytic cell three-dimensional temperature field reconstruction.

[0005] The present application is achieved by the following technical solutions:

[0006] The method for reconstructing a three-dimensional temperature field of an electrolytic cell based on multi-physical field coupling comprises the following steps:

[0007] Layering a three-dimensional model of the electrolytic cell according to the Z-axis direction, establishing a layered database according to the layering result, and setting a reference layer;

[0008] Generating an initial temperature field distribution of the reference layer based on an anisotropic radial basis function interpolation algorithm;

[0009] Establishing an electro-thermal-fluid coupling equation according to the initial temperature field of the reference layer, sampling the layered database, solving the electro-thermal-fluid coupling equation according to the sampling data, and obtaining the temperature field distribution of the reference layer;

[0010] Layering and mapping the temperature field distribution of the reference layer to a three-dimensional space along the Z-axis direction, correcting the temperature field distribution of the reference layer in combination with interlayer temperature attenuation and lateral heat diffusion effect, and obtaining a target temperature field distribution of the reference layer;

[0011] Rendering the target temperature field distribution to obtain a visualized image of the three-dimensional temperature field of the electrolytic cell.

[0012] The Z-axis layering modeling focuses on the temperature field of different components, avoids the loss of local details caused by overall modeling, and combines the anisotropic radial basis function interpolation to adapt to the heat conduction anisotropy caused by the current density gradient of the bipolar plate under complex flow channels, thereby reducing the temperature rise prediction deviation in areas such as the serpentine flow channel. Based on the sampling data, the electro-thermal-fluid coupling equation is solved to integrate the multi-physical field mechanism of electrochemical reaction, heat conduction, and fluid flow, further reducing the temperature estimation error of the boundary heat enhancement area and the membrane electrode interface. Meanwhile, the temperature field of the reference layer is layered and mapped to a three-dimensional space, and the temperature field is corrected in combination with interlayer temperature attenuation and lateral heat diffusion effect, so as to completely reproduce the overall nonlinear temperature distribution of the layered structure and ensure the accuracy of the reconstruction of the three-dimensional temperature field of the electrolytic cell.

[0013] Further, the layering of the three-dimensional model of the electrolytic cell according to the Z-axis direction, the establishment of the layered database according to the layering result, and the setting of the reference layer comprise the following steps:

[0014] Discretizing the three-dimensional model of the electrolytic cell into multiple two-dimensional sections along the Z-axis direction;

[0015] Adaptively layering the multiple two-dimensional sections in combination with a vertex curvature adaptive layering strategy according to curvature analysis and normal vector clustering algorithm;

[0016] Extracting three-dimensional coordinate data of each layer, establishing a layered database, and selecting a reference layer from the layering.

[0017] Further, the generation of the initial temperature field distribution of the reference layer based on the anisotropic radial basis function interpolation algorithm comprises the following steps:

[0018] The main diffusion axis in the flow channel cross-section direction is obtained by principal component analysis, and an anisotropic distance metric function is established;

[0019] The data in the layered database is spatially interpolated based on the anisotropic distance metric function, and an initial temperature field distribution of the reference layer is generated by combining regularization parameter optimization.

[0020] Further, the sampling of the layered database includes:

[0021] The reference layer is divided into four quadrants, the average temperature gradient in each quadrant is calculated, and the weight of each quadrant is assigned according to the average temperature gradient;

[0022] The number of sampling points in each quadrant is assigned based on the corresponding weight, and an additional sampling point is generated in combination with a boundary protection mechanism;

[0023] The layered database is sampled according to the corresponding number of sampling points and the generated additional sampling points to obtain sampling data.

[0024] Further, the temperature field distribution of the reference layer is obtained by solving the electro-thermal-fluid coupling equation based on the sampling data, including:

[0025] The electro-thermal-fluid coupling equation is discretized by finite volume method, and an artificial diffusion term is introduced;

[0026] The soft constraint boundary condition is set, the sampling data is input as the boundary, and the electro-thermal-fluid coupling equation after discretization and introduction of the artificial diffusion term is iteratively solved to obtain the temperature field distribution of the reference layer.

[0027] Further, the temperature field distribution of the reference layer is layered and mapped to the three-dimensional space along the Z-axis direction, and the target temperature field distribution of the reference layer is obtained by correcting the temperature field distribution of the reference layer in combination with the interlayer temperature attenuation and the lateral thermal diffusion effect, including:

[0028] The temperature attenuation coefficient of the temperature field distribution of the reference layer along the Z-axis is calculated, and the spatial mapping relationship between the non-reference layer vertex and the reference layer is established according to the fast nearest neighbor search algorithm;

[0029] The temperature field distribution of the reference layer is corrected according to the temperature attenuation coefficient and the lateral thermal diffusion effect to obtain the target temperature layer distribution of the reference layer.

[0030] Further, the target temperature field distribution is rendered to obtain a visual image of the three-dimensional temperature field of the electrolytic cell, including:

[0031] The target temperature field distribution of the reference layer is rendered based on a multi-modal rendering engine to obtain a surface isotherm cloud chart and a three-dimensional volume rendering chart of the electrolytic cell.

[0032] The electrolytic cell three-dimensional temperature field reconstruction system based on multi-physical field coupling is used for executing the electrolytic cell three-dimensional temperature field reconstruction method based on multi-physical field coupling, and comprises:

[0033] The acquisition module is used for acquiring a three-dimensional model of the electrolytic cell, layering in the Z-axis direction, establishing a layering database according to a layering result, and setting a reference layer;

[0034] The first processing module is used for generating an initial temperature field distribution of the reference layer based on an anisotropic radial basis function interpolation algorithm;

[0035] The second processing module is used for sampling the layering database and acquiring corresponding sampling data;

[0036] The third processing module is used for inputting the sampling data into an electro-thermal-fluid coupling equation, solving the initial temperature field distribution of the reference layer, and acquiring a temperature field distribution of the reference layer;

[0037] The fourth processing module is used for layering and mapping the temperature field distribution of the reference layer to a three-dimensional space along the Z-axis direction, correcting the temperature field distribution of the reference layer in combination with interlayer temperature attenuation and transverse thermal diffusion effect, and acquiring a target temperature field distribution of the reference layer;

[0038] The rendering module is used for rendering the target temperature field distribution of the reference layer to obtain a visual image of the three-dimensional temperature field of the electrolytic cell.

[0039] An electronic device comprises a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the electrolytic cell three-dimensional temperature field reconstruction method based on multi-physical field coupling.

[0040] A non-transitory computer readable storage medium has a computer program stored thereon, and the computer program is executed by a processor to implement the electrolytic cell three-dimensional temperature field reconstruction method based on multi-physical field coupling.

[0041] The present application has the following beneficial effects:

[0042] (1) Focus on the temperature field of different components by Z-axis layered modeling, avoid the loss of local details caused by overall modeling, and combine anisotropic radial basis function interpolation to adapt to the heat anisotropy caused by the current density gradient of the bipolar plate under complex flow channel, reduce the temperature rise prediction deviation of the serpentine flow channel and other areas. Based on the sampling data, the electro-thermal-flow coupling equation is solved to integrate the multi-physical field mechanism of electrochemical reaction, heat conduction and fluid flow, and further reduce the temperature estimation error of the boundary enhanced heat transfer area and the membrane electrode interface. At the same time, the temperature field of the reference layer is mapped to the three-dimensional space in layers, and the temperature field is corrected by combining the interlayer temperature attenuation and lateral heat diffusion effect, so as to completely reproduce the overall nonlinear temperature distribution of the layered structure and ensure the accuracy of the three-dimensional temperature field reconstruction of the electrolytic cell.

[0043] (2) Through Z-axis discretization combined with curvature analysis, normal vector clustering and vertex curvature adaptive layering strategy, match the structural differences of different components of the electrolytic cell, avoid the loss of local structure temperature information caused by traditional equal layering, and further construct the corresponding layered database and set the reference layer, so as to more accurately simulate the temperature distribution of different levels and effectively improve the accuracy of the temperature field distribution reconstruction of the electrolytic cell.

[0044] (3) Use principal component analysis to determine the main diffusion axis of the flow channel cross section and construct anisotropic distance measurement function, so that the spatial interpolation is more in line with the anisotropic characteristics of heat transfer in the electrolytic cell, and further combine the regularization parameter optimization to effectively reduce the interpolation error in the complex flow channel area and improve the accuracy of the initial temperature field distribution of the reference layer. Further combined with the data sampling of the layered database, the electro-thermal-flow coupling equation constructed according to the initial temperature field distribution is solved, and the stability of the solution is improved by discrete finite volume method and artificial diffusion term in the solving process, and combined with the soft constraint boundary condition, the solution result is closer to the real running state of the electrolytic cell, reduces the temperature estimation deviation of the key parts such as the boundary enhanced heat transfer area, and ensures the accuracy of the reference layer temperature field distribution obtained.

[0045] (4) Through the spatial mapping relationship established by temperature attenuation coefficient calculation and fast nearest neighbor search, realize the extension of the reference layer temperature field to three-dimensional space, and further correct the reference layer temperature field by combining interlayer temperature attenuation and lateral heat diffusion effect, which can effectively make up for the error caused by ignoring interlayer heat transfer, and completely reproduce the nonlinear temperature distribution in three-dimensional space. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 is a flow schematic diagram of the present application;

[0047] Figure 2 is a structure schematic diagram of a three-dimensional model before layering of an embodiment of the present application;

[0048] Figure 3is a structural schematic diagram of a three-dimensional model after layering of an embodiment of the present application;

[0049] Figure 4 is a structural schematic diagram of a three-dimensional model after final layering of an embodiment of the present application;

[0050] Figure 5 is a flowchart of visualization of a three-dimensional temperature field of an electrolytic cell of an embodiment of the present application;

[0051] Figure 6 is a structural schematic diagram of a three-dimensional temperature field reconstruction system of an electrolytic cell of an embodiment of the present application;

[0052] Figure 7 is a structural schematic diagram of an electronic device of an embodiment of the present application.

[0053] Wherein: 1, acquisition module, 2, first processing module, 3, second processing module, 4, third processing module, 5, fourth processing module, 6, rendering module, 7, electronic device, 71, memory, 72, processor. DETAILED DESCRIPTION

[0054] The present application will be further described below in conjunction with the drawings and embodiments.

[0055] Embodiment: The purpose of the present application is achieved by the following technical solutions:

[0056] The technical solutions in the embodiments of the present application will be clearly described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art belong to the scope of protection of the present application.

[0057] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, not to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of a kind and do not limit the number of objects, for example, the first object can be one or more. In addition, "and / or" in the specification and claims indicates at least one of the connected objects, and the character " / ", generally indicates that the objects before and after are in a "or" relationship.

[0058] The application will be described in detail below with reference to the drawings and specific embodiments and application scenarios thereof.

[0059] The method for reconstructing a three-dimensional temperature field of an electrolytic cell based on multi-physics coupling can be applied to a terminal, and can be executed by hardware or software in the terminal.

[0060] The terminal includes, but is not limited to, a portable communication device such as a mobile phone or a tablet computer having a touch-sensitive surface (for example, a touchscreen display and / or a touchpad). It should also be understood that, in some embodiments, the terminal can not be a portable communication device, but a desktop computer having a touch-sensitive surface (for example, a touchscreen display and / or a touchpad).

[0061] In the following various embodiments, a terminal including a display and a touch-sensitive surface is described. However, it should be understood that the terminal can include one or more other physical user interface devices such as physical keyboards, mice, and joysticks.

[0062] The method for reconstructing a three-dimensional temperature field of an electrolytic cell based on multi-physics coupling provided by the application can be executed by an electronic device or a functional module or functional entity in the electronic device capable of realizing the method for reconstructing a three-dimensional temperature field of an electrolytic cell based on multi-physics coupling. The electronic device mentioned in the application includes, but is not limited to, a mobile phone, a tablet computer, a computer, a camera, and a wearable device, etc. The method for reconstructing a three-dimensional temperature field of an electrolytic cell based on multi-physics coupling provided by the application will be described below with the electronic device as an example.

[0063] The method for reconstructing a three-dimensional temperature field of an electrolytic cell based on multi-physics coupling, as shown in Figure 1 includes the following steps.

[0064] Layering a three-dimensional model of the electrolytic cell according to the Z-axis direction, establishing a layered database according to the layering result and setting a reference layer;

[0065] Generating an initial temperature field distribution of the reference layer based on an anisotropic radial basis function interpolation algorithm;

[0066] Establishing an electro-thermal-fluid coupling equation according to the initial temperature field of the reference layer, sampling the layered database, solving the electro-thermal-fluid coupling equation according to the sampling data, and obtaining the temperature field distribution of the reference layer;

[0067] mapping the temperature field distribution of the reference layer along the Z-axis direction to the three-dimensional space layer by layer, correcting the temperature field distribution of the reference layer in combination with the interlayer temperature attenuation and lateral heat diffusion effect, and obtaining the target temperature field distribution of the reference layer;

[0068] rendering the target temperature field distribution to obtain a visualized image of the three-dimensional temperature field of the electrolytic cell.

[0069] The electrolytic cell is stacked along the Z-axis direction by multiple components such as bipolar plates, membrane electrodes, and flow channels, and the material properties, structural forms, and heat transfer characteristics of different components are significantly different, resulting in strong layered heterogeneity of the temperature field in the Z-axis direction. If the three-dimensional space is regarded as a unified whole to construct the three-dimensional temperature field of the electrolytic cell, the interlayer differences are easily ignored, resulting in the smoothing of local details such as the temperature change at the contact interface between the membrane electrode and the bipolar plate, and the real heat distribution cannot be reflected. Moreover, the three-dimensional model data is large, and the calculation amount is large if the whole domain is calculated directly, and the reconstruction efficiency is low.

[0070] Therefore, by performing fine modeling through layering and decomposition, the three-dimensional problem is divided into layered two-dimensional and interlayer correlation, reducing the calculation amount and improving the reconstruction amount.

[0071] The three-dimensional model of the electrolytic cell is layered along the Z-axis direction, and a layered database is established and a reference layer is set according to the layering result, comprising:

[0072] The three-dimensional model of the electrolytic cell is discretized into multiple two-dimensional cross sections along the Z-axis direction;

[0073] According to the curvature analysis and normal vector clustering algorithm, the multiple two-dimensional cross sections are adaptively layered in combination with the vertex curvature adaptive layering strategy;

[0074] The three-dimensional coordinate data of each layer is extracted, a layered database is established, and a reference layer is selected from the layering.

[0075] Specifically, the corresponding three-dimensional model is determined by extracting the STL (Stereolithography) model file of the electrolytic cell, and the STL model file records the surface geometric topology information of the components such as the bipolar plates, membrane electrodes, and flow channels of the electrolytic cell.

[0076] Then, the Z-axis layering algorithm is used to identify the distribution range of the Z-axis coordinates of the vertices, and the component stacking order of the electrolytic cell along the height direction is determined, so as to obtain the topology structure of the three-dimensional model, and then an initial discrete interval is set along the Z-axis to cut the complete three-dimensional model into a series of two-dimensional cross sections parallel to the XY plane. The structure diagram of the three-dimensional model before layering is shown in Figure 2 The structure diagram of the three-dimensional model after layering is shown in Figure 3 The expression of the interlayer spacing during layering is:

[0077] .

[0078] wherein the optimal number of layers is calculated by the formula:

[0079] ;

[0080] wherein, is a complexity penalty factor, the default value is 0.01, and the feature point data provided by a small number of sensors is initialized, is the optimal number of layers, and MAE is the mean absolute error of the temperature field reconstruction, is the layer height, i.e., the thickness of a single layer.

[0081] At the same time, three-dimensional vertex coordinate data is extracted to convert the discrete triangular facet information in the STL model file into structured coordinate data arranged in order along the Z-axis direction.

[0082] Then, the structural complexity of each two-dimensional section is identified through curvature analysis, and the greater the geometric morphological change, the higher the vertex curvature value. Then, the regions with similar directions of vertex normal vectors in each section are classified through a normal vector clustering algorithm to further clarify the partition of structure flatness and complexity.

[0083] Then, based on the results of curvature analysis and normal vector clustering algorithm, the two-dimensional sections obtained are adaptively layered through a vertex curvature adaptive layering strategy. The layering interval is reduced for regions with high curvature and complex structure to increase the number of two-dimensional sections to retain details, and the layering interval is increased for regions with low curvature and flat structure to reduce invalid sections. The final layered structure diagram of the three-dimensional model is shown in Figure 4 .

[0084] Finally, all vertex three-dimensional coordinates of each two-dimensional section are extracted one by one, and the corresponding layer component attributes are associated. These data are stored in order according to the layer number to construct a layered database. In addition to three-dimensional coordinate data, the layered database further records the corresponding feature point distribution and direction amount direction to provide a geometric reference for subsequent interpolation. On this basis, a reference layer is selected from all layers. In the selection process, the layer containing the core reaction and heat transfer region of the electrolytic cell is preferentially selected.

[0085] Although the layered database already has structured three-dimensional coordinate data and component attributes, it lacks corresponding temperature distribution information. The reference layer contains flow channels, membrane electrodes and other components. The flow channel region presents an anisotropic characteristic of strong heat transfer along the flow channel direction and weak heat transfer perpendicular to the flow channel direction due to fluid flow. Moreover, the number of discrete sensors installed in the electrolytic cell is small, and traditional interpolation methods cannot generate an initial temperature field that fits the true thermal state of the reference layer. Therefore, the anisotropic radial basis function interpolation algorithm is used to generate the initial temperature field distribution of the reference layer.

[0086] Specifically, the initial temperature field distribution of the reference layer is generated based on the anisotropic radial basis function interpolation algorithm, comprising:

[0087] The main diffusion axis in the direction of the flow passage cross section is obtained by principal component analysis, and an anisotropic distance measurement function is established.

[0088] Based on the anisotropic distance measurement function, each data in the layered database is spatially interpolated, and the initial temperature field distribution of the reference layer is generated by combining the regularization parameter optimization.

[0089] First, the principal component analysis is used to extract the key direction of dominant temperature diffusion from the geometric and heat transfer data of the reference layer flow passage, i.e. the main diffusion axis. Specifically, the three-dimensional coordinate data of the flow passage cross section of the reference layer in the layered database is combined with the flow direction and heat transfer data of the fluid in the flow passage. The direction with the largest data variance is identified by principal component analysis, and is determined as the main diffusion axis of the flow passage cross section.

[0090] Specifically, the calculation formula of the main diffusion axis by principal component analysis is:

[0091] ;

[0092] Where Cov is the covariance matrix, N is the number of sample points, is the two-dimensional coordinate of the kth sample point, is the sample point mean vector.

[0093] According to the main diffusion axis, an anisotropic distance weight matrix is calculated , wherein , is the PCA eigenvalue, and the spatial measurement standard of the radial basis function kernel function is adjusted, and the expression of the modified radial basis function kernel function is:

[0094] ;

[0095] Wherein, is the anisotropic radial basis kernel function, is the shape parameter, i.e. the kernel function width, is the anisotropic distance weight matrix.

[0096] Then, the shape parameter of the Gaussian kernel function is dynamically adjusted based on the nearest neighbor statistical result, and the expression of the adjustment formula is as follows:

[0097] ;

[0098] Wherein, is the shape parameter of the radial basis function kernel function, N is the number of sample points of the reference layer, and coordinates of the feature point i and the feature point j.

[0099] On this basis, each data in the layered database is spatially interpolated, and in the interpolation process, the limited discrete temperature measurement points are taken as the core, the heat transfer correlation degree of each unknown temperature point in the layered database and the temperature measurement point is calculated according to the anisotropic distance measurement function composed of the anisotropic distance weight matrix and the radial basis function kernel function, and the temperature value of the unknown point is calculated through the correlation degree weighting.

[0100] In the interpolation process, a Tikhonov regularization term is introduced into the coefficient matrix of the radial basis function kernel function, and the regularization parameter is optimized and selected through the L-curve criterion, so as to improve the stability and accuracy of the interpolation through the optimization of the regularization parameter.

[0101] The coefficient matrix of the radial basis function kernel function The matrix element in the coefficient matrix of the radial basis function kernel function .

[0102] The expression of the Tikhonov regularization term is:

[0103] ;

[0104] Wherein, L is a second-order difference matrix, selected by L-curve method, c is a coefficient vector to be solved, A is a design matrix, is an observation temperature vector.

[0105] Finally, according to the generated temperature values of each point, the initial temperature field distribution of the reference layer is obtained. The finally generated initial temperature field distribution of the reference layer not only contains high-resolution temperature data, but also includes temperature gradient distribution graph.

[0106] Considering that the amount of data in the layered database is large, in order to improve the subsequent calculation efficiency, the layered database is further sampled. The generated initial temperature field has shown the temperature distribution trend of the reference layer, especially through the anisotropic interpolation, the high gradient area characteristics such as heat gathering of flow channel corner and temperature change of membrane electrode interface, and the low gradient area characteristics such as plate plane are reserved. The gradient difference directly determines the influence weight of different areas on the reconstruction accuracy of temperature field. The high gradient area is the core area of thermal risk and the sensitive area of subsequent coupled equation solving. If the sampling is insufficient, the key thermal characteristics will be lost. The temperature distribution of the low gradient area is stable, and excessive sampling will cause data redundancy and waste of computing resources. Therefore, the four-quadrant weighted sampling algorithm is used to realize the data sampling processing of the layered database.

[0107] Specifically, the sampling of the layered database comprises:

[0108] The reference layer is divided into four quadrants, the average temperature gradient in each quadrant is calculated, and the weight of each quadrant is allocated according to the average temperature gradient;

[0109] The number of sampling points in each quadrant is allocated according to the corresponding weight, and an additional sampling point is generated based on a boundary protection mechanism;

[0110] According to the corresponding number of sampling points and the generated additional sampling points, the layered database is sampled to obtain sampling data.

[0111] Firstly, the reference layer is divided into four quadrants along the X-axis and Y-axis midlines to more accurately locate the gradient differences in different regions. Then the average temperature gradient in each quadrant is calculated Then, the weight is allocated according to the average temperature gradient. The higher the average gradient value of a quadrant, the more intense the temperature change in that region, the more prominent the thermal risk, and the greater the impact on the accuracy of the subsequent coupling equation solution, so a higher weight is given, and vice versa.

[0112] The formula for calculating the average temperature gradient is:

[0113] ;

[0114] Where, is the average temperature gradient amplitude of the qth quadrant, is the number of data points in the qth quadrant.

[0115] According to the corresponding sampling point number allocated according to the quadrant weight, the sampling density of the high gradient area and the sampling density of the low gradient area satisfy:

[0116] ;

[0117] Where, is the sampling density of the high gradient area, is the initial reference sampling density, is the temperature gradient of the qth quadrant, is the sampling density of the low gradient area.

[0118] The expression for allocating the number of sampling points is:

[0119] ;

[0120] Where, =0.1 is a smoothing factor to prevent division by zero, is the number of sampling points allocated to the qth quadrant, is the total number of sampling points, is the average temperature gradient amplitude of the qth quadrant, is a smoothing adjustment parameter.

[0121] At the same time, additional sampling points are generated in combination with the boundary protection mechanism, which are arranged at the boundary lines of each quadrant, the physical boundary of the reference layer and the boundary region of the gradient mutation, so as to not affect the sampling density of the high gradient region and to provide clear boundary temperature input for the coupled equation, thereby avoiding unreasonable fluctuations of the boundary temperature during solving.

[0122] The arrangement interval of the additional sampling points needs to meet:

[0123] ;

[0124] Wherein, The boundary attenuation coefficient is 0.2-0.5.

[0125] At this time, the reference layer only has an initial temperature field distribution, which is a preliminary temperature framework generated by anisotropic interpolation and has not been integrated into the coupling effect of the internal electric, thermal and flow multi-physical fields of the electrolytic cell, and thus deviates from the real running state and cannot be directly used as the final temperature result of the reference layer.

[0126] Therefore, control equations of the electric, thermal and flow physical fields are established first, the electric domain equation describes the current density distribution and ohmic heat, the thermal domain equation describes the temperature change and heat transfer, and the flow domain equation describes the fluid flow state, and then the control equations of the three physical fields are coupled to describe the coupling effect of the internal multi-physical fields of the electrolytic cell.

[0127] The expression of the electric-thermal-flow coupled equation is:

[0128] ;

[0129] Wherein, k is the equivalent thermal conductivity corrected by the membrane thickness, Q is the joule heat source term calculated based on the current density, is the fluid density, is the specific heat capacity, T is the temperature field, and t is the time variable, is the flow velocity vector field.

[0130] By introducing the key physical quantities such as the equivalent thermal conductivity corrected by the membrane thickness, the joule heat source term, the fluid density, the specific heat capacity and the flow velocity vector field, and combining the change of the temperature field distribution with time and space, the complex electric-thermal-flow coupling process is accurately simulated, and the electric-thermal-flow equation is solved in combination with the sampling data, so that the temperature field can be accurately predicted.

[0131] Specifically, the electric-thermal-flow coupled equation is solved according to the sampling data to obtain the temperature field distribution of the reference layer, including:

[0132] The electric-thermal-flow coupled equation is discretized by the finite volume method, and an artificial diffusion term is introduced.

[0133] The soft constraint boundary condition is set, the sampling data is taken as the boundary input, the electric-thermal-flow coupling equation after the discretization processing and the introduction of the artificial diffusion term is iteratively solved, and the temperature field distribution of the reference layer is obtained.

[0134] The electric, thermal and flow fields of the reference layer of the electrolytic cell are continuous physical fields, and the control equation thereof exists in the form of partial differential equation, which cannot be directly solved. Therefore, the space of the reference layer is divided into a large number of small control volumes by the finite volume method, each control volume is taken as a basic calculation unit, the partial differential equation is integrated in the control volume, so that the complex partial differential equation group is converted into an algebraic equation group containing thousands to tens of thousands of unknowns, so that the electric-thermal-flow equation has solvability.

[0135] Specifically, the expression of the finite volume method is:

[0136] ;

[0137] Wherein, is the electrical conductivity, V is the potential part. For each control volume Integrate, and the convection term is discretized by the second-order upwind scheme.

[0138] And in the flow channel corner, the membrane electrode and the bipolar plate contact area and other areas, because of the sudden change of physical parameters, the algebraic equation group after discretization may appear severe fluctuations of the solution, therefore, in order to avoid the risk of numerical oscillation after discretization, an artificial diffusion term is introduced to ensure that the key gradient characteristics such as flow channel heat accumulation and membrane electrode interface temperature change are not covered while suppressing oscillation, and the stability and result accuracy of the solution are balanced.

[0139] The expression of the artificial diffusion term is:

[0140] ;

[0141] Wherein, is a stability factor, the value range is 0.1-0.3, is the spatial discretization step.

[0142] And the stability factor of the artificial diffusion term can be adaptively adjusted according to the local grid size and temperature gradient obtained after discretization.

[0143] There are clear boundary conditions when the electrolytic cell is running, such as the inlet cooling liquid temperature of the flow channel, the heat dissipation coefficient of the edge of the plate, etc. If the boundary conditions are unknown when solving, the equation will have an unconstrained solution, which is seriously inconsistent with the actual working condition. Therefore, a soft constraint boundary condition is set to allow the sampling data to adapt to the equation logic within a small range, which not only ensures that the boundary temperature is close to the measured data, but also avoids the situation that the equation has no solution due to the small error of the sampling data. The sampling data of the four-quadrant sampling is classified according to the physical field, and the temperature data corresponds to the thermal domain boundary, the flow rate data corresponds to the flow domain boundary, and the current data corresponds to the electric domain boundary, which ensures that the solving result conforms to the physical logic of the actual operation of the electrolytic cell.

[0144] Based on the above-mentioned processed equation, an iterative solution is carried out. First, a set of initial values is assumed, including the average temperature of the reference layer, the average flow rate of the flow channel, the average current density and other parameters, which are substituted into the discrete algebraic equation set to calculate the temperature, flow rate and current density of each control body. Then, the deviation of the calculation result from the sampling data is compared, and the initial value is adjusted according to the deviation result, and then substituted into the equation for calculation. Repeat the process of assumption, calculation, deviation and correction until the deviation of all sampling points is less than the set threshold value, at which time the equation converges, and the obtained temperature distribution is the temperature field distribution of the reference layer. And in the solving process, the mass conservation error is also monitored in real time, and when the residual error exceeds the threshold value, the feedback mechanism is triggered to add sampling points in a specific area, forming a closed-loop optimization system, and finally outputting the temperature field distribution that satisfies the law of conservation of mass.

[0145] Since the obtained temperature field distribution of the reference layer is still essentially a single two-dimensional cross-sectional temperature distribution, it does not cover the full spatial structure of the electrolytic cell along the Z-axis, and ignores the interlayer heat transfer effect, which still deviates from the actual situation. Therefore, further through three-dimensional mapping and effect correction, the two-dimensional temperature information of the reference layer is expanded to three-dimensional temperature information including interlayer heat interaction.

[0146] Among them, the temperature field distribution of the reference layer is layered along the Z-axis direction and mapped to a three-dimensional space, and the temperature field distribution of the reference layer is corrected by combining the interlayer temperature attenuation and the lateral heat diffusion effect to obtain the target temperature field distribution of the reference layer, comprising:

[0147] The temperature attenuation coefficient of the temperature field distribution of the reference layer along the Z-axis is calculated, and the spatial mapping relationship between the non-reference layer vertex and the reference layer is established according to the fast nearest neighbor search algorithm;

[0148] The temperature field distribution of the reference layer is corrected according to the temperature attenuation coefficient and the lateral heat diffusion effect to obtain the target temperature layer distribution of the reference layer.

[0149] The calculation formula of the temperature attenuation coefficient is:

[0150] ;

[0151] wherein L is the characteristic attenuation length, and z is the height difference between the current layer and the reference layer.

[0152] The layers of the electrolytic cell are stacked along the Z axis. The projections of the non-reference layer and the reference layer on the XY plane have a large number of structurally similar regions. However, due to the thickness difference of the components between the layers, the Z coordinates of the vertices are different, and direct matching is prone to spatial misalignment. Therefore, the spatial mapping relationship between the vertices of the non-reference layer and the reference layer is established by the fast nearest neighbor search algorithm KDTree.

[0153] Specifically, when the spatial mapping relationship between the vertices of the non-reference layer and the reference layer is established by the fast nearest neighbor search algorithm, the three-dimensional coordinates of all vertices of the reference layer are first constructed into a KD-tree index structure, and then each vertex of the non-reference layer is traversed. The projection of the vertex on the XY plane is taken as the search target, and the vertex with the closest coordinates and consistent structure attributes in the KD-tree of the reference layer is quickly found, and a mapping relationship between the two is established.

[0154] On the basis of the established spatial mapping relationship, the mapped temperature of each vertex of the non-reference layer is calculated according to the nearest neighbor point of the reference layer , and the calculation formula is:

[0155] ;

[0156] wherein is the correction amount based on interlayer gradient transmission, is the temperature of the vertex of the non-reference layer, is the temperature of the nearest neighbor point of the reference layer, is the temperature attenuation coefficient.

[0157] Lateral heat diffusion refers to the transmission of heat in the XY plane. The correction mainly involves the heat diffusion equation and the thermal diffusion coefficient of the material. When the temperature field distribution of the reference layer is corrected according to the lateral heat diffusion effect, a certain range of neighborhood is first defined in the XY plane for each vertex of the reference layer and the non-reference layer, and then the temperature mean value of all vertices in the neighborhood is calculated. Combined with the thermal diffusion coefficient and the preset time step, the correction value of the vertex temperature is determined.

[0158] The calculation formula of the correction value is:

[0159] ;

[0160] wherein is the correction value of the temperature of the vertex of the non-reference layer, is the initial value of the vertex temperature, i.e. the mapped temperature calculated, is the thermal diffusion coefficient, is the time step, ​is the average temperature of all vertices in the neighborhood, is the neighborhood radius.

[0161] By calculating the mapping temperature and the corrected value of the vertex temperature, the correction of the temperature field distribution of the reference layer is realized, so as to obtain the target temperature field distribution of the reference layer, that is, the global three-dimensional temperature field distribution of the electrolytic cell.

[0162] After determining the target temperature field distribution, it is visualized, and the target temperature field distribution is rendered to obtain a visualized image of the three-dimensional temperature field of the electrolytic cell, including:

[0163] Based on the multi-modal rendering engine, the target temperature field distribution of the reference layer is rendered to obtain the surface isotheral cloud chart and the three-dimensional volume rendering chart of the electrolytic cell.

[0164] Through the multi-modal rendering technology, the surface cloud chart and the volume field are synchronously output, the light projection is used to optimize and enhance the temperature gradient perception, Figure 5 is the flow chart of the visualization provided by the embodiment, as Figure 5 shown, the multi-modal rendering engine is used for visualizing the three-dimensional temperature field, and the surface isotheral cloud chart and the three-dimensional volume rendering chart are synchronously generated, the light projection algorithm is used to optimize the temperature gradient display, and the interactive scalar bar and the multi-plane cutting view are dynamically generated.

[0165] Specifically, the isothermal surface is extracted through the Marching Cubes algorithm, and the surface topology structure is optimized through the Laplacian smoothing, so as to realize the generation of the surface isotheral cloud chart and the three-dimensional volume rendering chart.

[0166] And a transparency transfer function is defined to realize the volume rendering optimization, and the expression of the transparency transfer function is:

[0167] ;

[0168] wherein, is a temperature-related transparency function, and T is a current temperature value.

[0169] At the same time, the color mapping interval is automatically adjusted according to the real-time temperature range, and the key temperature threshold is marked in the corresponding cloud chart and rendering chart.

[0170] Another aspect of the embodiment also provides an electrolytic cell three-dimensional temperature field reconstruction system based on multi-physical field coupling, as Figure 6 shown, including:

[0171] The acquisition module 1 is used for acquiring the three-dimensional model of the electrolytic cell, layering in the Z-axis direction, establishing a layering database according to the layering result, and setting a reference layer;

[0172] The first processing module 2 is configured to generate an initial temperature field distribution of the reference layer based on an anisotropic radial basis function interpolation algorithm.

[0173] The second processing module 3 is configured to sample the layered database to obtain corresponding sampling data.

[0174] The third processing module 4 is configured to input the sampling data into an electro-thermal-fluid coupling equation to solve the initial temperature field distribution of the reference layer and obtain a temperature field distribution of the reference layer.

[0175] The fourth processing module 5 is configured to layer-mapping the temperature field distribution of the reference layer along the Z-axis direction to a three-dimensional space, and correcting the temperature field distribution of the reference layer in combination with interlayer temperature attenuation and lateral thermal diffusion effect to obtain a target temperature field distribution of the reference layer.

[0176] The rendering module 6 is configured to render the target temperature field distribution of the reference layer to obtain a visual image of the three-dimensional temperature field of the electrolytic cell.

[0177] The acquisition module, the first processing module, the second processing module, the third processing module, the fourth processing module and the rendering module are all data processing components with corresponding processing algorithms, such as computers and microprocessors, and are multi-thread parallel computing architectures with built-in four-quadrant dynamic allocation algorithms and boundary guarantee point generation logic, developed based on the PyVista framework, supporting temperature field multi-modal rendering and interactive analysis, and all providing real-time data communication interfaces with external sensors and databases.

[0178] As shown in Figure 7 The electronic device 7 includes a memory 71, a processor 72, and a computer program stored in the memory and executable on the processor, and the processor implements each process of the above-mentioned multi-physical field coupling based electrolytic cell three-dimensional temperature field reconstruction method embodiment when executing the program, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0179] It should be noted that the electronic device in the embodiments of the present application includes the above-mentioned mobile electronic device and non-mobile electronic device.

[0180] The embodiments of the present application also provide a non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement each process of the above-mentioned multi-physical field coupling based electrolytic cell three-dimensional temperature field reconstruction method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0181] The processor is a processor in the electronic device in the above embodiments. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0182] The application also provides a computer program product, which comprises a computer program, and the computer program is executed by a processor to implement the above method for reconstructing a three-dimensional temperature field of an electrolytic cell based on multi-physical field coupling.

[0183] The processor is a processor in the electronic device in the above embodiments. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0184] The application further provides a chip, which comprises a processor and a communication interface. The communication interface is coupled with the processor. The processor is used to run programs or instructions to implement each process of the above method for reconstructing a three-dimensional temperature field of an electrolytic cell based on multi-physical field coupling, and the same technical effects can be achieved. To avoid repetition, details are not described herein.

[0185] It should be understood that the chip mentioned in the application can also be referred to as a device-level chip, a device chip, a chip device, or a system-on-chip device, etc.

[0186] The above embodiments are only a preferred scheme of the application, and do not limit the application in any form. Other variants and modifications can be made without departing from the technical scheme of the claims.

Claims

1. A method for reconstructing a three-dimensional temperature field of an electrolytic cell based on multi-physical field coupling, characterized in that, The application relates to a method for visualizing a three-dimensional temperature field of an electrolytic cell. The method comprises the following steps: a three-dimensional model of the electrolytic cell is layered along a Z-axis direction, a layered database is established according to a layered result, and a reference layer is set; an initial temperature field distribution of the reference layer is generated based on an anisotropic radial basis function interpolation algorithm; an electro-thermal-fluid coupling equation is established according to the initial temperature field of the reference layer, the layered database is sampled, the electro-thermal-fluid coupling equation is solved according to the sampling data, and a temperature field distribution of the reference layer is obtained; the temperature field distribution of the reference layer is layered and mapped to a three-dimensional space along the Z-axis direction, the temperature field distribution of the reference layer is corrected in combination with interlayer temperature attenuation and transverse thermal diffusion effects, and a target temperature field distribution of the reference layer is obtained; the target temperature field distribution is rendered, and a visualized image of the three-dimensional temperature field of the electrolytic cell is obtained. The method comprises the following steps: the three-dimensional model of the electrolytic cell is discretized into multiple two-dimensional sections along the Z-axis direction; the multiple two-dimensional sections are adaptively layered according to curvature analysis and normal vector clustering algorithms in combination with a vertex curvature adaptive layering strategy; three-dimensional coordinate data of each layer is extracted, a layered database is established, and a reference layer is selected from the layers; the initial temperature field distribution of the reference layer is generated based on the anisotropic radial basis function interpolation algorithm, which comprises the following steps: a main diffusion axis in the direction of the flow passage section is obtained through principal component analysis, and an anisotropic distance measurement function is established; the data in the layered database is spatially interpolated based on the anisotropic distance measurement function, and the initial temperature field distribution of the reference layer is generated in combination with a regularization parameter optimization; the temperature field distribution of the reference layer is obtained by solving the electro-thermal-fluid coupling equation according to the sampling data, which comprises the following steps: the electro-thermal-fluid coupling equation is discretized through a finite volume method, and an artificial diffusion term is introduced; 2. The multi-physical field coupling based reconstruction method of a three-dimensional temperature field of an electrolytic cell according to claim 1, characterized in that, a soft constraint boundary condition is set, the sampling data are taken as boundary inputs, the electro-thermal-fluid coupling equation after the discretization and the introduction of the artificial diffusion term is iteratively solved, and the temperature field distribution of the reference layer is obtained. The sampling of the layered database comprises the following steps: the reference layer is divided into four quadrants, the temperature gradient average values in the quadrants are calculated, and the weight of each quadrant is distributed according to the temperature gradient average values; the sampling point numbers of the quadrants are distributed based on the corresponding weights, and additional sampling points are generated in combination with a boundary protection mechanism; 3. The multi-physical field coupling based reconstruction method of a three-dimensional temperature field of an electrolytic cell according to claim 1, characterized in that, the layered database is sampled according to the corresponding sampling point numbers and the generated additional sampling points, and sampling data are obtained. The temperature field distribution of the reference layer is layered and mapped to the three-dimensional space along the Z-axis direction, the temperature field distribution of the reference layer is corrected in combination with interlayer temperature attenuation and transverse thermal diffusion effects, and the target temperature field distribution of the reference layer is obtained, which comprises the following steps: the temperature attenuation coefficient of the temperature field distribution of the reference layer along the Z-axis is calculated, and a spatial mapping relationship between non-reference layer vertices and the reference layer is established according to a fast nearest neighbor search algorithm; 4. The multi-physical field coupling based reconstruction method of a three-dimensional temperature field of an electrolytic cell according to claim 1, characterized in that, the temperature field distribution of the reference layer is corrected according to the temperature attenuation coefficient and the transverse thermal diffusion effect, and the target temperature layer distribution of the reference layer is obtained. The target temperature field distribution is rendered, and the visualized image of the three-dimensional temperature field of the electrolytic cell is obtained. The target temperature field distribution of the reference layer is rendered based on a multi-modal rendering engine, to obtain a surface isotherm cloud chart and a three-dimensional volume rendering chart of the electrolytic cell.

5. A system for reconstructing a three-dimensional temperature field of an electrolytic cell based on multi-physical field coupling, configured to perform the method for reconstructing a three-dimensional temperature field of an electrolytic cell based on multi-physical field coupling according to any one of claims 1 to 4, characterized in that, The method comprises the following steps: An acquisition module is configured to acquire a three-dimensional model of the electrolytic cell, and layer the three-dimensional model in the Z-axis direction, and establish a layering database according to the layering result and set a reference layer; A first processing module is configured to generate an initial temperature field distribution of the reference layer based on an anisotropic radial basis function interpolation algorithm; A second processing module is configured to sample the layering database to obtain corresponding sampling data; A third processing module is configured to input the sampling data into an electro-thermal-fluid coupling equation, solve the initial temperature field distribution of the reference layer, and obtain the temperature field distribution of the reference layer; A fourth processing module is configured to layer map the temperature field distribution of the reference layer to a three-dimensional space along the Z-axis direction, correct the temperature field distribution of the reference layer in combination with interlayer temperature attenuation and lateral thermal diffusion effects, and obtain a target temperature field distribution of the reference layer; A rendering module is configured to render the target temperature field distribution of the reference layer to obtain a visualized image of the three-dimensional temperature field of the electrolytic cell.

6. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the method for reconstructing a three-dimensional temperature field of an electrolytic cell based on multi-physical field coupling according to any one of claims 1 to 4.

7. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method for reconstructing a three-dimensional temperature field of an electrolytic cell based on multi-physical field coupling according to any one of claims 1 to 4.

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