Intelligent electric energy distribution cabinet heat flow field prediction method and system

Abstract

The invention provides an intelligent electric energy distribution cabinet heat flow field prediction method and system, and relates to the technical field of cabinet heat flow field prediction, and the method comprises the steps: carrying out the clustering analysis of the obtained operation data and material attributes of each region in an intelligent electric energy distribution cabinet, and the element layout information of equipment in the cabinet; respectively establishing a thermal-mechanical coupling model, a fluid dynamic model and an electromagnetic model; and according to the final refined grid model, simulating fluid dynamics by using a motion constraint smoothing particle fluid dynamics method so as to obtain an optimal layout scheme of the internal structure of the intelligent electric energy distribution cabinet. And calculating the optimal layout scheme by using an analog simulation technology to obtain an optimization scheme of air flow and heat transfer in the intelligent electric energy distribution cabinet, and realizing prediction of the heat flow field of the intelligent electric energy distribution cabinet. Accurate prediction and optimization of the heat flow field of the power distribution cabinet are realized, the reliability and safety of a power system are improved, and the accident risk and loss are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of cabinet thermal flow field prediction, and in particular to a method and system for predicting the thermal flow field of a smart power distribution cabinet. Background Art

[0002] In the field of smart grid, the intelligent and modern design of power distribution cabinets as a key component is crucial to improving the reliability and efficiency of power systems. With the continuous expansion of the scale of smart grid construction, the intelligence level of power systems is gradually improving. In this context, the intelligent and modern design of power distribution cabinets as an important part of smart grids has become particularly important.

[0003] However, the existing technology still has many problems in the intelligent design of distribution cabinets. Traditional distribution cabinets mostly use simple mechanical protection and monitoring, lacking the ability to monitor the operating status of the power system in real time and analyze data, resulting in the inefficient operation of electrical equipment in the power grid system. For example, experimental methods are often costly, time-consuming, and difficult to achieve continuous monitoring; while numerical simulations may lead to insufficient accuracy of prediction results due to improper model simplification and boundary condition settings. In addition, when dealing with complex thermal flow field problems, existing technologies often lack comprehensive consideration of the coupling effects of multiple physical fields, especially when dealing with challenges such as high line losses, poor reliability, and high pressure on new energy load management in distribution networks, such as the interaction between thermal coupling, fluid dynamics, and electromagnetism. Therefore, these problems limit the improvement of distribution cabinet performance optimization and fault prevention capabilities. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for predicting thermal flow field of a smart power distribution cabinet to improve the above problems. In order to achieve the above purpose, the technical solution adopted by the present invention is as follows: In a first aspect, the present application provides a method for predicting thermal flow field of a smart power distribution cabinet, comprising: The acquired operation data of each area inside the smart power distribution cabinet, material properties, and component layout information of the equipment inside the cabinet are clustered and analyzed to form a structured database as input parameters; the operation data includes temperature, flow and pressure data, the material properties include the type of plate and coating material, and the component layout information includes equipment layout, equipment function, cable connection and space utilization; The multi-physics simulation software is used to couple the structured database, and a thermal coupling model, a fluid dynamics model, and an electromagnetic model are established respectively. The three models representing different physical fields are coupled to obtain the simulation results of the thermal flow field and structural response. The thermal coupling model is used to simulate the influence of temperature changes on the structure of the smart power distribution cabinet, the fluid dynamics model is used to simulate the influence of the air flow driven by the fan inside the smart power distribution cabinet on the heat dissipation, and the electromagnetic model is used to simulate the influence of the electromagnetic field on the thermal flow field inside the cabinet. Based on the simulation results, the adaptive mesh refinement algorithm is used to preferentially insert set cells in the area with large temperature gradient, and then the mesh size is dynamically adjusted according to the error estimation to obtain the final refined mesh model; Based on the final refined mesh model, the motion-constrained smoothed particle fluid dynamics method is used to simulate fluid dynamics, and the stress distribution is determined through fluid-structure coupling analysis. Finally, the response surface method is used for multi-objective optimization to obtain the optimal layout of the internal structure of the smart power distribution cabinet. The optimal layout scheme is calculated and processed using simulation technology to obtain a grid model of the three-dimensional flow domain of the smart power distribution cabinet. Based on the preset boundary conditions, the grid model and the preset conditions are input into the Navier-Stokes equations and the energy equation to solve the velocity field, pressure field and temperature field inside the smart power distribution cabinet. After parameter optimization and sensitivity analysis, the optimization scheme for air flow and heat transfer inside the smart power distribution cabinet is obtained. Based on the optimization scheme, the fan speed and heat sink angle are adjusted to predict the thermal flow field of the smart power distribution cabinet.

[0005] Preferably, based on the simulation results, the set cells are preferentially inserted in the area with large temperature gradient through the adaptive mesh refinement algorithm, and then the mesh size is dynamically adjusted according to the error estimation to obtain the final refined mesh model, which includes: According to the multi-physics field coupling simulation results, the refinement area is determined through local functional error estimation and the error distribution map is obtained; Based on the error distribution graph, the refined units with large temperature gradients whose error estimation values ​​exceed a preset threshold are marked, and a refinement operation is performed on the marked refined units to obtain a preliminary refined mesh model, wherein the refinement operation includes inserting new nodes into the refined units and splitting the original units; The preliminary refined mesh model is optimized through the adaptive mesh refinement algorithm, the global error tolerance is set, and the sum of the local error indicators of all units is ensured to be equal to the global error tolerance. The mesh size is dynamically adjusted to obtain the final refined mesh model.

[0006] Preferably, the global error tolerance is set, and the sum of the local error indicators of all units is ensured to be equal to the global error tolerance, and the grid size is dynamically adjusted to obtain the final refined grid model, which includes: Set a global error tolerance tol for each unit K , calculate its local error index, where the local error index is calculated based on the residual in the finite element method; Ensure that the sum of the local error indicators of all units is equal to the global error tolerance tol; The mesh size is adjusted and refined to obtain the final refined mesh model, wherein the refinement process selects a small mesh size for the area with large errors and a large mesh size for the area with small errors.

[0007] Preferably, according to the final refined grid model, the motion-constrained smoothed particle fluid dynamics method is used to simulate fluid dynamics, and the stress distribution is determined by fluid-solid coupling analysis, and finally the response surface method is used for multi-objective optimization to obtain the optimal layout of the internal structure of the smart power distribution cabinet, which includes: According to the refined grid model, the fluid domain inside the cabinet is discretized into a set of smooth particle hydrodynamic particles, where each particle has mass and position ， After kernel function approximation processing, the approximate result of the continuous function value is obtained; Based on the approximate results of the continuous function value, the preliminary simulation results of the fluid dynamics inside the cabinet are obtained after smoothed particle fluid dynamics processing; According to the preliminary simulation results, after fluid-solid coupling analysis and processing, the data exchange between the fluid and solid interface inside the smart power distribution cabinet was obtained, and the physical information neural network model was trained to obtain the predicted data of fluid velocity, pressure, temperature and mass concentration. The predicted data was calculated through fluid-solid coupling stress distribution to obtain the fluid-solid coupling stress distribution inside the smart power distribution cabinet; The stress distribution is processed through multi-objective optimization based on the response surface method, the stress distribution under different design variables is predicted, and the optimal layout plan of the internal structure of the smart power distribution cabinet is obtained.

[0008] Preferably, the stress distribution is processed by multi-objective optimization based on the response surface method, the stress distribution under different design variables is predicted, and the optimal layout scheme of the internal structure of the smart power distribution cabinet is obtained, which includes: A response surface model is established, and after selecting sample points and conducting finite element analysis, a response surface model of stress distribution is obtained; The response surface model is iteratively calculated through non-dominated sorting and crowding distance in non-dominated sorting genetic algorithm to predict the stress distribution under different design variables and determine the optimal solution set. After optimal solution set analysis and processing, the optimal layout plan of the internal structure of the smart power distribution cabinet is obtained.

[0009] In a second aspect, the present application also provides a smart power distribution cabinet thermal flow field prediction system, including: Analysis module: used to cluster and analyze the acquired operation data of each area inside the smart power distribution cabinet, material properties, and component layout information of the equipment inside the cabinet to form a structured database as input parameters; the operation data includes temperature, flow and pressure data, the material properties include the type of plate and coating material, and the component layout information includes equipment layout, equipment function, cable connection and space utilization; Calculation module: used to use multi-physics field simulation software to perform coupling calculations on the structured database, establish a thermal coupling model, a fluid dynamics model, and an electromagnetic model, and couple the three models representing different physical fields to obtain simulation results of thermal flow field and structural response. The thermal coupling model is used to simulate the impact of temperature changes on the structure of the smart power distribution cabinet, the fluid dynamics model is used to simulate the impact of air flow driven by the fan inside the smart power distribution cabinet on heat dissipation, and the electromagnetic model is used to simulate the impact of the electromagnetic field on the thermal flow field inside the cabinet; Estimation module: Based on the simulation results, the adaptive mesh refinement algorithm is used to preferentially insert set cells in the area with large temperature gradient, and then dynamically adjust the mesh size according to the error estimation to obtain the final refined mesh model; Optimization module: used to simulate fluid dynamics using the motion-constrained smoothed particle fluid dynamics method based on the final refined grid model, determine stress distribution through fluid-solid coupling analysis, and finally apply the response surface method for multi-objective optimization to obtain the optimal layout of the internal structure of the smart power distribution cabinet; Prediction module: It is used to calculate and process the optimal layout plan using simulation technology, and obtain the grid model of the three-dimensional flow domain of the smart power distribution cabinet. Based on the preset boundary conditions, the grid model and the preset conditions are input into the Navier-Stokes equations and the energy equation to solve the velocity field, pressure field and temperature field inside the smart power distribution cabinet; and after parameter optimization and sensitivity analysis, the optimization plan for air flow and heat transfer inside the smart power distribution cabinet is obtained, and the fan speed and heat sink angle are adjusted based on the optimization plan to realize the prediction of the thermal flow field of the smart power distribution cabinet.

[0010] In a third aspect, the present application also provides a thermal flow field prediction device for a smart power distribution cabinet, comprising: Memory for storing computer programs; A processor is used to implement the steps of the thermal flow field prediction method of the intelligent power distribution cabinet when executing the computer program.

[0011] In a fourth aspect, the present application further provides a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned method for predicting the thermal flow field of a smart power distribution cabinet are implemented.

[0012] The beneficial effects of the present invention are: The present invention can more accurately predict the stress distribution under different design variables by combining the response surface model and the non-dominated sorting genetic algorithm. The response surface model provides an approximate stress distribution model based on the selection of sample points and finite element analysis processing, while the NSGA-II algorithm optimizes the selection of design variables through non-dominated sorting and crowding distance calculation, thereby improving the prediction accuracy; the multi-objective optimization capability of the NSGA-II algorithm can find the optimal solution set among multiple design objectives, not only considering a single performance indicator, but also integrating multiple objective functions, such as thermal flow field distribution, structural response and electromagnetic field influence, thereby realizing the optimal layout plan of the internal structure of the cabinet.

[0013] Through accurate thermal flow field prediction and structural response analysis, the present invention can identify and prevent potential thermal stress problems, reduce the risk of equipment failure due to overheating or structural failure, enhance the reliability and safety of the smart power distribution cabinet, and extend the service life of the equipment.

[0014] The present invention improves the air flow and heat transfer efficiency inside the cabinet by optimizing design parameters such as fan speed and heat sink angle, which helps to more effectively dissipate the heat generated by the equipment and maintain the uniformity and stability of the temperature inside the cabinet; it allows real-time monitoring and dynamic adjustment of the heat flow field inside the cabinet, and through the rapid prediction capability of the response surface model, the thermal management strategy can be adjusted in time to adapt to the changing heat load and operating conditions.

[0015] The present invention comprehensively considers the influence of multiple physical fields such as thermal coupling, fluid dynamics and electromagnetism, and simultaneously considers the influence of the electromagnetic field on the thermal flow field. By integrating electromagnetic models, the electromagnetic compatibility inside the cabinet is improved and the influence of electromagnetic interference on equipment performance is reduced.

[0016] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 It is a schematic diagram of the process flow of the thermal flow field prediction method of the intelligent power distribution cabinet described in an embodiment of the present invention; Figure 2 It is a schematic diagram of the structure of the thermal flow field prediction system of the intelligent power distribution cabinet described in an embodiment of the present invention; Figure 3 It is a schematic diagram of the structure of the thermal flow field prediction device of the intelligent power distribution cabinet described in an embodiment of the present invention.

[0019] In the figure: 701, analysis module; 702, calculation module; 703, estimation module; 704, optimization module; 705, prediction module; 800, thermal flow field prediction device of smart power distribution cabinet; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0021] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0022] Embodiment 1:

[0023] This embodiment provides a method for predicting thermal flow fields of a smart power distribution cabinet.

[0024] See also Figure 1 , the figure shows that the method includes step S100, step S200, step S300, step S400 and step S500.

[0025] S100. Perform cluster analysis on the acquired operation data of each area inside the smart power distribution cabinet, material properties, and component layout information of the equipment inside the cabinet to form a structured database as input parameters; the operation data includes temperature, flow, and pressure data, the material properties include plate type and coating material, and the component layout information includes equipment layout, equipment function, cable connection, and space utilization.

[0026] It is understandable that in this step, it should be noted that temperature data refers to the actual temperature readings of various areas inside the cabinet; flow data: involves the rate of air flow inside the cabinet, especially the effect of air flow driven by the fan on heat dissipation; pressure data: may involve pressure changes inside the cabinet, especially in a closed or semi-closed cabinet environment, pressure changes may affect cooling efficiency. Material properties include plate types: such as aluminum profiles or cold-rolled steel plates, etc. The thermal conductivity, heat resistance and mechanical strength of these materials have a direct impact on the heat dissipation and structural stability of the cabinet, coating materials: coating materials on the surface of the cabinet, such as orange grain, fine sand grain, etc., these coatings may affect the thermal radiation and appearance durability of the cabinet, and processing technology: such as 9-fold profile cabinets and 16-fold cabinets, etc., different processing technologies affect the structural stability and heat dissipation performance of the cabinet. In addition, the component layout information includes the installation layout of the equipment inside the cabinet, such as the distribution of AC distribution units, patch panels, active equipment, optical cable terminal boxes, etc. The equipment is arranged according to the function and cable connection needs, such as the arrangement order of switches, routers, and bridges, and in terms of space utilization, large equipment is installed at the bottom of the cabinet, and small equipment such as optical transceivers may be installed on trays, all of which affect the distribution of thermal flow fields inside the cabinet. By classifying and organizing this data, detailed input parameters can be provided for subsequent multi-physics field coupling simulation calculations, thereby more accurately simulating and predicting the thermal flow field and structural response inside the cabinet.

[0027] S200. Use multi-physics field simulation software to perform coupling calculations on the structured database, establish a thermal coupling model, a fluid dynamics model, and an electromagnetic model, and couple the three models representing different physical fields to obtain simulation results of the thermal flow field and structural response. The thermal coupling model is used to simulate the impact of temperature changes on the structure of the smart power distribution cabinet, the fluid dynamics model is used to simulate the impact of fan-driven air flow inside the smart power distribution cabinet on heat dissipation, and the electromagnetic model is used to simulate the impact of the electromagnetic field on the thermal flow field inside the cabinet.

[0028] It should be noted that the thermal flow field simulation results describe the heat distribution and flow inside the cabinet, including parameters such as temperature distribution, heat flux, and heat flux density, which describe how heat propagates and distributes inside the cabinet; while the structural response simulation results describe the physical responses of the cabinet structure under the influence of heat, such as deformation, stress, and displacement. Due to temperature changes, the metal parts inside the cabinet may expand thermally, which may cause structural deformation or stress concentration, affecting the stability and life of the cabinet. In other words, in short, it is based on three independent physical field models, through coupling and simulation calculations, to finally obtain the simulation results of the thermal flow field and structural response inside the cabinet.

[0029] In this step, it should be noted that the thermal coupling model is based on the heat conduction equation and the stress field equation. Each unit satisfies the heat conduction and mechanical equilibrium equations. Through the finite element method, these equations are converted into a solution format to obtain the equivalent conduction matrix, heat capacity matrix and equivalent node heat flow vector of the unit, as well as the stiffness matrix, node load and node temperature load; the fluid dynamics model uses the fluid dynamics model to simulate the air flow inside the cabinet through control equations such as the continuity equation, momentum equation (Navier-Stokes equation) and energy conservation equation, describes the flow characteristics and energy exchange of the fluid, and is discretized and solved through numerical methods such as the finite difference method and the finite element method; the electromagnetic model uses Maxwell's equations to describe the distribution and changes of the electromagnetic field. In COMSOL Multiphysics, the electromagnetic field interface can be used to simulate the electromagnetic field inside the cabinet, considering the influence of the electromagnetic field generated by the electromagnetic components on the thermal flow field.

[0030] In summary, by coupling the above three models, through the simulation environment provided by the "Model Builder", it is easy to simulate electromagnetic, structural, acoustic, fluid, heat transfer and other physical phenomena, and flexibly couple multiple physical phenomena in a single model. During the coupling process, the variables of one physical field are input to the corresponding position of another physical field to achieve data interaction and influence. The simulation results include temperature distribution inside the cabinet, fluid velocity and pressure distribution, electromagnetic field strength, etc. These results comprehensively reflect the thermal flow field state inside the cabinet, and its structural response includes deformation, stress and displacement caused by thermal expansion, etc. This information is crucial for evaluating the structural integrity and reliability of the cabinet.

[0031] It can be understood that the step S200 includes: Based on the operation data of each area inside the smart power distribution cabinet, the calculation formula for establishing the thermal coupling model is as follows: In the formula, M is the material density, is the specific heat capacity, Tis temperature, t is time, k is thermal conductivity, and Q is the internal heat source; The calculation formula for establishing the fluid dynamics model for simulating the air flow driven by the fan inside the cabinet is as follows:

[0032] In the formula, ρ is the fluid density, t is the time, z is the fluid velocity vector, and ∇⋅ is the divergence operator, which indicates the size of the source or sink of the vector field; For pressure, is the dynamic viscosity, f is the volume force vector; The calculation formula for establishing an electromagnetic model that simulates the influence of electromagnetic fields on the thermal flow field inside the cabinet is as follows:

[0033] Where D is the electric displacement field, is the free charge density, E is the electric field, and B is the magnetic field.

[0034] The three models are coupled to obtain simulation results of thermal flow field and structural response. The coupling process involves synchronization of data exchange and boundary conditions to ensure that the interaction between physical fields is properly handled. For example, the heat flux boundary condition in the fluid dynamics model may depend on the temperature distribution provided by the thermal coupling model, while the electromagnetic force in the electromagnetic model may affect the flow field in the fluid dynamics model. Through this coupling, the complex physical phenomena inside the cabinet can be simulated and its performance can be predicted. The simulation results include temperature distribution, velocity field, pressure distribution, and electromagnetic field strength, etc., providing a scientific basis for cabinet design and performance optimization.

[0035] Specifically, in the coupling calculation method, strong coupling and weak coupling calculations are involved, among which strong coupling (tight coupling) is suitable for theoretical analysis of coupling fields. The coupling effect is constructed into the control equation through unit matrix or load vector, and then the control equation is directly solved; while weak coupling (loose coupling) is suitable for numerical calculation of coupling fields. This method solves the fluid dynamic equation and the structural dynamic equation once in each step, and realizes the coupling of the two fields by applying the result of the first physical field as an external load to the second physical field. For weak coupling, its advantage is that the existing general fluid and structural software can be reused, and suitable solution methods can be formulated for each software separately. It should be noted that in the coupling calculation process, data exchange and iterative solution are required. The pressure or shear stress calculated by the fluid dynamics model (CFD) is used as the input load of the thermal coupling model. The structural displacement calculated by the thermal coupling model is used to correct the geometric boundary of the structure in the fluid domain according to this displacement. This process is solved iteratively between CFD and structural analysis (CSD) until it converges in each load step. Then the thermal coupling is analyzed. It is necessary to analyze the coupling of thermal temperature field and structural statics. It is necessary to analyze the three major heat transfer modes of thermodynamics (heat conduction, heat convection, and heat radiation). The analysis needs to consider the influence of temperature difference stress caused by different thermal expansion of the shell and tube due to temperature difference on the deformation coordination of tube sheet, heat exchange tube, tube sheet and tube box or shell cylinder. In addition, the electromagnetic model simulates the influence of electromagnetic field on the thermal flow field inside the cabinet. It is based on Maxwell's equations. The degree of interaction between charge and current in the electromagnetic field is described by coupling constant. After considering many factors, the simulation result is finally obtained through calculation.

[0036] In summary, the simulation results obtained through coupling calculation include: 1. Thermal flow field distribution: including temperature distribution, heat flux, etc., reflecting the thermal state inside the cabinet; 2. Structural response: including deformation and stress caused by thermal expansion or other thermal effects; 3. Fluid dynamics response: including velocity field, pressure field, etc., describing the air flow inside the cabinet; 4. Electromagnetic field response: including the distribution of electric and magnetic fields, and their potential impact on the thermal flow field.

[0037] S300, based on the simulation results, the set cells are preferentially inserted into the area with large temperature gradient through the adaptive mesh refinement algorithm, and then the mesh size is dynamically adjusted according to the error estimation to obtain the final refined mesh model.

[0038] It should be noted that in this step, the thermal flow field changes dramatically in places with large temperature gradients, requiring higher-precision simulation results. Therefore, this can be achieved through an adaptive mesh refinement algorithm, which dynamically adjusts the mesh size based on the error estimate. The local mesh refinement setting means that a smaller mesh size is used in these key areas, and thus in this scenario.h To capture more subtle changes in the thermal flow field and thus improve the simulation accuracy, the optimized mesh model will be used for subsequent simulation calculations to obtain more accurate thermal flow field and structural response results. Mesh optimization can be achieved by adjusting the mesh size, shape, and connection relationship to ensure that the mesh has good computing performance while maintaining high accuracy.

[0039] It can be understood that step S300 includes S301, S302 and S303, wherein: S301, according to the multi-physics field coupling simulation results, after local functional error estimation processing, determine the refinement area and obtain the error distribution map, the calculation formula of which is as follows:

[0040] In the formula, E ( h ) is the error estimate, is the numerical solution, u is the exact solution, Ω is the solution domain, h is the grid size, dx is a differential element; It is understood that this step mainly calculates the local error estimate for each element. This is usually based on the residual in the finite element method or the gradient-based error estimation method. For example, in thermal flow field analysis, the error estimate can be expressed as the local rate of change of the temperature gradient, which is used to identify areas with large temperature gradients.

[0041] S302, based on the error distribution graph, marking the refined units with large temperature gradients whose error estimation values ​​exceed a preset threshold, and performing a refinement operation on the marked refined units to obtain a preliminary refined mesh model, wherein the refinement operation includes inserting new nodes into the refined units and splitting the original units; It is understood that for the marked elements, refinement operations are performed. This usually involves inserting new nodes in these elements and splitting the original elements into smaller elements. For example, in thermal flow field analysis, the h-adaptive method can be used to subdivide the elements into smaller elements to improve accuracy. Currently, it is only applicable to shell elements and is mainly used for metal forming simulation, compressive buckling of thin-walled structures and other problems.

[0042] S303, optimizing the preliminary refined mesh model through an adaptive mesh refinement algorithm, setting a global error tolerance, ensuring that the sum of the local error indicators of all units is equal to the global error tolerance, dynamically adjusting the mesh size, and obtaining a final refined mesh model.

[0043] In this step, the mesh is optimized to ensure that the mesh quality meets the simulation requirements, such as avoiding mesh distortion or overly slender cells. The optimized mesh model will be used for subsequent simulation calculations to obtain more accurate thermal flow field and structural response results. During the refinement process, the mesh size is dynamically adjusted according to the error estimate, which means that in areas with large errors, a smaller unit size is used to improve accuracy; in areas with small errors, the unit size can be appropriately increased to reduce the amount of calculation. Mesh optimization can be achieved by adjusting the mesh size, shape, and connection relationship to ensure that the mesh has good computing performance while maintaining high accuracy.

[0044] In this step S303, including S3031, S3032 and S3033, wherein: S3031. Set a global error tolerance tol for each unit. K , calculate its local error index, where the local error index is calculated based on the residual in the finite element method, and its calculation formula is as follows:

[0045] In the formula, For the K The local error index of each unit, C is a constant, For the K The diameter of the unit, that is, the size of the unit, For the K The H^2 norm of the exact solution on each element; S3032. The calculation formula for ensuring that the sum of the local error indicators of all units is equal to the global error tolerance tol is as follows:

[0046] In the formula, ∑ is a series of terms accumulated. K For each element in the model, For the K The local error index of each unit, tol is the global error tolerance; S3033, adjusting the mesh size, and refining the adjusted mesh size to obtain a final refined mesh model, wherein the refinement process selects a small mesh size for a region with a large error, and selects a large mesh size for a region with a small error, and the calculation formula is as follows:

[0047] In the formula, For the K The diameter of the unit, ∝ is the scale symbol, For function V In the unit KThe H^2 norm on , V is the temperature distribution.

[0048] In this step, this means that the maximum diameter of the element is inversely proportional to the H^2 norm of the solution. In areas with larger errors, a smaller mesh size is used; in areas with smaller errors, a larger mesh size can be used to ensure that more detailed thermal flow field changes can be captured in key areas (such as areas with large temperature gradients), while maintaining an appropriate mesh size in other areas to optimize computational efficiency.

[0049] S400, based on the final refined mesh model, uses the motion constrained smoothed particle fluid dynamics method to simulate fluid dynamics, and determines the stress distribution through fluid-solid coupling analysis. Finally, the response surface method is used for multi-objective optimization to obtain the optimal layout plan for the internal structure of the smart power distribution cabinet.

[0050] It can be understood that the step S400 includes S401, S402, S403 and S404, wherein: S401. According to the refined grid model, the fluid domain inside the cabinet is discretized into a group of smooth particle fluid dynamic particles, each of which has mass and position. After kernel function approximation processing, an approximate result of the continuous function value is obtained, and the calculation formula is as follows:

[0051] In the formula, is the kernel function, L is the smooth length, For the location x Function f The smoothed or average value of For all particles j The sum of For the j The mass of the particle, For the j The density of particles, For the j Particle positions The original function value at ; S402, based on the approximate result of the continuous function value, a preliminary simulation result of the fluid dynamics inside the cabinet is obtained through smoothed particle fluid dynamics processing; In this step, the motion-constrained smoothed particle fluid dynamics method is used to simulate the air flow inside the cabinet. This is a Lagrangian description method that is particularly suitable for dealing with fluid dynamics problems with complex boundary conditions. Among them, the motion-constrained smoothed particle fluid dynamics discretizes the Navier-Stokes equations, uses smoothed particle fluid dynamics particles to represent the fluid, and uses the Newton-Euler equations to describe the movement of the particles. In this scenario, it simulates the effect of fan-driven air flow on heat dissipation, as well as the interaction between the fluid and the internal structure of the cabinet.

[0052] It should be noted that, based on the results of kernel function approximation, we use the smoothed particle fluid dynamics method to express the fluid dynamics equations inside the cabinet, especially the momentum equation and the continuity equation. In this scenario, after the smoothed particle fluid dynamics expression, the updated formulas for particle velocity and pressure are obtained, and the fluid-solid coupling problem inside the cabinet is further processed based on the fluid dynamics equations expressed by smoothed particle fluid dynamics. After kinematic constraint processing, the coupling conditions between the fluid and the solid boundary are obtained. Among them, for incompressible fluids, the pressure Poisson equation is solved by the projection method to couple the velocity and pressure, and the calculation formula is as follows:

[0053] in, is the pressure at the current position, is the reference pressure, ρ is the density at the current location, is the reference density, γ is the specific heat ratio; S403. According to the preliminary simulation results, after fluid-solid coupling analysis and processing, the data exchange between the fluid and solid interface inside the smart power distribution cabinet is obtained, and the physical information neural network model is trained to obtain the predicted data of the fluid velocity, pressure, temperature and mass concentration. The predicted data is calculated through the fluid-solid coupling stress distribution to obtain the fluid-solid coupling stress distribution inside the smart power distribution cabinet; It should be noted that in this step, the Import ANSYS CDB Surface step is used to import the structural surface mesh and map it with the corresponding surface in the fluid analysis to ensure that the heat flow and pressure loads calculated by the fluid can be transferred to the solid structure, and vice versa; then in the physical information neural network model, the input includes the spatial position (x, y) and time (t) of the fluid, and the output is the physical quantity of the fluid. The loss function consists of the physical equation residual, boundary conditions, and the continuity equation of the fluid; according to the output of the physical information neural network model, the fluid-solid coupling stress distribution is calculated and processed to obtain the fluid-solid coupling stress distribution inside the cabinet. In this step, the Export ANSYS Load File step is used to export the fluid load data file, including key parameters such as heat flow and temperature, and combined with the response of the solid structure, the stress distribution at the interface between the fluid and the solid is calculated to provide accurate data support for the structural design and thermal management of the cabinet.

[0054] That is, bilateral kinematic constraints are used to enforce the incompressibility of the fluid, and unilateral kinematic constraints are used to handle the coupling between the fluid and solid structures (such as cabinet walls and heat sinks). This involves transferring the boundary conditions of the solid to the fluid region to ensure that the interaction between the fluid and the solid is correctly simulated. The data types used in this step include the velocity field, pressure field of the fluid, and displacement field of the solid, which are all data unique to the motion-constrained smoothed particle fluid dynamics simulation.

[0055] S404. The stress distribution is processed through multi-objective optimization based on the response surface method, the stress distribution under different design variables is predicted, and the optimal layout plan of the internal structure of the smart power distribution cabinet is obtained.

[0056] It should be noted that a response surface model is constructed to approximate the complex stress distribution function obtained in the fluid-solid coupling analysis. The response surface method is a statistical method. It selects a set of sample points in the design space and simulates them. Then, these results are used to construct an approximate response surface (usually a polynomial model). This response surface model can be used to predict the stress distribution under different design variables. The response surface method (RSM) is used to simulate the implicit relationship between design variables and performance indicators by constructing a fitting proxy model, and optimize the design parameters to achieve the goals of minimum maximum overall deformation, maximum natural frequency and lightest mass, so as to find the best layout solution in the design space. The algorithm used in this step combines statistical principles with multi-physics field knowledge of fluid dynamics and solid mechanics to creatively solve the structural optimization problem.

[0057] This step S404 includes S4041, S4042 and S4043, wherein: S4041. Establish a response surface model. After selecting sample points and conducting finite element analysis, a response surface model of stress distribution is obtained. The calculation formula is as follows:

[0058] Where y is the stress distribution, f(x) is the function of the design variable vector x, is the error term; S4042. The response surface model is iteratively calculated by the non-dominated sorting and crowding distance in the non-dominated sorting genetic algorithm to predict the stress distribution under different design variables and determine the optimal solution set. The calculation formula is as follows:

[0059] In the formula, is the objective function vector, fi (x) is i An objective function, x is the design variable vector; S4043. After the optimal solution set analysis and processing, the optimal layout scheme of the internal structure of the smart power distribution cabinet is obtained, and its calculation formula is as follows:

[0060] In the formula, is the optimal design variable vector, is the set of all possible design variable vectors x, and argmin is the parameter value that minimizes the objective function.

[0061] It can be understood that in step S4041, in the prediction of the thermal flow field of the smart power distribution cabinet, the establishment of the response surface model is achieved by selecting sample points and finite element analysis. The technical effect of this step is that it can approximate the complex thermal flow field behavior inside the entire cabinet through limited sample points. Specifically, first, a series of design variables (such as material properties, structural dimensions, etc.) and their corresponding stress distribution results are obtained through experiments or simulations. These data points are used to construct a mathematical model, namely a response surface model, which can predict the stress distribution inside the cabinet under different design variables. In step S4042, after obtaining the response surface model, the non-dominated sorting genetic algorithm (NSGA-II) is used for iterative calculation to predict the stress distribution under different design variables and determine the optimal solution set. The NSGA-II algorithm is a multi-objective optimization algorithm that maintains the diversity of the population through non-dominated sorting and crowding distance calculations and finds the Pareto optimal solution set. Among them, the iterative calculation process of the algorithm involves the fitness evaluation of each individual in the population, including their performance on multiple objective functions and their positional relationship in the solution space. In this step, the optimal design solution that balances multiple performance indicators can be identified, providing a comprehensive optimization direction for the structural design of the cabinet; in step S4043, after the iterative calculation of the NSGA-II algorithm, a solution set containing multiple Pareto optimal solutions is obtained, and these optimal solutions are analyzed and processed to determine the optimal layout solution of the internal structure of the smart power distribution cabinet, and one or several most feasible design solutions in practical applications can be selected from multiple optimal solutions. Therefore, by evaluating the feasibility of these solutions in actual manufacturing, cost, performance, etc., the optimal design variable vector x∗ is finally determined. This vector represents the parameter value that can make the objective function F(x) obtain the minimum value among all possible design variable vectors X, which not only involves the optimization of the solution, but also includes the consideration of the practicality and feasibility of the solution. In actual research, the application of these methods can be further extended to areas such as dynamic thermal management, fault diagnosis and life prediction of cabinets, providing more comprehensive technical support for the design and operation and maintenance of smart power distribution cabinets.

[0062] S500. Calculate and process the optimal layout plan using simulation technology to obtain a grid model of the three-dimensional flow domain of the smart power distribution cabinet. Based on preset boundary conditions, input the grid model and preset conditions into the Navier-Stokes equations and the energy equation to solve the velocity field, pressure field and temperature field inside the smart power distribution cabinet. After parameter optimization and sensitivity analysis, obtain an optimization plan for air flow and heat transfer inside the smart power distribution cabinet. Adjust the fan speed and heat sink angle based on the optimization plan to predict the thermal flow field of the smart power distribution cabinet.

[0063] It is understandable that in this step, advanced mesh generation techniques such as adaptive mesh refinement (AMR) are used to ensure higher mesh density in key areas (such as near the heat sink and fan area) to capture local flow details. The finite volume method (FVM) is then used to discretize the control equations, and appropriate numerical methods (such as the SIMPLE algorithm) are used to couple the solution of the pressure and velocity fields. For heat transfer, the energy equation can be used to calculate the temperature distribution, considering three heat transfer mechanisms: convection, conduction, and radiation. Then a multi-objective optimization algorithm (such as a genetic algorithm) is used to find the best combination of fan speed and heat sink angle to minimize temperature deviation and maximize heat dissipation efficiency. The flow field characteristics and thermal field characteristics inside the cabinet need to be considered during the optimization process. These data types are unique to this scenario, such as local eddy current intensity and temperature gradient in hot spot areas. Therefore, through these three steps, not only can the thermal flow field of the smart power distribution cabinet be predicted, but also specific optimization solutions and adjustment strategies can be provided to achieve the optimization of cabinet thermal management, reflecting the creativity and effectiveness of the application of computational fluid dynamics simulation technology in complex electromechanical systems.

[0064] Embodiment 2:

[0065] like Figure 2 As shown, this embodiment provides a smart power distribution cabinet thermal flow field prediction system, see Figure 2 The system comprises: Analysis module 701: used to perform cluster analysis on the acquired operation data of each area inside the smart power distribution cabinet, material properties, and component layout information of the equipment inside the cabinet, to form a structured database as input parameters; wherein the operation data includes temperature, flow and pressure data, the material properties include the type of plate and coating material, and the component layout information includes equipment layout, equipment function, cable connection and space utilization; Calculation module 702: used to perform coupling calculation on the structured database using multi-physics field simulation software, respectively establish a thermal coupling model, a fluid dynamics model and an electromagnetic model, and couple the three models representing different physical fields to obtain simulation results of thermal flow field and structural response, wherein the thermal coupling model is used to simulate the influence of temperature change on the structure of the smart power distribution cabinet, the fluid dynamics model is used to simulate the influence of air flow driven by the fan inside the smart power distribution cabinet on heat dissipation, and the electromagnetic model is used to simulate the influence of the electromagnetic field on the thermal flow field inside the cabinet; Estimation module 703: used to insert set cells preferentially in the area with large temperature gradient through adaptive mesh refinement algorithm based on simulation results, and then dynamically adjust the mesh size according to error estimation to obtain the final refined mesh model; Optimization module 704: used to simulate fluid dynamics using a motion-constrained smoothed particle fluid dynamics method based on the final refined grid model, determine stress distribution through fluid-solid coupling analysis, and finally apply a response surface method to perform multi-objective optimization to obtain an optimal layout solution for the internal structure of the smart power distribution cabinet; Prediction module 705: used to calculate and process the optimal layout plan using simulation technology, obtain the grid model of the three-dimensional flow domain of the smart power distribution cabinet, and based on the preset boundary conditions, input the grid model and the preset conditions into the Navier-Stokes equations and the energy equation to solve the velocity field, pressure field and temperature field inside the smart power distribution cabinet; and after parameter optimization and sensitivity analysis, obtain the optimization plan for air flow and heat transfer inside the smart power distribution cabinet, adjust the fan speed and heat sink angle based on the optimization plan, and then realize the prediction of the thermal flow field of the smart power distribution cabinet.

[0066] Specifically, the estimation module 703 includes: Determination unit: It is used to determine the refinement area and obtain the error distribution map according to the multi-physics field coupling simulation results after local functional error estimation. The calculation formula is as follows:

[0067] In the formula, E ( h ) is the error estimate, is the numerical solution, u is the exact solution, Ω is the solution domain, h is the grid size, dx is a differential element; Marking refined units: used to mark the refined units with large temperature gradients whose error estimation values ​​exceed the preset threshold value based on the error distribution map, and perform refinement operations on the marked refined units to obtain a preliminary refined mesh model, wherein the refinement operation includes inserting new nodes into the refined units and splitting the original units; Optimization processing unit; used to optimize the preliminary refined mesh model through the adaptive mesh refinement algorithm, set the global error tolerance, and ensure that the sum of the local error indicators of all units is equal to the global error tolerance, dynamically adjust the mesh size, and obtain the final refined mesh model.

[0068] Specifically, the optimization processing unit includes: Setting unit: used to set a global error tolerance tol for each unit K , calculate its local error index, where the local error index is calculated based on the residual in the finite element method, and its calculation formula is as follows:

[0069] In the formula, For the K The local error index of each unit, C is a constant, For the K The diameter of the unit, that is, the size of the unit, For the K The H^2 norm of the exact solution on each element; Calculation unit: The calculation formula used to ensure that the sum of the local error indicators of all units is equal to the global error tolerance tol is as follows:

[0070] In the formula, ∑ is a series of terms accumulated. K For each element in the model, For the K The local error index of each unit, tol is the global error tolerance; Adjustment unit: used to adjust the mesh size and refine the adjusted mesh size to obtain the final refined mesh model. The refinement process selects a small mesh size for areas with large errors and a large mesh size for areas with small errors. The calculation formula is as follows:

[0071] In the formula, For the K The diameter of the unit, ∝ is the scale symbol, For function V In the unit K The H^2 norm on , V is the temperature distribution.

[0072] Specifically, the optimization module 704 includes: The first processing unit is used to discretize the fluid domain inside the cabinet into a group of smooth particle fluid dynamic particles according to the refined grid model, where each particle has mass and position. After kernel function approximation processing, the approximate result of the continuous function value is obtained. The calculation formula is as follows:

[0073] In the formula, is the kernel function, L is the smooth length, For the location x Function f The smoothed or average value of For all particles j The sum of For the j The mass of the particle, For thej The density of particles, For the j Particle positions The original function value at ; The second processing unit is used to obtain preliminary simulation results of the fluid dynamics inside the cabinet based on the approximate results of the continuous function value through smoothed particle fluid dynamics processing; The third processing unit is used to obtain the data exchange of the fluid and solid interface inside the smart power distribution cabinet through fluid-solid coupling analysis and processing based on the preliminary simulation results, and obtain the predicted data of the fluid velocity, pressure, temperature and mass concentration through training through the physical information neural network model, and calculate the predicted data through fluid-solid coupling stress distribution to obtain the stress distribution of fluid-solid coupling inside the smart power distribution cabinet; The fourth processing unit is used to process the stress distribution through multi-objective optimization based on the response surface method, predict the stress distribution under different design variables, and obtain the optimal layout plan of the internal structure of the smart power distribution cabinet.

[0074] Specifically, the optimization module 704 includes: Establishment unit: used to establish the response surface model. After selecting sample points and conducting finite element analysis, the response surface model of stress distribution is obtained. The calculation formula is as follows:

[0075] Where y is the stress distribution, f(x) is the function of the design variable vector x, is the error term; Iterative calculation unit: It is used to iteratively calculate the response surface model through non-dominated sorting and crowding distance in the non-dominated sorting genetic algorithm, predict the stress distribution under different design variables, and determine the optimal solution set. The calculation formula is as follows:

[0076] In the formula, is the objective function vector, fi (x) is i An objective function, x is the design variable vector; Analysis and processing unit: used to obtain the optimal layout plan of the internal structure of the smart power distribution cabinet through optimal solution set analysis and processing. The calculation formula is as follows:

[0077] In the formula, is the optimal design variable vector, is the set of all possible design variable vectors x, and argmin is the parameter value that minimizes the objective function.

[0078] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0079] Embodiment 3:

[0080] Corresponding to the above method embodiment, this embodiment also provides a smart power distribution cabinet thermal flow field prediction device. The smart power distribution cabinet thermal flow field prediction device described below and the smart power distribution cabinet thermal flow field prediction method described above can refer to each other.

[0081] Figure 3 FIG. 8 is a block diagram of a thermal flow field prediction device 800 for a smart power distribution cabinet according to an exemplary embodiment. Figure 3 As shown, the smart power distribution cabinet thermal flow field prediction device 800 includes: a processor 801 and a memory 802. The smart power distribution cabinet thermal flow field prediction device 800 also includes one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0082] The processor 801 is used to control the overall operation of the smart power distribution cabinet thermal flow field prediction device 800 to complete all or part of the steps in the above-mentioned smart power distribution cabinet thermal flow field prediction method. The memory 802 is used to store various types of data to support the operation of the smart power distribution cabinet thermal flow field prediction device 800. These data may include, for example, instructions for any application or method operating on the smart power distribution cabinet thermal flow field prediction device 800, as well as application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 802 or sent via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules may be a keyboard, a mouse or buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the smart power distribution cabinet thermal flow field prediction device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 may include: a Wi-Fi module, a Bluetooth module or an NFC module.

[0083] In an exemplary embodiment, the smart power distribution cabinet thermal flow field prediction device 800 can be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, referred to as ASIC), digital signal processors (Digital Signal Processing Device, referred to as DSP), digital signal processing devices (Digital Signal Processing Device, referred to as DSPD), programmable logic devices (Programmable Logic Device, referred to as PLD), field programmable gate arrays (Field Programmable Gate Array, referred to as FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned smart power distribution cabinet thermal flow field prediction method.

[0084] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned smart power distribution cabinet thermal flow field prediction method are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions can be executed by the processor 801 of the smart power distribution cabinet thermal flow field prediction device 800 to complete the above-mentioned smart power distribution cabinet thermal flow field prediction method.

[0085] Embodiment 4:

[0086] Corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment. The readable storage medium described below and the smart power distribution cabinet thermal flow field prediction method described above can refer to each other.

[0087] A computer program is stored on the readable storage medium, and when the computer program is executed by the processor, the steps of the method for predicting the thermal flow field of the intelligent power distribution cabinet in the above method embodiment are implemented.

[0088] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other readable storage medium that can store program codes.

[0089] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0090] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for predicting thermal flow field of smart power distribution cabinets. It is characterized in that include: The acquired operation data of each area inside the smart power distribution cabinet, material properties, and component layout information of the equipment inside the cabinet are clustered and analyzed to form a structured database as input parameters; the operation data includes temperature, flow and pressure data, the material properties include the type of plate and coating material, and the component layout information includes equipment layout, equipment function, cable connection and space utilization; The multi-physics simulation software is used to couple the structured database, and a thermal coupling model, a fluid dynamics model, and an electromagnetic model are established respectively. The three models representing different physical fields are coupled to obtain the simulation results of the thermal flow field and structural response. The thermal coupling model is used to simulate the influence of temperature changes on the structure of the smart power distribution cabinet, the fluid dynamics model is used to simulate the influence of the air flow driven by the fan inside the smart power distribution cabinet on the heat dissipation, and the electromagnetic model is used to simulate the influence of the electromagnetic field on the thermal flow field inside the cabinet. Based on the simulation results, the adaptive mesh refinement algorithm is used to preferentially insert set cells in the area with large temperature gradient, and then the mesh size is dynamically adjusted according to the error estimation to obtain the final refined mesh model; Based on the final refined mesh model, the motion-constrained smoothed particle fluid dynamics method is used to simulate fluid dynamics, and the stress distribution is determined through fluid-structure coupling analysis. Finally, the response surface method is used for multi-objective optimization to obtain the optimal layout of the internal structure of the smart power distribution cabinet. The best layout scheme is calculated and processed by simulation technology to obtain the grid model of the three-dimensional flow domain of the smart power distribution cabinet. Based on the preset boundary conditions, the grid model and the preset conditions are input into the Navier-Stokes equation and the energy equation to solve the velocity field, pressure field and temperature field inside the smart power distribution cabinet. After parameter optimization and sensitivity analysis, the optimization scheme for air flow and heat transfer inside the smart power distribution cabinet is obtained. The fan speed and heat sink angle are adjusted based on the optimization scheme to predict the thermal flow field of the smart power distribution cabinet.

2. According to the method for predicting thermal flow field of a smart power distribution cabinet according to claim 1, It is characterized in that The thermal-mechanical coupling model, the fluid dynamics model and the electromagnetic model are established respectively, including: Based on the operation data of each area inside the smart power distribution cabinet, the calculation formula for establishing the thermal coupling model is as follows: In the formula, M is the material density, is the specific heat capacity, T is temperature, t is time, k is thermal conductivity, and Q is the internal heat source; The calculation formula for establishing the fluid dynamics model for simulating the air flow driven by the fan inside the cabinet is as follows: In the formula, ρ is the fluid density, t is the time, z is the fluid velocity vector, and ∇ is the divergence operator, which indicates the size of the source or sink of the vector field; For pressure, is the dynamic viscosity, f is the volume force vector; The calculation formula for establishing an electromagnetic model that simulates the influence of electromagnetic fields on the thermal flow field inside the cabinet is as follows: Where D is the electric displacement field, is the free charge density, E is the electric field, and B is the magnetic field.

3. According to the method for predicting thermal flow field of a smart power distribution cabinet as described in claim 1, It is characterized in that Based on the simulation results, the adaptive mesh refinement algorithm is used to preferentially insert set cells in the area with large temperature gradient, and then the mesh size is dynamically adjusted according to the error estimation to obtain the final refined mesh model, which includes: According to the multi-physics field coupling simulation results, after local functional error estimation processing, the refinement area is determined and the error distribution map is obtained. The calculation formula is as follows: In the formula, E ( h ) is the error estimate, is the numerical solution, u is the exact solution, Ω is the solution domain, h is the grid size, dx is a differential element; Based on the error distribution graph, the refined units with large temperature gradients whose error estimation values ​​exceed a preset threshold are marked, and a refinement operation is performed on the marked refined units to obtain a preliminary refined mesh model, wherein the refinement operation includes inserting new nodes into the refined units and splitting the original units; The preliminary refined mesh model is optimized through the adaptive mesh refinement algorithm, the global error tolerance is set, and the sum of the local error indicators of all units is ensured to be equal to the global error tolerance. The mesh size is dynamically adjusted to obtain the final refined mesh model.

4. According to the method for predicting thermal flow field of a smart power distribution cabinet as described in claim 3, It is characterized in that The global error tolerance is set, and the sum of the local error indicators of all units is ensured to be equal to the global error tolerance, and the mesh size is dynamically adjusted to obtain the final refined mesh model, which includes: Set a global error tolerance tol for each unit K , calculate its local error index, where the local error index is calculated based on the residual in the finite element method, and its calculation formula is as follows: In the formula, For the K The local error index of each unit, C is a constant, For the K The diameter of the unit, that is, the size of the unit, For the K The H^2 norm of the exact solution on each element; The calculation formula to ensure that the sum of the local error indicators of all units is equal to the global error tolerance tol is as follows: In the formula, ∑ is a series of terms accumulated. K For each element in the model, For the K The local error index of each unit, tol is the global error tolerance; The mesh size is adjusted and refined to obtain the final refined mesh model. In the refinement process, a small mesh size is selected for the area with large error and a large mesh size is selected for the area with small error. The calculation formula is as follows: In the formula, For the K The diameter of the unit, ∝ is the scale symbol, For function V In the unit K The H^2 norm on , V is the temperature distribution.

5. According to the method for predicting thermal flow field of a smart power distribution cabinet as described in claim 1, It is characterized in that According to the final refined grid model, the motion-constrained smoothed particle fluid dynamics method is used to simulate fluid dynamics, and the stress distribution is determined by fluid-solid coupling analysis. Finally, the response surface method is used for multi-objective optimization to obtain the optimal layout of the internal structure of the smart power distribution cabinet, including: According to the refined grid model, the fluid domain inside the cabinet is discretized into a set of smooth particle hydrodynamic particles, each of which has mass and position. After kernel function approximation, the approximate result of the continuous function value is obtained, and the calculation formula is as follows: In the formula, is the kernel function, L is the smooth length, For the location x Function f The smoothed or average value of For all particles j The sum of For the j The mass of the particle, For the j The density of particles, For the j Particle positions The original function value at ; Based on the approximate results of the continuous function value, the preliminary simulation results of the fluid dynamics inside the cabinet are obtained after smoothed particle fluid dynamics processing; According to the preliminary simulation results, after fluid-solid coupling analysis and processing, the data exchange between the fluid and solid interface inside the smart power distribution cabinet was obtained, and the physical information neural network model was trained to obtain the predicted data of fluid velocity, pressure, temperature and mass concentration. The predicted data was calculated through fluid-solid coupling stress distribution to obtain the fluid-solid coupling stress distribution inside the smart power distribution cabinet; The stress distribution is processed through multi-objective optimization based on the response surface method, the stress distribution under different design variables is predicted, and the optimal layout plan of the internal structure of the smart power distribution cabinet is obtained.

6. According to the method for predicting thermal flow field of a smart power distribution cabinet according to claim 5, It is characterized in that The stress distribution is processed by multi-objective optimization based on the response surface method, the stress distribution under different design variables is predicted, and the optimal layout scheme of the internal structure of the smart power distribution cabinet is obtained, including: A response surface model was established. After selecting sample points and conducting finite element analysis, the response surface model of stress distribution was obtained. The calculation formula is as follows: Where y is the stress distribution, f(x) is the function of the design variable vector x, is the error term; The response surface model is iteratively calculated through the non-dominated sorting and crowding distance in the non-dominated sorting genetic algorithm to predict the stress distribution under different design variables and determine the optimal solution set. The calculation formula is as follows: In the formula, is the objective function vector, f i (x) is i An objective function, x is the design variable vector; After the optimal solution set analysis and processing, the optimal layout scheme of the internal structure of the smart power distribution cabinet is obtained, and its calculation formula is as follows: In the formula, is the optimal design variable vector, is the set of all possible design variable vectors x, and argmin is the parameter value that minimizes the objective function.

7. A smart power distribution cabinet thermal flow field prediction system, based on the smart power distribution cabinet thermal flow field prediction method according to claim 1, It is characterized in that include: Analysis module: used to cluster and analyze the acquired operation data of each area inside the smart power distribution cabinet, material properties, and component layout information of the equipment inside the cabinet to form a structured database as input parameters; the operation data includes temperature, flow and pressure data, the material properties include the type of plate and coating material, and the component layout information includes equipment layout, equipment function, cable connection and space utilization; Calculation module: used to use multi-physics field simulation software to perform coupling calculations on the structured database, establish a thermal coupling model, a fluid dynamics model, and an electromagnetic model, and couple the three models representing different physical fields to obtain simulation results of thermal flow field and structural response. The thermal coupling model is used to simulate the impact of temperature changes on the structure of the smart power distribution cabinet, the fluid dynamics model is used to simulate the impact of air flow driven by the fan inside the smart power distribution cabinet on heat dissipation, and the electromagnetic model is used to simulate the impact of the electromagnetic field on the thermal flow field inside the cabinet; Estimation module: Based on the simulation results, the adaptive mesh refinement algorithm is used to preferentially insert set cells in the area with large temperature gradient, and then dynamically adjust the mesh size according to the error estimation to obtain the final refined mesh model; Optimization module: used to simulate fluid dynamics using the motion-constrained smoothed particle fluid dynamics method based on the final refined grid model, determine stress distribution through fluid-solid coupling analysis, and finally apply the response surface method for multi-objective optimization to obtain the optimal layout of the internal structure of the smart power distribution cabinet; Prediction module: used to calculate and process the optimal layout plan using simulation technology to obtain the grid model of the three-dimensional flow domain of the smart power distribution cabinet. Based on the preset boundary conditions, the grid model and the preset conditions are input into the Navier-Stokes equation and the energy equation to solve the velocity field, pressure field and temperature field inside the smart power distribution cabinet. After parameter optimization and sensitivity analysis, the optimization scheme for air flow and heat transfer inside the smart power distribution cabinet is obtained. The fan speed and heat sink angle are adjusted based on the optimization scheme to predict the thermal flow field of the smart power distribution cabinet.

8. According to claim 7, the intelligent power distribution cabinet thermal flow field prediction system, It is characterized in that The estimation module includes: Determination unit: It is used to determine the refinement area and obtain the error distribution map according to the multi-physics field coupling simulation results after local functional error estimation. The calculation formula is as follows: In the formula, E ( h ) is the error estimate, is the numerical solution, u is the exact solution, Ω is the solution domain, h is the grid size, dx is a differential element; Marking refined units: used to mark the refined units with large temperature gradients whose error estimation values ​​exceed the preset threshold value based on the error distribution map, and perform refinement operations on the marked refined units to obtain a preliminary refined mesh model, wherein the refinement operation includes inserting new nodes into the refined units and splitting the original units; Optimization processing unit; used to optimize the preliminary refined mesh model through the adaptive mesh refinement algorithm, set the global error tolerance, and ensure that the sum of the local error indicators of all units is equal to the global error tolerance, dynamically adjust the mesh size, and obtain the final refined mesh model.

9. The intelligent power distribution cabinet thermal flow field prediction system according to claim 8, It is characterized in that The optimization processing unit includes: Setting unit: used to set a global error tolerance tol for each unit K , calculate its local error index, where the local error index is calculated based on the residual in the finite element method, and its calculation formula is as follows: In the formula, For the K The local error index of each unit, C is a constant, For the K The diameter of the unit, that is, the size of the unit, For the K The H^2 norm of the exact solution on each element; Calculation unit: The calculation formula used to ensure that the sum of the local error indicators of all units is equal to the global error tolerance tol is as follows: In the formula, ∑ is a series of terms accumulated. K For each element in the model, For the K The local error index of each unit, tol is the global error tolerance; Adjustment unit: used to adjust the mesh size and refine the adjusted mesh size to obtain the final refined mesh model. The refinement process selects a small mesh size for areas with large errors and a large mesh size for areas with small errors. The calculation formula is as follows: In the formula, For the K The diameter of the unit, ∝ is the scale symbol, For function V In the unit K The H^2 norm on , V is the temperature distribution.

10. The intelligent power distribution cabinet thermal flow field prediction system according to claim 7, It is characterized in that The optimization module includes: The first processing unit is used to discretize the fluid domain inside the cabinet into a group of smooth particle fluid dynamic particles according to the refined grid model, where each particle has mass and position. After kernel function approximation processing, the approximate result of the continuous function value is obtained. The calculation formula is as follows: In the formula, is the kernel function, L is the smooth length, For the location x Function f The smoothed or average value of For all particles j The sum of For the j The mass of the particle, For the j The density of particles, For the j Particle positions The original function value at ; The second processing unit is used to obtain preliminary simulation results of the fluid dynamics inside the cabinet based on the approximate results of the continuous function value through smoothed particle fluid dynamics processing; The third processing unit is used to obtain the data exchange of the fluid and solid interface inside the smart power distribution cabinet through fluid-solid coupling analysis and processing based on the preliminary simulation results, and obtain the predicted data of the fluid velocity, pressure, temperature and mass concentration through training through the physical information neural network model, and calculate the predicted data through fluid-solid coupling stress distribution to obtain the stress distribution of fluid-solid coupling inside the smart power distribution cabinet; The fourth processing unit is used to process the stress distribution through multi-objective optimization based on the response surface method, predict the stress distribution under different design variables, and obtain the optimal layout plan of the internal structure of the smart power distribution cabinet.

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