Power distribution cabinet radiator optimization method based on fluid-structure interaction simulation

Abstract

The invention provides a power distribution cabinet radiator optimization method based on fluid-structure interaction simulation, and the method specifically comprises the steps: taking the temperature and energy consumption minimization of a radiator as the optimization target of an optimization algorithm, and taking the configuration parameters of the radiator in a power distribution cabinet as the optimization parameters of the optimization algorithm; optimizing parameters are initialized, initial configuration parameters are obtained, a power distribution cabinet box model is correspondingly constructed for three-field bidirectional fluid-solid coupling simulation, and performance index values of the radiator under the initial configuration parameters are obtained; performing optimization iteration on the configuration parameters through an optimization algorithm in combination with the calculated performance index value and the power distribution cabinet box body model until the performance index value of the simulation result corresponding to the obtained optimal configuration parameter reaches a preset threshold value; and outputting the optimal configuration parameter, and taking the output optimal configuration parameter as the configuration parameter of the installation of the radiator in the power distribution cabinet. According to the method, the radiator configuration optimization is carried out by combining the bidirectional fluid-solid coupling simulation and the optimization algorithm, and the optimization efficiency and the optimization precision of radiator design optimization can be effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution equipment, in particular to an optimization method for the radiator of a power distribution cabinet based on fluid-structure interaction simulation. Background Art

[0002] As an important device for controlling power distribution, the operation of a power distribution cabinet will directly affect the power transmission efficiency and power consumption safety. After the power distribution cabinet is put into use, it needs to operate continuously at a high intensity to meet the power supply demand. Inevitably, the internal components will quickly heat up, which will cause the problem of reduced working efficiency of the power distribution cabinet. In severe cases, it will directly lead to the damage of the power distribution cabinet and thus pose a safety hazard. The heat source of the power distribution cabinet is mainly its internal components. Most of the existing power distribution cabinets are equipped with radiators to reduce the temperature inside the cabinet.

[0003] In order to improve the heat dissipation efficiency of the radiator, the radiator is often further designed and optimized from aspects such as position, wind direction, air volume, and heat dissipation module configuration through simulation methods to further improve the operation safety of the power distribution cabinet. The two-way fluid-structure interaction (FIS) technology can more accurately simulate the interaction between fluids and solids, and has unique advantages in dealing with complex heat dissipation problems. It is often used to optimize the structural design of the radiator and the fluid flow path to improve the heat dissipation efficiency of the radiator.

[0004] However, when optimizing the design of the radiator of a power distribution cabinet, it involves multi-field coupling of fluid-structure-electric field, which significantly increases the complexity and non-linearity of the optimization simulation. The two-way fluid-structure interaction technology often relies on experience or trial-and-error methods to achieve the optimization design. When facing coupling problems such as high complexity and non-linearity, it will face problems such as slow solution speed, low calculation accuracy, and being easily affected by mesh division, resulting in low design optimization efficiency and optimization accuracy for the radiator. Summary of the Invention

[0005] The object of the present invention is to overcome the disadvantages that when using the two-way fluid-structure interaction technology to optimize the design of the radiator of a power distribution cabinet, it is necessary to perform multi-field coupling of fluid-structure-electric field, and the design optimization efficiency and optimization accuracy of the radiator are both relatively low. The present invention provides an optimization method for the radiator of a power distribution cabinet based on fluid-structure interaction simulation. During the process of simulating and optimizing the radiator of the power distribution cabinet through the two-way fluid-structure interaction technology, an optimization algorithm suitable for dealing with high-complexity and non-linear problems is introduced, and the optimal combination of radiator-related parameters is obtained through the iterative search of the optimization algorithm, effectively improving the optimization efficiency and optimization accuracy of the radiator design optimization.

[0006] The object of the present invention is achieved by the following technical solutions:

[0007] An optimization method for the radiator of a power distribution cabinet based on fluid-structure interaction simulation, comprising:

[0008] Taking the minimum radiator temperature and energy consumption as the optimization objectives of the optimization algorithm, and the configuration parameters of the radiator in the power distribution cabinet as the optimization parameters of the optimization algorithm;

[0009] Initialize the optimization parameters of the optimization algorithm, obtain the initial configuration parameters, correspondingly construct the three-field bidirectional fluid-structure interaction simulation of the power distribution cabinet box model, and obtain the performance index values of the radiator under the initial configuration parameters;

[0010] Combining the performance index values of the radiator under the initial configuration parameters and the power distribution cabinet box model, use the optimization algorithm to optimize and iterate the configuration parameters until the performance index values of the simulation results corresponding to the obtained optimal configuration parameters reach the preset threshold;

[0011] Output the optimal configuration parameters, and use the output optimal configuration parameters as the configuration parameters for the installation of the radiator in the power distribution cabinet.

[0012] For the radiator of the power distribution cabinet, use the bidirectional fluid-structure interaction technology to conduct the three-field bidirectional fluid-structure interaction simulation, and realize the simulation in the way of multi-field coupling of fluid-structure-electric field, which is more in line with the actual situation of the radiator of the power distribution cabinet and improves the reliability of the simulation. And in the process of simulating and optimizing the radiator of the power distribution cabinet through the bidirectional fluid-structure interaction technology and realizing the design optimization of the radiator, an optimization algorithm suitable for dealing with high complexity and nonlinear problems is introduced, and the optimal combination of radiator-related parameters is obtained through the iterative search of the optimization algorithm, effectively improving the optimization efficiency and optimization accuracy of the radiator design optimization.

[0013] Further, the combination of the performance index values of the radiator under the initial configuration parameters and the power distribution cabinet box model, and the use of the optimization algorithm to optimize and iterate the configuration parameters until the performance index values of the simulation results corresponding to the obtained optimal configuration parameters reach the preset threshold includes:

[0014] According to the difference between the performance index value of the initial configuration parameters and the preset threshold, use the optimization algorithm to update the configuration parameters to obtain the optimal configuration parameters of the current iteration;

[0015] Configure the power distribution cabinet box model based on the optimal configuration parameters, and conduct the three-field bidirectional fluid-structure interaction simulation according to the configured power distribution cabinet box model;

[0016] Obtain the flow field data of the flow field around the radiator in the power distribution cabinet and the generated simulation power data under the current optimal configuration parameters according to the simulation results;

[0017] Calculate the performance index values of the radiator under the optimal configuration parameters according to the flow field data and the simulation power data;

[0018] When the performance index value is lower than the corresponding preset threshold, output the optimal configuration parameters;

[0019] In other cases, according to the difference between the performance index value corresponding to the current optimal configuration parameters and the preset threshold, the optimal configuration parameters are re-obtained through an optimization algorithm until the corresponding performance index value reaches the corresponding preset threshold.

[0020] Furthermore, the performance index values of the radiator include the temperature value inside the power distribution cabinet and the energy consumption value of the radiator.

[0021] Furthermore, calculating the performance index values of the radiator under the optimal configuration parameters according to the flow field data and the simulated power data includes:

[0022] Obtaining the hydrodynamic characteristics around the radiator based on the flow field data, and obtaining the transient change data of the thermal field inside the power distribution cabinet according to the hydrodynamic characteristics;

[0023] Calculating the temperature value inside the power distribution cabinet according to the transient change data;

[0024] Calculating the energy consumption value of the radiator under the optimal configuration parameters according to the simulated power data.

[0025] Furthermore, calculating the temperature value inside the power distribution cabinet according to the transient change data includes:

[0026] Constructing a heat balance equation inside the power distribution cabinet based on the transient change data of the internal thermal field of the power distribution cabinet;

[0027] Based on the heat balance equation and combined with the heat conduction formula, the temperature value inside the power distribution cabinet is solved;

[0028] Calculating the energy consumption value of the radiator under the optimal configuration parameters according to the simulated power data.

[0029] Furthermore, the expression of the heat balance equation is:

[0030]

[0031] Where ρ is the density of the radiator material, C p is the specific heat capacity at constant pressure of the radiator material, u is the velocity field, g is the volume heat source density, is the temperature gradient, is the heat source density gradient, q is the heat flow direction, Q is the volume heat source, Q ted is the additional heat source.

[0032] Furthermore, the expression of the heat conduction formula is:

[0033]

[0034] Where q is the heat flow direction, k is the thermal conductivity, is the temperature gradient.

[0035] Further, the configuration parameters include the position, shape, and area of the radiator, as well as the wind direction and air volume passing through.

[0036] Further, the corresponding construction of the three-field bidirectional fluid-structure interaction simulation for the power distribution cabinet box model further includes:

[0037] Obtain the power distribution cabinet parameters of the target power distribution cabinet, and establish an equivalent model of the target power distribution cabinet based on the power distribution cabinet parameters;

[0038] Obtain the degree of influence of each component on the surface of the target power distribution cabinet on the squareness of the target power distribution cabinet, and simplify the equivalent model according to the degree of influence of each component on the squareness to obtain the power distribution cabinet box model;

[0039] Configure the radiator in the power distribution cabinet box model according to the initial configuration parameters, and perform three-field bidirectional fluid-structure interaction simulation.

[0040] The beneficial effects of the present invention are:

[0041] For the radiator of the power distribution cabinet, the three-field bidirectional fluid-structure interaction simulation is carried out by using the bidirectional fluid-structure interaction technology, and the simulation is realized in the way of multi-field coupling of fluid-structure-electric field, which is more in line with the actual situation of the radiator of the power distribution cabinet and improves the reliability of the simulation. And in the process of simulating and optimizing the radiator of the power distribution cabinet by using the bidirectional fluid-structure interaction technology and realizing the optimization of the radiator design, an optimization algorithm suitable for dealing with high-complexity and non-linear problems is introduced, and the optimal combination of the radiator-related parameters is obtained by using the iterative search of the optimization algorithm, effectively improving the optimization efficiency and optimization accuracy of the radiator design optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a schematic flow chart of the present invention;

[0043] Figure 2 is a schematic flow chart of optimizing and iterating the configuration parameters by using the optimization algorithm in an embodiment of the present invention;

[0044] Figure 3 is a cloud chart of the internal temperature gradient change of the power distribution cabinet without wind in an embodiment of the present invention;

[0045] Figure 4 is a cloud chart of the internal temperature gradient change of the power distribution cabinet with wind in an embodiment of the present invention;

[0046] Figure 5 is a schematic diagram of the internal thermal field temperature contour of the power distribution cabinet with a small air volume in an embodiment of the present invention;

[0047] Figure 6 is a schematic diagram of the internal thermal field temperature contour of the power distribution cabinet with a large air volume in an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0048] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0049] Embodiment:

[0050] An optimization method for the radiator of a distribution cabinet based on fluid-structure interaction simulation, as Figure 1 shown, includes:

[0051] Taking the minimum radiator temperature and energy consumption as the optimization objectives of the optimization algorithm, and the configuration parameters of the radiator in the distribution cabinet as the optimization parameters of the optimization algorithm;

[0052] Initialize the optimization parameters of the optimization algorithm, obtain the initial configuration parameters, correspondingly construct a distribution cabinet box model for three-field two-way fluid-structure interaction simulation, and obtain the performance index values of the radiator under the initial configuration parameters;

[0053] Combining the performance index values of the radiator under the initial configuration parameters and the distribution cabinet box model, perform optimization iteration on the configuration parameters through the optimization algorithm until the performance index values of the simulation results corresponding to the obtained optimal configuration parameters reach the preset threshold;

[0054] Output the optimal configuration parameters, and use the output optimal configuration parameters as the configuration parameters for the installation of the radiator in the distribution cabinet.

[0055] When designing and optimizing the radiator, taking the reduction of the radiator temperature and energy consumption as the optimization objectives, by optimizing the configuration parameters of the radiator, the temperature and energy consumption of the radiator can be reduced, which can reduce the performance degradation or failure of electrical components in the distribution cabinet caused by overheating, ensure the operation safety of the distribution cabinet, and improve the overall operation efficiency of the distribution cabinet.

[0056] And considering that the distribution cabinet is a complex system, which includes multiple physical fields such as electricity, heat, and structure. When using the two-way fluid-structure interaction technology to design and optimize the radiator of the distribution cabinet, in addition to considering the influence of fluid flow and structural changes on the heat dissipation effect, it is also necessary to further consider the influence of the interaction between the electric field and other physical fields on the heat dissipation effect. Therefore, use the two-way fluid-structure interaction technology to perform three-field two-way fluid-structure interaction simulation on the radiator of the distribution cabinet to verify the heat dissipation effect of the corresponding configuration parameters, and then realize the optimization of the configuration parameters of the radiator.

[0057] If the traditional method that relies on experience or trial and error is used to optimize the configuration parameters, in the face of the multiple optimization objectives and complex simulation environment of the radiator of the distribution cabinet, problems such as slow solution speed and low calculation accuracy will inevitably occur. Therefore, an optimization algorithm is further introduced to handle the solution of multiple optimization objectives in the high complexity and non-linearity scenarios at this time.

[0058] Among them, the optimization algorithm in this embodiment can be a particle swarm optimization algorithm, a genetic algorithm, a gradient descent algorithm, etc., which can ensure the optimization efficiency and optimization accuracy for the optimized design of the radiator.

[0059] Taking the particle swarm optimization algorithm as an example, the optimization objectives of the particle swarm algorithm are to minimize the radiator temperature and energy consumption. The configuration parameters of the radiator are the optimization parameters. Initialize the optimization parameters of the particle swarm optimization algorithm, that is, give a group of particles, and each particle represents a possible combination of radiator configuration parameters. At this time, the given particles are the initial configuration parameters, and the initial configuration parameters can be randomly generated or some of the particles can be set according to experience. At the same time, set other algorithm parameters, such as basic parameters like the particle swarm size, the number of iterations, the learning factor, and the inertia weight.

[0060] Combined with the set optimization objectives of minimizing the radiator temperature and energy consumption, perform three-field bidirectional fluid-structure interaction simulations for the scenario where the radiator is installed in the power distribution cabinet with the initial configuration parameters to judge the effects of the current initial configuration parameters on the radiator temperature and energy consumption, obtain the corresponding fitness values, and then realize subsequent optimization iterations based on the obtained fitness values.

[0061] When implementing three-field bidirectional fluid-structure interaction simulations to determine the corresponding fitness values, it is necessary to construct the corresponding power distribution cabinet box model to provide the corresponding boundary conditions for the operation simulation of the radiator.

[0062] Among them, when constructing the power distribution cabinet box model for three-field bidirectional fluid-structure interaction simulations, the following steps are included:

[0063] Obtain the power distribution cabinet parameters of the target power distribution cabinet, and establish an equivalent model of the target power distribution cabinet based on the power distribution cabinet parameters;

[0064] Obtain the influence degree of each component on the surface of the target power distribution cabinet on the squareness of the target power distribution cabinet, and simplify the equivalent model according to the influence degree of each component on the squareness to obtain the power distribution cabinet box model;

[0065] Configure the radiator in the power distribution cabinet box model according to the initial configuration parameters, and perform three-field bidirectional fluid-structure interaction simulations.

[0066] Considering that the sizes and structures of different power distribution cabinets are different, in order to ensure the accuracy of the simulation results, when constructing the power distribution cabinet box model, first determine the target power distribution cabinet that needs to be optimized for the radiator, then obtain the corresponding power distribution cabinet parameters, and then establish the corresponding equivalent model.

[0067] When constructing the equivalent model of the power distribution cabinet, it is necessary to set the corresponding external dimensions according to the parameters of the power distribution cabinet, set the positions and opening sizes of the air inlet and outlet ducts of the power distribution cabinet, set the heat source at the same time, define the corresponding materials and sizes, adjust their placement positions, and finally set the radiator and define its corresponding materials. Its position and size can be used as configuration parameters to participate in the subsequent optimization algorithm for optimization.

[0068] Moreover, in order to reduce the simulation calculation amount, components on the surface of the target power distribution cabinet that have a relatively small impact on the squareness of the target power distribution cabinet, that is, have a relatively small impact on simulation results such as the temperature of the radiator, are removed to obtain the corresponding power distribution cabinet box model.

[0069] Specifically, the construction and simulation of the power distribution cabinet box model can be realized through COMSOL software. The multi-physics coupling function of COMSOL software can consider the interaction between fluid and solid structures in the physical field to more accurately simulate the thermal radiation effect of the heat source in the fluid, and the transient changes of the internal thermal field of the power distribution cabinet can be observed at different flow rates or different model sizes.

[0070] Therefore, after obtaining the initial configuration parameters, the corresponding fitness value is determined through the corresponding three-field bidirectional fluid-structure interaction simulation, and then the optimization algorithm is combined with the current fitness value for optimization iteration until the desired optimization goal is achieved. The fitness value here is the performance index value reflecting heat dissipation and energy consumption.

[0071] In this embodiment, the performance index values of the radiator include the temperature value inside the power distribution cabinet and the energy consumption value of the radiator.

[0072] Specifically, combining the performance index values of the radiator under the initial configuration parameters and the power distribution cabinet box model, the configuration parameters are optimized and iterated through the optimization algorithm until the performance index values of the simulation results corresponding to the obtained optimal configuration parameters reach the preset threshold, as Figure 2 shown, including the following steps:

[0073] According to the difference between the performance index value of the initial configuration parameters and the preset threshold, the configuration parameters are updated through the optimization algorithm to obtain the optimal configuration parameters for the current iteration;

[0074] Configure the power distribution cabinet box model based on the optimal configuration parameters, and perform three-field bidirectional fluid-structure interaction simulation according to the configured power distribution cabinet box model;

[0075] Obtain the flow field data of the flow field around the radiator inside the power distribution cabinet and the generated simulation power data under the current optimal configuration parameters according to the simulation results;

[0076] Calculate the performance index values of the radiator under the optimal configuration parameters according to the flow field data and the simulation power data;

[0077] When the performance index value is lower than the corresponding preset threshold, the optimal configuration parameters are output;

[0078] In other cases, according to the difference between the performance index value corresponding to the current optimal configuration parameters and the preset threshold, the optimal configuration parameters are re-obtained through an optimization algorithm until the corresponding performance index value reaches the corresponding preset threshold.

[0079] The above steps are the iterative process of the optimization algorithm, which can calculate the new performance index value according to the configuration parameters corresponding to each iteration and evaluate whether it is closer to the preset threshold, so as to guide the optimization direction of the configuration parameters and efficiently complete the optimization of the radiator configuration parameters. And in the optimization iteration process, a simulation is carried out each time to obtain accurate performance index values.

[0080] The working principle of the radiator is mainly to transfer heat from the heat source to the surrounding environment through convective heat transfer. Through the flow field data, the actual situation of the physical field around the radiator in the simulation process can be reflected, and then the transient change data of the heat field of the heat transfer and temperature distribution around the radiator can be obtained, so as to intuitively reflect the temperature distribution in the power distribution cabinet. Through the analysis of the temperature distribution, the distribution of the temperature values in the power distribution cabinet can be directly obtained, and for the energy consumption situation, it can be calculated through the electrical data generated in the simulation process.

[0081] Among them, calculating the performance index value of the radiator under the optimal configuration parameters according to the flow field data and the simulated power data includes:

[0082] Obtaining the hydrodynamic characteristics around the radiator based on the flow field data, and obtaining the transient change data of the heat field in the power distribution cabinet according to the hydrodynamic characteristics;

[0083] Calculating the temperature value in the power distribution cabinet according to the transient change data;

[0084] Calculating the energy consumption value of the radiator under the optimal configuration parameters according to the simulated power data.

[0085] The flow field data around the radiator in the simulation process can be directly collected through COMSOL software. Among them, the flow field data includes parameters such as flow velocity, flow direction, pressure, and temperature.

[0086] Then analyze the collected flow field data to obtain the hydrodynamic characteristics around the radiator, such as flow velocity distribution, vortex structure, pressure gradient, temperature gradient, etc. These characteristics can reflect the flow state, energy transfer and conversion process of the fluid around the radiator.

[0087] Combined with the obtained hydrodynamic characteristics, the correlation between the flow field around the radiator and the heat field in the power distribution cabinet can be established, and then the transient change data of the corresponding heat field in the power distribution cabinet can be obtained.

[0088] Then, calculate the temperature value inside the power distribution cabinet based on the obtained transient change data, including:

[0089] Based on the transient change data of the internal thermal field of the power distribution cabinet, construct the heat balance equation inside the power distribution cabinet;

[0090] Based on the heat balance equation and combined with the heat conduction formula, solve to obtain the temperature value inside the power distribution cabinet;

[0091] Calculate the energy consumption value of the radiator under the optimal configuration parameters according to the simulated power data.

[0092] Among them, the expression of the heat balance equation is:

[0093]

[0094] Among them, ρ is the density of the radiator material, C p is the specific heat capacity at constant pressure of the radiator material, u is the velocity field, g is the volume heat source density, is the temperature gradient, is the heat source density gradient, q is the heat flow direction, Q is the volume heat source, Q ted is the additional heat source.

[0095] The above heat balance equation represents the balance of heat generated by conduction, convection, and heat sources, represents convective heat transfer, represents the heat conduction term, Q + Q ted then represents the heat generated by the heat source.

[0096] The expression of the heat conduction formula is:

[0097]

[0098] Among them, q is the heat flow direction, k is the thermal conductivity, is the temperature gradient.

[0099] The above heat conduction formula indicates that the direction of heat flow is opposite to the direction of the temperature gradient, and heat conduction occurs along the direction of temperature decrease.

[0100] Substitute the heat conduction formula into the heat balance equation to obtain an equation containing temperature, time, and other heat transfer parameters, and solve it through numerical methods such as the finite difference method and the finite element method to obtain the temperature value inside the power distribution cabinet.

[0101] When calculating the corresponding energy consumption value through the simulated power data, it can be calculated by multiplying the corresponding current and voltage in the simulated power data and combining the corresponding simulated operation time.

[0102] After obtaining the corresponding temperature value and energy consumption value, they can be compared with the preset threshold to evaluate the optimization effect of the current configuration parameters. If the optimization goal cannot be achieved, the optimization algorithm can be used to continue the optimization iteration until the optimal configuration parameters are obtained.

[0103] Moreover, for the obtained process data, in addition to providing a data basis for calculating the performance index value, it can also analyze the obtained flow field data to identify deficiencies in the radiator design, such as uneven flow distribution and process blockage, etc., further providing a basis for the improved design of the radiator.

[0104] After outputting the optimal configuration parameters, when actually installing the radiator in the power distribution cabinet during construction, the relevant parameters of the radiator can be set according to the output optimal configuration parameters to achieve a better heat dissipation effect.

[0105] In this embodiment, the configuration parameters include the position, shape, and area of the radiator, as well as the wind direction and air volume passing through. In addition to the position, shape, and area that have a direct correlation with the heat dissipation performance of the radiator, configuration parameters of the wind direction and air volume that have an indirect relationship with the heat dissipation performance of the radiator are additionally set.

[0106] For the wind direction and air volume, taking the simulation optimization iteration of one power distribution cabinet as an example, under the condition that other configuration parameters are the same, the change cloud maps of the temperature gradient inside the power distribution cabinet when there is no wind and when there is wind are respectively as Figure 3 and Figure 4 shown. The isotherm conditions of the internal thermal field of the power distribution cabinet under small air volume and large air volume are respectively as Figure 5 and Figure 6 shown. Through Figures 3 to 6 the reliability of selecting the wind direction and air volume as configuration parameters can be visually verified.

[0107] Thus, it can be seen that by adjusting the above configuration parameters, the purpose of effectively adjusting the heat dissipation performance and energy consumption performance of the radiator can be achieved.

[0108] The above-described embodiments are only a preferred solution of the present invention, and do not impose any form of limitation on the present invention. There are other variations and modifications without exceeding the technical solutions recorded in the claims.

Claims

1. The optimization method of power distribution cabinet radiator based on fluid-solid coupling simulation is characterized by: include: The optimization goal of the optimization algorithm is to minimize the radiator temperature and energy consumption, and the configuration parameters of the radiator in the power distribution cabinet are the optimization parameters of the optimization algorithm; Initialize the optimization parameters of the optimization algorithm, obtain the initial configuration parameters, build a distribution cabinet model to perform three-field bidirectional fluid-solid coupling simulation, and obtain the performance index value of the radiator under the initial configuration parameters; combine the performance index value of the radiator under the initial configuration parameters and the distribution cabinet model, and iterate the configuration parameters through the optimization algorithm until the performance index value of the simulation result corresponding to the optimal configuration parameters reaches the preset threshold; The optimal configuration parameters are output, and the output optimal configuration parameters are used as the configuration parameters for installing the radiator in the power distribution cabinet.

2. The method for optimizing the radiator of a power distribution cabinet based on fluid-solid coupling simulation according to claim 1 is characterized in that: The method combines the performance index value of the radiator under the initial configuration parameters and the distribution cabinet box model, and iterates the configuration parameters through an optimization algorithm until the performance index value of the simulation result corresponding to the obtained optimal configuration parameters reaches a preset threshold, including: According to the difference between the initial configuration parameter performance indicator value and the preset threshold, the configuration parameters are updated through the optimization algorithm to obtain the optimal configuration parameters of the current iteration; Configure the distribution cabinet model based on the optimal configuration parameters, and perform three-field bidirectional fluid-solid coupling simulation based on the configured distribution cabinet model; According to the simulation results, the flow field data of the flow field around the radiator in the power distribution cabinet under the current optimal configuration parameters and the generated simulated power data are obtained; Calculate the performance index value of the radiator under the optimal configuration parameters based on the flow field data and the simulated power data; When the performance indicator value is lower than the corresponding preset threshold, the optimal configuration parameters are output; In other cases, the optimal configuration parameters are re-obtained through an optimization algorithm according to the difference between the performance indicator value corresponding to the current optimal configuration parameter and the preset threshold value, until the corresponding performance indicator value reaches the corresponding preset threshold value.

3. The method for optimizing the radiator of a power distribution cabinet based on fluid-solid coupling simulation according to claim 2 is characterized in that: The performance index value of the radiator includes the temperature value in the power distribution cabinet and the energy consumption value of the radiator.

4. The method for optimizing the radiator of a power distribution cabinet based on fluid-solid coupling simulation according to claim 3 is characterized in that: The calculation of the performance index value of the radiator under the optimal configuration parameters according to the flow field data and the simulated power data includes: Based on the flow field data, the fluid dynamics characteristics around the radiator are obtained, and the transient change data of the thermal field in the distribution cabinet are obtained according to the fluid dynamics characteristics; Calculate the temperature value in the power distribution cabinet based on transient change data; The energy consumption value of the radiator under the optimal configuration parameters is calculated based on the simulated power data.

5. The method for optimizing the radiator of a power distribution cabinet based on fluid-solid coupling simulation according to claim 4 is characterized in that: The step of calculating the temperature value in the power distribution cabinet according to the transient change data includes: Based on the transient change data of the thermal field inside the power distribution cabinet, the thermal balance equation inside the power distribution cabinet is constructed; Based on the heat balance equation and combined with the heat conduction formula, the temperature value inside the power distribution cabinet is obtained; The energy consumption value of the radiator under the optimal configuration parameters is calculated based on the simulated power data.

6. The method for optimizing the radiator of a power distribution cabinet based on fluid-solid coupling simulation according to claim 5 is characterized in that: The expression of the heat balance equation is: Where ρ is the heat sink material density, C p is the constant pressure specific heat capacity of the radiator material, u is the velocity field, g is the volume heat source density, is the temperature gradient, is the heat source density gradient, q is the heat flow direction, Q is the volume heat source, Q ted For additional heat source.

7. The method for optimizing the radiator of a power distribution cabinet based on fluid-solid coupling simulation according to claim 6 is characterized in that: The heat conduction formula is expressed as: Where q is the heat flow direction, k is the thermal conductivity, is the temperature gradient.

8. The method for optimizing a power distribution cabinet radiator based on fluid-solid coupling simulation according to claim 1, characterized in that: The configuration parameters include the position, shape and area of ​​the radiator and the wind direction and volume passing therethrough.

9. The method for optimizing a power distribution cabinet radiator based on fluid-solid coupling simulation according to claim 1, characterized in that: The corresponding construction of the distribution cabinet box model for three-field bidirectional fluid-solid coupling simulation also includes: Obtain the distribution cabinet parameters of the target distribution cabinet, and establish an equivalent model of the target distribution cabinet based on the distribution cabinet parameters; obtain the influence of each component on the surface of the target distribution cabinet on the squareness of the target distribution cabinet, and simplify the equivalent model according to the influence of each component on the squareness to obtain the distribution cabinet box model; The radiator in the distribution cabinet model is configured according to the initial configuration parameters, and three-field bidirectional fluid-solid coupling simulation is performed.

Images