A buoyancy-driven switch cabinet air flow circulation simulation method and system

Abstract

The application belongs to the technical field of simulation analysis, and provides a floating force driven switch cabinet air circulation simulation method and system, which comprises the following steps: constructing a geometric model of a switch cabinet air tank, and setting a simulation boundary condition according to an actual operation condition of the switch cabinet air tank; mapping a local temperature gradient value and a local characteristic scale value of any spatial position in the switch cabinet air tank into a floating force driving intensity coefficient, and constructing a floating force driving intensity distribution atlas of the switch cabinet air tank, and extracting a floating force driving flow field function partition; performing asymmetric configuration on a ventilation opening structure of the air tank, constructing an asymmetric heat dissipation air duct simulation scheme in the switch cabinet, and obtaining simulation field distribution data of the asymmetric heat dissipation air duct; quantifying the simulation field distribution data into a floating force driving effective coefficient, and determining optimal air duct configuration parameters. The application can accurately identify an air flow function partition through the floating force driving intensity coefficient, quantify a ventilation efficiency, and automatically optimize air duct configuration, so that the heat dissipation efficiency can be significantly improved, and the design cycle can be shortened.

Description

Technical Field

[0001] This invention belongs to the field of simulation analysis technology, and in particular relates to a simulation method and system for airflow circulation in a buoyancy-driven switchgear. Background Technology

[0002] Dry air-insulated metal-enclosed switchgear employs a sealed air-box structure. The Joule heat generated by the internal current-carrying conductors primarily relies on the natural convection of the dry air within the box for diffusion and transfer. Due to the constraints of the conductors, insulators, and other physical structures within the box, the airflow organization is complex. Poor heat dissipation can directly lead to excessively high localized temperatures, threatening the equipment's insulation performance and operational safety. Therefore, understanding the temperature and flow field distribution patterns inside the air-box through simulation analysis, and optimizing the ventilation outlet design accordingly, is a crucial technical approach to improving the switchgear's natural convection heat dissipation capacity and reducing temperature rise in critical components.

[0003] However, existing simulation methods for airflow circulation in switchgear mostly rely on forced convection as a preset condition, typically setting the inlet air velocity directly as a boundary condition to drive the flow field calculation. They lack specialized simulation and evaluation methods for buoyancy-driven natural convection conditions. While existing methods can couple the solution of fluid control equations and energy equations to obtain temperature and flow field distributions, the simulation results are mostly used for passive observation of flow field states and temperature distributions. It is difficult to extract quantitative indicators reflecting the effectiveness of buoyancy-driven operation, nor can they accurately identify the spatial orientation of effective upward airflow channels inside the air box or the heat accumulation dead zones caused by insufficient buoyancy. This keeps simulation analysis at the level of phenomenological description, failing to provide quantitative basis for targeted improvements to ventilation structures. When heat dissipation performance is substandard, designers can only rely on experience to repeatedly adjust the vent positions and opening areas and re-simulate, lacking clear quantitative optimization criteria. This results in long design cycles and high trial-and-error costs. Especially for novel ventilation structures such as asymmetric heat dissipation ducts, existing methods cannot establish a quantitative correlation between vent configuration parameters and natural convection circulation efficiency, severely restricting the efficient conversion of simulation results into engineering optimization solutions. Summary of the Invention

[0004] To address the shortcomings of existing technologies, the present invention aims to provide a buoyancy-driven airflow circulation simulation method and system for switchgear. By accurately identifying airflow functional zones through the buoyancy-driven strength coefficient, quantifying ventilation efficiency, and automatically optimizing air duct configuration, the heat dissipation efficiency can be significantly improved and the design cycle shortened.

[0005] To achieve the above objectives, this invention provides a buoyancy-driven airflow circulation simulation method for switchgear, comprising the following steps: S1. Construct the geometric model of the switchgear air box and set the simulation boundary conditions according to the actual operating conditions of the switchgear air box. S2. Map the local temperature gradient value and local characteristic scale value at any spatial location in the switchgear air box to the buoyancy drive strength coefficient, construct the buoyancy drive strength distribution map inside the switchgear air box, and extract the functional partition of the buoyancy drive flow field. S3. Based on the functional zoning of the buoyancy-driven flow field, the structure of the air box ventilation port is configured asymmetrically to construct a simulation scheme for the asymmetrical heat dissipation air duct in the switch cabinet, and the simulation field distribution data of the asymmetrical heat dissipation air duct is obtained. S4. Quantify the simulation field distribution data into buoyancy drive effective coefficient, evaluate the ventilation efficiency of the asymmetric heat dissipation air duct, and determine the optimal air duct configuration parameters to guide the ventilation structure design of the switchgear.

[0006] In a preferred embodiment, in step S1, a geometric model of the switchgear gas box is constructed, and simulation boundary conditions are set according to the actual operating conditions of the switchgear gas box, including: A geometric model is established based on the spatial geometric configuration parameters of the switchgear gas box; Based on the internal space boundary enclosed by the cabinet walls in the geometric model, the area occupied by the internal solids excluding the switch cabinet is defined as the solution region. The physical properties of dry air within the operating temperature range of the switchgear air box are set as the fluid domain physical properties of the closed cavity inside the air box. The Joule heat loss value of the internal current-carrying conductor in the switchgear gas box under rated load is set as the body heat source. The ambient temperature and convective heat transfer coefficient of the external environment of the switchgear gas box are set as the wall heat transfer boundary conditions of the outer surface of the switchgear cabinet wall, and the flow mode of the closed cavity inside the gas box is set as natural convection mode.

[0007] In a preferred embodiment, step S2, mapping the local temperature gradient value and local characteristic scale value at any spatial location within the switchgear gas box to a buoyancy drive strength coefficient, includes: The temperature field is solved by applying simulation boundary conditions to the geometric model of the switchgear gas box, and the temperature distribution data of each spatial location inside the switchgear gas box are obtained. Numerical difference is performed on the temperature distribution data along the spatial coordinate direction to obtain the local temperature gradient value at any spatial location inside the switchgear air box; The distance between the target spatial location and the nearest wall or the nearest internal entity is used as the local feature scale value of the target spatial location; Mapping local temperature gradient values ​​to local characteristic scale values ​​into buoyancy driving strength coefficients: ; In the formula, Indicates spatial location Buoyancy driving strength coefficient at the location, The coefficient of thermal expansion of dry air, where g is the acceleration due to gravity. Indicates spatial location The local temperature gradient value at that location, Indicates spatial location Local feature scale value at the location, This indicates the kinematic viscosity of dry air.

[0008] In a preferred embodiment, temperature distribution data for various spatial locations inside the switchgear gas box are obtained, including: The fluid domain and solid domain of the geometric model are meshed separately to obtain a computational mesh containing the fluid domain mesh and the solid domain mesh; The simulation boundary conditions are applied to the corresponding nodes of the computational grid, and the energy balance relationship at each node is iteratively analyzed until convergence, thus obtaining the temperature values ​​at each node of the computational grid. Based on the temperature values ​​at each node of the computational grid, temperature distribution data for each spatial location inside the switchgear gas box is generated.

[0009] In a preferred embodiment, step S2 involves constructing a buoyancy drive intensity distribution map inside the switchgear air box and extracting functional zones of the buoyancy drive flow field, including: The buoyancy drive strength coefficient is used as the spectral value of each spatial position inside the switchgear air box. The spectral values ​​are arranged into a three-dimensional spatial coordinate system according to the coordinates of each spatial position to generate a buoyancy drive strength distribution map. In the buoyancy-driven intensity distribution map, continuous spatial regions with map values ​​higher than a first preset threshold are marked as airflow uplift-dominant regions, and continuous spatial regions with map values ​​lower than a second preset threshold are marked as backflow stagnation regions. The region dominated by rising airflow and the region of stagnant backflow are used as functional zones for the buoyancy-driven flow field.

[0010] In a preferred embodiment, step S3 involves constructing a simulation scheme for the asymmetric heat dissipation duct in the switchgear, and obtaining simulation field distribution data for the asymmetric heat dissipation duct, including: Based on the orientation of the central axis of the dominant area of ​​rising airflow in the buoyancy-driven flow field functional zoning, determine the location of the air outlet at the top of the switchgear air box; Based on the spatial location of the backflow retention area in the buoyancy-driven flow field functional zoning, determine the location of the air inlet at the bottom of the switchgear air box; Based on the location of the air outlet and air inlet, the ventilation structure of the air box of the switchgear is configured asymmetrically, with the air inlet and air outlet respectively opened on the wall of the switchgear air box, to construct an asymmetrical heat dissipation air duct simulation scheme. Simulation boundary conditions were applied to the asymmetric heat dissipation duct simulation scheme, and fluid-structure interaction simulation was performed to obtain simulation field distribution data.

[0011] In a preferred embodiment, step S4, quantizing the simulation field distribution data into buoyancy-driven effective coefficients, includes: Based on the simulation field distribution data of the asymmetric heat dissipation air duct, the airflow velocity vector data of each spatial location inside the switch cabinet air box are obtained; When the angle between the airflow velocity vector data of a spatial location and the direction of gravity is greater than 90 degrees, the weighting factor of the spatial location takes a positive value. Based on the buoyancy drive strength coefficient at each spatial location, the internal enclosed cavity volume and included angle of the switchgear air box, the effective buoyancy drive coefficient of the asymmetric heat dissipation air duct is quantified.

[0012] In a preferred embodiment, the formula for calculating the buoyancy-driven effective coefficient is: ; In the formula, This represents the effective coefficient of buoyancy drive for asymmetric heat dissipation air channels. Indicates spatial location Buoyancy driving strength coefficient at the location, Indicates spatial location The angle between the airflow velocity vector and the direction of gravity at that location. Indicates spatial location The weighting factor at the location, V represents the internal enclosed cavity volume of the switchgear air box.

[0013] In a preferred embodiment, step S4 involves evaluating the ventilation efficiency of the asymmetric heat dissipation duct and determining the optimal duct configuration parameters to guide the ventilation structure design of the switchgear, including: The effective coefficient of buoyancy drive of the asymmetric heat dissipation air duct is used as the quantitative evaluation result of the ventilation efficiency of the asymmetric heat dissipation air duct simulation scheme. By changing the configuration parameters of the air inlet and the air outlet, multiple different simulation schemes of asymmetric heat dissipation air ducts were obtained, and the effective coefficient of buoyancy drive corresponding to each scheme was obtained. The inlet and outlet configuration parameters corresponding to the simulation scheme of the asymmetric heat dissipation air duct with the largest effective coefficient of buoyancy drive are used as alternative air duct configuration parameters. Update the configuration of the air inlet and outlet according to the alternative air duct configuration parameters, and repeat the alternative air duct configuration parameter selection operation until the change in the effective coefficient of buoyancy drive in two adjacent selections is less than the preset convergence threshold, and stop the iteration to obtain the optimal air duct configuration parameters. The ventilation structure design of the switchgear is guided by the optimal air duct configuration parameters.

[0014] A buoyancy-driven airflow circulation simulation system for switchgear, used to implement the above method, includes: The simulation boundary setting module is used to construct the geometric model of the switchgear gas box and set the simulation boundary conditions according to the actual operating conditions of the switchgear gas box. The buoyancy partition extraction module is used to map the local temperature gradient value and local feature scale value at any spatial location in the switchgear air box to the buoyancy drive strength coefficient, and to construct the buoyancy drive strength distribution map inside the switchgear air box, and extract the functional partition of the buoyancy drive flow field. The asymmetric air duct simulation module is used to configure the air box ventilation structure asymmetrically according to the functional zoning of the buoyancy-driven flow field, construct an asymmetric heat dissipation air duct simulation scheme in the switch cabinet, and obtain the simulation field distribution data of the asymmetric heat dissipation air duct. The efficiency evaluation and optimization module is used to quantify the simulation field distribution data into buoyancy drive effective coefficient, evaluate the ventilation efficiency of the asymmetric heat dissipation air duct, and determine the optimal air duct configuration parameters to guide the ventilation structure design of the switch cabinet.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention constructs a spatially distributed buoyancy-driven intensity coefficient, mapping the local temperature gradient and local characteristic scale of various spatial locations within the switchgear gas box into a quantified buoyancy-driven intensity index. This achieves a refined spatial characterization of the natural convection driving force within the sealed cavity. The buoyancy-driven intensity distribution map constructed based on this clearly presents the spatial distribution pattern of the buoyancy-driven force within the gas box. The extracted buoyancy-driven flow field functional zoning precisely defines the spatial boundaries and directional characteristics of the airflow ascending dominance region and the recirculation stagnation region. This allows simulation analysis to move from observing the overall temperature field to the quantitative identification of local airflow organization characteristics, providing a precise spatial positioning basis for ventilation structure optimization.

[0016] This invention quantifies the simulated field distribution data of asymmetric heat dissipation ducts into buoyancy-driven effective coefficients, establishing a quantitative correlation between ventilation port configuration schemes and natural convection circulation efficiency. Iterative configuration adjustments are made with maximizing the buoyancy-driven effective coefficient as the optimization objective. This eliminates the reliance on empirical trial-and-error for determining configuration parameters such as ventilation port location, opening area, and opening angle. Instead, optimal solutions are scientifically obtained through explicit quantitative convergence criteria. This effectively overcomes the shortcomings of traditional simulation methods, which can only passively evaluate design schemes and cannot actively guide parameter optimization. It significantly shortens the design iteration cycle of ventilation structures and improves the rationality of asymmetric heat dissipation duct configurations and the reliability of heat dissipation performance in switchgear. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart of the method of the present invention; Figure 2This is a convergence curve of the airway configuration parameter optimization process in Example 1; Figure 3 This is a histogram comparing the steady-state temperature rise of key monitoring points before and after optimization in Example 1; Figure 4 This is a functional block diagram of the system of the present invention. Detailed Implementation

[0018] The embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0019] Example 1: As Figure 1 As shown, a buoyancy-driven airflow circulation simulation method for switchgear includes the following steps: S1. Construct the geometric model of the switchgear air box and set the simulation boundary conditions according to the actual operating conditions of the switchgear air box. S2. Map the local temperature gradient value and local characteristic scale value at any spatial location in the switchgear air box to the buoyancy drive strength coefficient, construct the buoyancy drive strength distribution map inside the switchgear air box, and extract the functional partition of the buoyancy drive flow field. S3. Based on the functional zoning of the buoyancy-driven flow field, the structure of the air box ventilation port is configured asymmetrically to construct a simulation scheme for the asymmetrical heat dissipation air duct in the switch cabinet, and the simulation field distribution data of the asymmetrical heat dissipation air duct is obtained. S4. Quantify the simulation field distribution data into buoyancy drive effective coefficient, evaluate the ventilation efficiency of the asymmetric heat dissipation air duct, and determine the optimal air duct configuration parameters to guide the ventilation structure design of the switchgear.

[0020] In S1, a geometric model of the switchgear gas box is constructed, and simulation boundary conditions are set according to the actual operating conditions of the switchgear gas box, including: A geometric model is established based on the spatial geometric configuration parameters of the switchgear gas box; Based on the internal space boundary enclosed by the cabinet walls in the geometric model, the area occupied by the internal solids excluding the switch cabinet is defined as the solution region. The physical properties of dry air within the operating temperature range of the switchgear air box are set as the fluid domain physical properties of the closed cavity inside the air box. The Joule heat loss value of the internal current-carrying conductor in the switchgear gas box under rated load is set as the body heat source. The ambient temperature and convective heat transfer coefficient of the external environment of the switchgear gas box are set as the wall heat transfer boundary conditions of the outer surface of the switchgear cabinet wall, and the flow mode of the closed cavity inside the gas box is set as natural convection mode.

[0021] A geometric model is established based on the spatial geometric configuration parameters of the switchgear gas box. These parameters include the length, width, and height of the cabinet wall, the thickness of the cabinet wall, the spatial layout and orientation of the internal current-carrying conductors, the cross-sectional shape and size of the internal current-carrying conductors, the installation position of the insulating support components, and the location and shape of the ventilation openings on the cabinet wall. Based on these parameters, a closed structure enclosed by the cabinet wall, the geometry of the current-carrying conductors inside the closed structure, and the geometry of the insulating support components are constructed in a three-dimensional spatial coordinate system. The cabinet wall, the internal current-carrying conductors, the insulating support components, and the ventilation opening structure are then combined into a geometric model.

[0022] Based on the internal space boundary enclosed by the cabinet walls in the geometric model, the internal solid area excluding the switch cabinet is defined as the solution region. The internal solid area includes the space occupied by the internal current-carrying conductors and the space occupied by the insulating support components. The solution region is the connected space where dry air can flow freely inside the air box.

[0023] The physical properties of dry air within the operating temperature range of the switchgear air box are set as the fluid domain physical properties of the closed cavity inside the air box. The fluid domain physical properties are used to describe the physical properties of dry air in the solution region. The physical properties include the density, specific heat capacity at constant pressure, thermal conductivity and dynamic viscosity of dry air.

[0024] The Joule heat loss value of the internal current-carrying conductor in the switchgear gas box under rated load is set as the volume heat source. The internal current-carrying conductor is a conductive component that carries the working current. When the current flows through the internal current-carrying conductor, heat is generated due to the resistance of the conductor itself. The rate of heat generation is the Joule heat loss value. The Joule heat loss value is applied as the volume heat source to the space region where the internal current-carrying conductor is located, so that the space region continuously releases heat to the surrounding dry air during the simulation.

[0025] The ambient temperature and convective heat transfer coefficient of the external environment of the switch cabinet air box are set as the wall heat transfer boundary conditions of the outer surface of the switch cabinet wall. The ambient temperature is the temperature of the air outside the switch cabinet, the convective heat transfer coefficient is used to characterize the heat exchange capacity between the outer surface of the switch cabinet wall and the external ambient air, and the wall heat transfer boundary conditions are used to control the rate at which heat is transferred from the outer surface of the cabinet wall to the external environment.

[0026] The flow mode of the closed cavity inside the air box is set to natural convection mode. Natural convection mode means that the flow of dry air inside the air box is driven by the density difference caused by the temperature difference. The dry air near the internal current-carrying conductor is heated and its density decreases, so it moves upward. The area far from the internal current-carrying conductor has a lower temperature and a higher density, so it sinks downward. Thus, a circulating flow driven by buoyancy is formed inside the air box.

[0027] By constructing a complete geometric model that includes cabinet walls, internal current-carrying conductors, insulating supports, and ventilation structures, and accurately identifying the connected regions where dry air can flow freely as the solution region based on the internal space boundary enclosed by the cabinet walls, and setting the density, specific heat capacity at constant pressure, thermal conductivity, and dynamic viscosity of dry air as fluid domain property parameters, the simulation model can realistically reflect the thermodynamic behavior of dry air in a closed cavity.

[0028] By setting the Joule heat loss value of the internal current-carrying conductor under rated load as the bulk heat source and applying it to the area where the conductor is located, and in conjunction with the wall heat transfer boundary conditions on the outer surface of the cabinet wall and the natural convection mode settings of the closed cavity inside the air box, a complete heat transfer and flow simulation boundary was established, providing reliable basic data support for the subsequent spatial distribution calculation of the buoyancy drive strength coefficient and the accurate extraction of airflow organization characteristics.

[0029] In S2, the local temperature gradient value and local characteristic scale value at any spatial location within the switchgear air box are mapped to the buoyancy driving strength coefficient, including: The temperature field is solved by applying simulation boundary conditions to the geometric model of the switchgear gas box, and the temperature distribution data of each spatial location inside the switchgear gas box is obtained. The fluid domain and solid domain of the geometric model are meshed separately to obtain a computational grid containing the fluid domain grid and the solid domain grid. The simulation boundary conditions are applied to the corresponding nodes of the computational grid, and the energy balance relationship at each node is iteratively analyzed until convergence, and the temperature value at each node of the computational grid is obtained. Based on the temperature value at each node of the computational grid, the temperature distribution data of each spatial location inside the switchgear gas box is generated. Numerical difference is performed on the temperature distribution data along the spatial coordinate direction to obtain the local temperature gradient value at any spatial location inside the switchgear air box; The distance between the target spatial location and the nearest wall or the nearest internal entity is used as the local feature scale value of the target spatial location; Mapping local temperature gradient values ​​to local characteristic scale values ​​into buoyancy driving strength coefficients: ; In the formula, Indicates spatial location Buoyancy driving strength coefficient at the location, The coefficient of thermal expansion of dry air, where g is the acceleration due to gravity. Indicates spatial location The local temperature gradient value at that location, Indicates spatial location Local feature scale value at the location, This indicates the kinematic viscosity of dry air.

[0030] Construct a buoyancy-driven intensity distribution map inside the switchgear air box, and extract functional zones of the buoyancy-driven flow field, including: The buoyancy drive strength coefficient is used as the spectral value of each spatial position inside the switchgear air box. The spectral values ​​are arranged into a three-dimensional spatial coordinate system according to the coordinates of each spatial position to generate a buoyancy drive strength distribution map. In the buoyancy-driven intensity distribution map, continuous spatial regions with map values ​​higher than a first preset threshold are marked as airflow uplift-dominant regions, and continuous spatial regions with map values ​​lower than a second preset threshold are marked as backflow stagnation regions. The region dominated by rising airflow and the region of stagnant backflow are used as functional zones for the buoyancy-driven flow field.

[0031] The fluid domain and solid domain of the geometric model are meshed separately. Meshing is the process of discretizing a continuous spatial geometric region into a finite number of tiny units. The fluid domain is the connected space inside the air box where dry air can flow freely. The solid domain is the space occupied by the cabinet walls, internal current-carrying conductors, and insulating supports. When meshing the fluid domain, the mesh is locally refined in the area near the walls. When meshing the solid domain, the appropriate unit type is selected according to the geometry of each solid component.

[0032] The simulation boundary conditions are loaded onto the corresponding nodes of the computational grid. The simulation boundary conditions include fluid domain physical parameters, volume heat source, wall heat transfer boundary conditions, and natural convection mode settings. The corresponding node of the computational grid refers to the vertex position of each micro-cell. The above boundary conditions are assigned to the corresponding nodes according to the spatial location and region type of each node. After loading, an energy balance relationship is established for each node. This energy balance relationship represents the conservation relationship between the heat flowing into the node, the heat flowing out of the node, and the heat generated by the node itself. The energy balance relationship on all nodes is iteratively solved until the temperature value of each node no longer changes between two adjacent iterations. The temperature value of each node at this time is the stable temperature value that satisfies the energy balance.

[0033] Based on the temperature values ​​at each node of the computational grid, the temperature values ​​are arranged in a three-dimensional spatial coordinate system according to the spatial coordinates of each node, generating temperature distribution data covering the entire solution domain. Numerical differencing is performed on the temperature distribution data along the spatial coordinate directions. For each spatial location, the temperature value at the adjacent location along a certain coordinate direction is subtracted from the temperature value at that location, and then divided by the distance between the adjacent location and the current location. This yields the rate of temperature change at that location along that coordinate direction. This operation is performed on all three spatial coordinate directions to obtain the temperature rate of change components in the three directions. These three components are combined into a single spatial vector, which represents the local temperature gradient value at that spatial location. The direction of this vector points towards the direction of the fastest temperature increase, and the magnitude of this vector characterizes the drastic degree of temperature change in space.

[0034] The distance between the target spatial location and the nearest wall or the nearest internal entity is used as the local characteristic scale value of the target spatial location. The internal entity includes internal current-carrying conductors and insulating supports. The distance is determined by continuously expanding a sphere outward from the target spatial location as the center. The expansion stops when the surface of the sphere first contacts the surface of any wall or internal entity. At this time, the radius of the sphere is the distance between the target spatial location and the nearest wall or the nearest internal entity.

[0035] The local temperature gradient value and local characteristic scale value are mapped to the buoyancy driving strength coefficient in the following manner: The first product is obtained by multiplying the modulus of the local temperature gradient value by the fourth power of the local characteristic scale value; the second product is obtained by multiplying the gravitational acceleration by the coefficient of thermal expansion; the third product is obtained by multiplying the first and second products; the square of the kinematic viscosity of the dry air is obtained; and the third product is divided by the square of the kinematic viscosity to obtain the buoyancy driving strength coefficient at that spatial location. This coefficient characterizes the propulsive power of the airflow driven by the temperature gradient at that spatial location. The buoyancy driving strength coefficient calculated for each spatial location is divided by the maximum value of the buoyancy driving strength coefficient across all spatial locations to map the buoyancy driving strength coefficient at each spatial location to a unified numerical range, resulting in a normalized buoyancy driving strength coefficient. The normalized buoyancy driving strength coefficient is only used for generating the buoyancy driving strength distribution map and threshold determination; the calculation of the effective buoyancy driving coefficient uses the buoyancy driving strength coefficient before normalization.

[0036] The value of gravitational acceleration is determined by the altitude and latitude of the switchgear and is used as a constant in the calculation of the buoyancy drive strength coefficient in the airflow circulation simulation of the switchgear. The magnitude of gravitational acceleration directly affects the value of the buoyancy drive strength coefficient. When gravitational acceleration increases, the buoyancy drive strength coefficient also increases. This is because a greater gravitational acceleration will make the buoyancy generated by the density difference stronger, thereby enhancing the driving ability of the airflow.

[0037] The coefficient of thermal expansion originates from the inherent properties of dry air and characterizes the extent to which dry air expands in volume when heated. This parameter is determined by the intermolecular forces and molecular motion characteristics of dry air, and has different values ​​at different temperatures. The coefficient of thermal expansion plays an amplifying role in the calculation of the buoyancy driving strength coefficient. A larger coefficient of thermal expansion means that the same temperature gradient can produce a greater density difference, thus significantly increasing the buoyancy driving strength coefficient, indicating that the spatial location has a stronger buoyancy driving capability under the same temperature gradient.

[0038] The local temperature gradient value is derived from the vector magnitude obtained by numerically differencing the temperature distribution data along the spatial coordinate direction, characterizing the degree of temperature change in space at that location. The magnitude of the local temperature gradient value has an amplifying effect on the buoyancy driving strength coefficient. The larger the temperature gradient value, the more significant the temperature change per unit distance at that location, resulting in a greater density difference and a stronger buoyancy driving effect. Therefore, the buoyancy driving strength coefficient increases linearly with the increase of the local temperature gradient value.

[0039] The local characteristic scale value is derived from the straight-line distance between a spatial location and the nearest wall or interior entity, representing the available space for airflow development at that location. The local characteristic scale value has a strong amplifying effect on the buoyancy-driven intensity coefficient because, after a fourth-degree calculation in the formula, it becomes directly proportional to the buoyancy-driven intensity coefficient. When the local characteristic scale value increases, the buoyancy-driven intensity coefficient increases by a fourth-degree order, indicating that in open spatial regions, the buoyancy driven by the temperature gradient can be more fully converted into upward airflow.

[0040] The kinematic viscosity of dry air is derived from its inherent properties and characterizes its ability to resist flow deformation. This parameter is related to the temperature and pressure of the dry air. Kinematic viscosity has a suppressive effect on the buoyancy driving strength coefficient because it is located in the denominator of the formula. When the kinematic viscosity increases, the buoyancy driving strength coefficient decreases accordingly, indicating that the flow resistance of dry air is greater and the efficiency of temperature gradient-driven airflow is lower.

[0041] The normalized buoyancy drive intensity coefficient is used as the map value for each spatial location inside the switchgear air box. The map values ​​are arranged to their corresponding coordinates in a three-dimensional coordinate system, generating a buoyancy drive intensity distribution map covering the entire internal space of the air box. In this map, continuous spatial regions with map values ​​higher than a first preset threshold are marked as airflow uplift dominance regions, and continuous spatial regions with map values ​​lower than a second preset threshold are marked as backflow stagnation regions. The first preset threshold is greater than the second preset threshold. A continuous spatial region refers to a region where spatial locations whose map values ​​meet the threshold conditions are spatially connected to form a complete area. The boundary contour and central axis orientation of the continuous spatial region of the airflow uplift dominance region are extracted as geometric feature parameters of the natural convection uplift channel. The spatial location and volume ratio of the backflow stagnation region are extracted as positioning parameters for the ventilation structure optimization area. The marked airflow uplift dominance region and backflow stagnation region are collectively used as functional partitions of the buoyancy drive flow field.

[0042] The first preset threshold is obtained by: statistically analyzing the normalized spectrum values ​​of all spatial locations in the buoyancy drive intensity distribution spectrum, sorting all normalized spectrum values ​​from largest to smallest, and taking the minimum value of the spectrum value at the front of the sorted spectrum at a specific proportion as the first preset threshold. This specific proportion is determined based on the desired proportion of the volume occupied by the effective upward airflow channel inside the switchgear air box.

[0043] The second preset threshold is obtained by: statistically analyzing the normalized spectrum values ​​of all spatial locations in the buoyancy drive intensity distribution spectrum, sorting all normalized spectrum values ​​from smallest to largest, and taking the maximum value of the spectrum values ​​at a specific proportion at the front of the sort as the second preset threshold. This specific proportion is determined based on the allowable proportion of the volume occupied by the backflow retention area inside the switchgear air box. The first preset threshold is greater than the second preset threshold, and the spatial area corresponding to the normalized spectrum values ​​between the first preset threshold and the second preset threshold is identified as the normal airflow area.

[0044] By mapping the local temperature gradient values ​​and local characteristic scale values ​​at various spatial locations within the air chamber to a quantified buoyancy-driven strength coefficient, the originally dispersed temperature field data and geometric scale information are integrated into a unified buoyancy-driven capability evaluation index, achieving a refined spatial characterization of the natural convection driving force within a sealed cavity. The buoyancy-driven strength distribution map constructed based on this coefficient clearly presents the continuous spatial distribution pattern of the buoyancy-driven force within the air chamber, from strong to weak. The extracted buoyancy-driven flow field functional zoning precisely defines the spatial boundaries, central axis orientation, and volume proportions of the airflow ascending dominance region and the recirculation stagnation region. This allows designers to directly locate weak points in the ventilation structure, providing a clear spatial basis for determining the positions of the air inlet and outlet in subsequent asymmetric heat dissipation duct configuration schemes.

[0045] In S3, a simulation scheme for the asymmetric heat dissipation duct in the switchgear is constructed, and the simulation field distribution data of the asymmetric heat dissipation duct is obtained, including: Based on the orientation of the central axis of the dominant area of ​​rising airflow in the buoyancy-driven flow field functional zoning, determine the location of the air outlet at the top of the switchgear air box; Based on the spatial location of the backflow retention area in the buoyancy-driven flow field functional zoning, determine the location of the air inlet at the bottom of the switchgear air box; Based on the location of the air outlet and air inlet, the ventilation structure of the air box of the switchgear is configured asymmetrically, with the air inlet and air outlet respectively opened on the wall of the switchgear air box, to construct an asymmetrical heat dissipation air duct simulation scheme. Simulation boundary conditions were applied to the asymmetric heat dissipation duct simulation scheme, and fluid-structure interaction simulation was performed to obtain simulation field distribution data.

[0046] The central axis direction of the airflow rising dominance region is extracted from the functional zoning of the buoyancy-driven flow field. The airflow rising dominance region is a continuous spatial region inside the air box where the buoyancy driving strength coefficient is higher than a first preset threshold. This indicates that the buoyancy driving effect in this region is strong enough to drive dry air to move upward along this region to form an effective airflow rising channel. The central axis direction is the extension path of the center line connecting the centers of this continuous spatial region along the airflow direction in three-dimensional space. This central axis direction reflects the dominant flow direction of the rising airflow inside the air box. The intersection of the extension line of the central axis direction and the top wall of the air box is used as the setting position of the air outlet, so that the center of the air outlet coincides with the intersection point, and the opening of the air outlet faces the external environment.

[0047] The spatial location of the recirculation stagnation area is extracted from the functional partition of the buoyancy-driven flow field. The recirculation stagnation area is a continuous spatial region inside the air box where the buoyancy-driven intensity coefficient is lower than the second preset threshold. It indicates that the buoyancy-driven effect in this region is insufficient, and the dry air cannot form an effective upward flow in this region. It is easy to accumulate in this region and form a heat accumulation dead zone. The spatial location includes the geometric center coordinates and boundary range of this region in three-dimensional space. The setting position of the air inlet is determined according to the spatial location, so that the center of the air inlet is located directly below the geometric center of the recirculation stagnation area, and the opening of the air inlet faces the inside of the air box.

[0048] The ventilation structure of the air box is asymmetrically configured based on the location of the air inlet and outlet. Asymmetrical configuration means that the positions of the air inlet and outlet on the air box wall are not symmetrical. The air inlet is opened on the bottom wall of the air box according to a certain setting position, and the air outlet is opened on the top wall of the air box according to a certain setting position. The projections of the center of the air inlet and the center of the air outlet on the horizontal plane do not coincide, so that the air inlet and outlet form an alternating distribution in space. After completing the asymmetrical configuration, an asymmetrical heat dissipation air duct simulation scheme is obtained. This simulation scheme includes the specific positions and opening geometric parameters of the air inlet and outlet on the air box wall.

[0049] Simulation boundary conditions are applied to the asymmetric heat dissipation duct simulation scheme. These boundary conditions, based on the simulation boundary conditions set in step S1, include additional opening boundary conditions for the air inlet and outlet. These conditions include fluid domain property parameters, volume heat source, wall heat transfer boundary conditions, natural convection mode settings, and the opening boundary conditions for the air inlet and outlet. Based on this, fluid-structure interaction (FSI) simulation is performed. FSI simulation simultaneously solves for the flow field of dry air flowing inside the air chamber and the temperature field of heat transfer between the solid and fluid. The fluid region is the connected space occupied by the dry air inside the air chamber, and the solid region is the cabinet wall and interior... The space occupied by the current-carrying conductor and insulating support components undergoes heat and momentum exchange at the interface between the fluid and solid regions. Through iterative solutions, the flow and temperature parameters of the fluid region and the temperature parameters of the solid region are made to satisfy their respective conservation relationships at the same time, resulting in temperature field distribution data and flow field distribution data under asymmetric configuration. The temperature field distribution data is the set of temperature values ​​at each spatial location inside the air box, and the flow field distribution data is the set of dry air flow velocity vector values ​​at each spatial location inside the air box. The temperature field distribution data and the flow field distribution data are used together as the simulation field distribution data of the asymmetric heat dissipation air duct.

[0050] Fluid-structure interaction (FSI) simulation specifically refers to applying simulation boundary conditions to an asymmetric heat dissipation duct simulation scheme, using the loaded computational mesh as the initial solution object, establishing temperature and heat flux continuity conditions at the fluid-solid interface, and alternately solving between the fluid and solid regions while transferring temperature and heat flux values ​​at the interface. In the fluid region, the temperature and flow fields are obtained using the interface temperature as the boundary condition; in the solid region, the temperature field is obtained using the interface heat flux as the boundary condition. The solution results for the fluid and solid regions are compared. When the difference between the temperature values ​​transferred from the fluid region to the solid region and from the solid region to the fluid region at the interface exceeds a preset tolerance, the temperature and heat flux values ​​at the interface are corrected, and the alternating solution is restarted until the temperature and heat flux values ​​on both sides of the interface meet the consistency condition. The alternating solution is then stopped, and the temperature and flow fields of the fluid region and the temperature field of the solid region at the point where the alternating solution stops are used as the simulation field distribution data for the asymmetric heat dissipation duct.

[0051] Based on the central axis orientation of the airflow rising dominance zone and the spatial location of the return flow retention zone within the buoyancy-driven flow field functional zoning, the placement positions of the air inlets and outlets are determined. The inlets are positioned directly below the return flow retention zone, and the outlets are located at the intersection of the extended line of the central axis of the airflow rising dominance zone and the top wall. These determined positions serve as the quantitative basis for the ventilation configuration, achieving an asymmetrical spatial distribution of the inlets and outlets. Simulation field distribution data obtained through fluid-structure interaction simulation with simulated boundary conditions on this asymmetrical configuration scheme accurately reflects the temperature and flow field characteristics under asymmetrical heat dissipation duct conditions. This provides a complete data foundation for the subsequent quantitative evaluation of the buoyancy-driven effectiveness coefficient, enabling the ventilation structure design scheme to move from qualitative analysis to quantitative evaluation with feasible technical prerequisites.

[0052] In S4, the simulated field distribution data is quantized into effective buoyancy-driven coefficients, including: Based on the simulation field distribution data of the asymmetric heat dissipation air duct, the airflow velocity vector data of each spatial location inside the switch cabinet air box are obtained; When the angle between the airflow velocity vector data of a spatial location and the direction of gravity is greater than 90 degrees, the weighting factor of the spatial location takes a positive value. Based on the buoyancy drive strength coefficient at each spatial location, the internal enclosed cavity volume and included angle of the switchgear air box, the effective buoyancy drive coefficient of the asymmetric heat dissipation air duct is quantified.

[0053] The formula for calculating the effective coefficient of buoyancy drive is: ; In the formula, This represents the effective coefficient of buoyancy drive for asymmetric heat dissipation air channels. Indicates spatial location Buoyancy driving strength coefficient at the location, Indicates spatial location The angle between the airflow velocity vector and the direction of gravity at that location. Indicates spatial location Weighting factor at the location, This indicates the volume of the internal enclosed cavity of the switchgear air box. This represents a volume element.

[0054] Evaluate the ventilation efficiency of asymmetric heat dissipation ducts and determine the optimal duct configuration parameters to guide the ventilation structure design of switchgear, including: The effective coefficient of buoyancy drive of the asymmetric heat dissipation air duct is used as the quantitative evaluation result of the ventilation efficiency of the asymmetric heat dissipation air duct simulation scheme. By changing the configuration parameters of the air inlet and the air outlet, multiple different simulation schemes of asymmetric heat dissipation air ducts were obtained, and the effective coefficient of buoyancy drive corresponding to each scheme was obtained. The inlet and outlet configuration parameters corresponding to the simulation scheme of the asymmetric heat dissipation air duct with the largest effective coefficient of buoyancy drive are used as alternative air duct configuration parameters. Update the configuration of the air inlet and outlet according to the alternative air duct configuration parameters, and repeat the alternative air duct configuration parameter selection operation until the change in the effective coefficient of buoyancy drive in two adjacent selections is less than the preset convergence threshold, and stop the iteration to obtain the optimal air duct configuration parameters. The ventilation structure design of the switchgear is guided by the optimal air duct configuration parameters.

[0055] The airflow velocity vector data of each spatial location inside the switch cabinet air box is extracted from the simulation field distribution data of the asymmetric heat dissipation air duct. The airflow velocity vector data is the set of the direction and magnitude of the dry air flow velocity at each spatial location inside the air box. This data comes from the flow field distribution data obtained after performing fluid-structure interaction simulation on the asymmetric heat dissipation air duct simulation scheme.

[0056] The buoyancy drive intensity coefficients corresponding to each spatial location inside the switchgear air box are obtained from the buoyancy drive intensity distribution map. The angle between the airflow velocity vector direction and the gravity direction at each spatial location is used as the basis for determining the weighting factor. When the angle is greater than 90 degrees, it indicates that the airflow movement direction at that location is opposite to the gravity direction, i.e., the airflow moves upward and is consistent with the buoyancy drive direction. In this case, the weighting factor at that location is set to a positive value. When the angle is less than 90 degrees, it indicates that the airflow movement direction at that location is consistent with the gravity direction, i.e., the airflow moves downward and is opposite to the buoyancy drive direction. In this case, the weighting factor at that location is set to a negative value. When the angle is equal to 90 degrees, it indicates that the airflow at that location moves horizontally and is perpendicular to the buoyancy drive direction. In this case, the weighting factor at that location is set to zero.

[0057] The internal enclosed cavity volume of the switchgear air box is used as the integration region. Within this integration region, the product of the buoyancy drive strength coefficient and the corresponding weighting factor at each spatial location is accumulated. The accumulation method is to divide the integration region into an infinite number of tiny volume units, calculate the product of the buoyancy drive strength coefficient and the weighting factor for each tiny volume unit, multiply it by the volume of that tiny volume unit, and then sum the calculation results of all tiny volume units. The summed result is then divided by the internal enclosed cavity volume to obtain the buoyancy drive effectiveness coefficient of the asymmetric heat dissipation air duct. This coefficient characterizes the overall effective utilization of airflow by buoyancy drive within the entire internal space of the air box. The larger the buoyancy drive effectiveness coefficient, the more effective the airflow circulation formed by natural convection and the higher the heat dissipation efficiency under this asymmetric heat dissipation air duct scheme.

[0058] The effective buoyancy drive coefficient is derived from the product of the buoyancy drive strength coefficient and the weighting factor in the simulation field distribution data, which is then divided by the total volume after being multiplied by the total volume of the entire internal space of the air box. This coefficient represents the overall effective utilization of buoyancy for airflow within the entire air box under the asymmetric heat dissipation air duct scheme. The larger the value of the effective buoyancy drive coefficient, the more effective the airflow circulation formed by natural convection and the higher the heat dissipation efficiency under this air duct configuration.

[0059] The volume of the internal enclosed cavity originates from the total capacity of the internal space enclosed by the cabinet walls in the geometric model of the switchgear air box. This volume serves as the denominator in the integral calculation to normalize the integral result of the product of the buoyancy drive strength coefficient and the weighting factor. The volume of the internal enclosed cavity plays an averaging role in the formula. When the volume increases, the effective coefficient of buoyancy drive decreases accordingly. This is because, under the same buoyancy drive conditions, a larger volume of space requires driving more mass of dry air to participate in the circulation.

[0060] The buoyancy-driven strength coefficient is derived from the calculation result obtained in step S2 by mapping the local temperature gradient value with the local feature scale value. This coefficient is the calculation result before normalization and characterizes the ability of the buoyancy driven by the temperature gradient at each spatial location inside the air box to propel the airflow. The buoyancy-driven strength coefficient is multiplied by the weighting factor in the formula and then participates in the volume integral. Its value has a direct impact on the buoyancy-driven effective coefficient. The larger the buoyancy-driven strength coefficient, the greater the contribution of the location to the overall effective coefficient.

[0061] The angle between the airflow velocity vector and the gravity direction is derived from the angle between the airflow velocity direction at each spatial location and the vertically upward direction extracted from the flow field distribution data obtained after performing fluid-structure interaction simulation on the asymmetric heat dissipation duct simulation scheme. This angle is used to determine whether the airflow motion direction at each spatial location is consistent with the buoyancy drive direction. When the angle is greater than 90 degrees, the weight factor at that location is positive and increases the effective coefficient of buoyancy drive. When the angle is less than 90 degrees, the weight factor at that location is negative and decreases the effective coefficient of buoyancy drive.

[0062] The weighting factor is derived from the value obtained by cosine operation and phase transformation of the angle between the airflow velocity vector and the gravity direction. Specifically, the weighting factor is positive when the angle is greater than 90 degrees, negative when the angle is less than 90 degrees, and zero when the angle is equal to 90 degrees. The weighting factor is multiplied by the buoyancy drive strength coefficient and participates in the volume integral. Its function is to selectively amplify or attenuate the buoyancy drive strength coefficient according to the airflow direction, so as to ensure that the effective buoyancy drive coefficient only reflects the airflow component that contributes positively to heat removal.

[0063] The volume element is derived from the infinitely divided volume of the closed cavity inside the switchgear air box. This element is used to accumulate the product of the buoyancy drive strength coefficient and the weighting factor point by point in the integral operation. The volume element is multiplied by the product of the buoyancy drive strength coefficient and the weighting factor and then integrated. The cumulative effect makes the effective coefficient of buoyancy drive cover the entire internal space of the air box.

[0064] The calculated effective coefficient of buoyancy drive is used as the quantitative evaluation result of the ventilation efficiency of the asymmetric heat dissipation air duct simulation scheme. This result is a numerical index used to quantitatively compare the ventilation efficiency under different air inlet and outlet configuration schemes.

[0065] The configuration parameters of the air inlet and the air outlet are changed. The air inlet configuration parameters include the opening position of the air inlet on the bottom wall of the air box, the opening area of ​​the air inlet, and the opening angle of the air inlet. The air outlet configuration parameters include the opening position of the air outlet on the top wall of the air box, the opening area of ​​the air outlet, and the opening angle of the air outlet. Each change of a set of configuration parameters generates a different asymmetric heat dissipation air duct simulation scheme. Fluid-structure interaction simulation is performed on each scheme and the effective buoyancy drive coefficient corresponding to each scheme is calculated.

[0066] The effective buoyancy drive coefficients of each scheme are numerically compared, and the inlet and outlet configuration parameters corresponding to the scheme with the largest effective buoyancy drive coefficient are selected as candidate air duct configuration parameters. The configuration of the inlet and outlet is updated according to the candidate air duct configuration parameters. Based on this, a new asymmetric heat dissipation air duct simulation scheme is generated, and fluid-structure interaction simulation and effective buoyancy drive coefficient calculation are performed. The newly calculated effective buoyancy drive coefficient is numerically compared with the previous effective buoyancy drive coefficient, and the difference between the two is calculated. When the absolute value of the difference is greater than or equal to the preset convergence threshold, the cycle of configuration update, simulation, and coefficient comparison is repeated until the absolute value of the difference between the effective buoyancy drive coefficients of two adjacent cycles is less than the preset convergence threshold, at which point the cycle stops. The candidate air duct configuration parameters obtained in the last cycle are then taken as the optimal air duct configuration parameters.

[0067] During the iterative optimization process, the preset convergence threshold is determined jointly based on the magnitude of the effective buoyancy drive coefficient and the design accuracy requirements. The setting principle is to set the threshold as the product of the effective buoyancy drive coefficient obtained in the first calculation and the preset proportional coefficient. The preset proportional coefficient is selected as a value less than 1 based on engineering experience. This value ensures that the relative change rate of the effective buoyancy drive coefficient between two adjacent iterations does not exceed a preset percentage when the iteration ends. The specific value of this percentage is determined by the designer based on the heat dissipation performance design tolerance of the switchgear.

[0068] When the effective buoyancy drive coefficient calculated for the first time is 1500, the preset proportional coefficient is set to 0.001, and the preset convergence threshold is 1.5. At this time, the iteration stops when the absolute value of the difference between the effective buoyancy drive coefficients of two adjacent iterations is less than 1.5. That is, when the relative change rate of the effective buoyancy drive coefficients of two adjacent iterations does not exceed 0.1%, the current scheme is considered to have met the convergence condition.

[0069] When the effective buoyancy drive coefficient calculated for the first time is 3000, the preset proportional coefficient is set to 0.0005, and the preset convergence threshold is 1.5. At this time, the iteration stops when the absolute value of the difference between the effective buoyancy drive coefficients of two adjacent iterations is less than 1.5. That is, when the relative change rate of the effective buoyancy drive coefficients of two adjacent iterations does not exceed 0.05%, the current scheme is considered to have met the convergence condition. When higher design accuracy is required, a smaller preset proportional coefficient is used.

[0070] The optimal air duct configuration parameters are used as the design basis for the ventilation structure of the switchgear. Based on the opening position, opening area and opening angle of the air inlet in the parameters, an air inlet is opened on the bottom wall of the air box. Based on the opening position, opening area and opening angle of the air outlet in the parameters, an air outlet is opened on the top wall of the air box, thus completing the final design of the ventilation structure of the switchgear.

[0071] By quantifying the simulation field distribution data of asymmetric heat dissipation ducts into a buoyancy-driven effective coefficient, which comprehensively reflects the consistency between the buoyancy-driven intensity and the airflow direction at various spatial locations within the air box, ventilation efficiency is transformed from incomparable distribution data into a single, quantifiable numerical indicator. This provides a unified quantitative basis for judging the merits of different duct configuration schemes. The inlet and outlet configuration parameters are iteratively adjusted with the goal of maximizing this coefficient. The stopping condition is that the change in the buoyancy-driven effective coefficient between two adjacent iterations is less than a preset convergence threshold. This ensures that the determination of the optimal duct configuration parameters has a clear convergence criterion, automatically obtaining the configuration scheme with the best ventilation efficiency without relying on human experience. This scheme is directly output as the opening position, opening area, and opening angle of the inlet and outlet, respectively, and can be directly used to guide the actual engineering design of the switchgear ventilation structure.

[0072] Figure 2 The process for determining the optimal air duct configuration parameters is demonstrated, which is based on the buoyancy-driven effective coefficient of an asymmetric heat dissipation air duct. The optimization process involves a closed-loop optimization iteration. At the starting point of the optimization path, the system is in an inefficient state of stochastic empirical design with a low buoyancy-driven effective coefficient. By collaboratively adjusting configuration parameters such as the inlet position, outlet coordinates, and orientation angle, multiple simulation alternatives are generated, and their corresponding calculations are performed. The blue curve records the convergence characteristics of this process: the first few iterations corrected the spatial deviation between the channel center and the dominant region of rising airflow, thus... The rapid leap signifies the initial formation of the main channel for asymmetric heat dissipation; subsequent iterations focus on weakening and eliminating localized micro-backflows, such as those in stagnant backflow areas, thus... The process gradually approaches the theoretical efficiency limit. The convergence criterion is set as the change in the effective coefficient of buoyancy drive in two adjacent iterations being less than a preset convergence threshold. This automatic optimization process based on quantitative indicators abandons the inefficient mode of manual interpretation and repeated trial and error in traditional design, realizing a technological leap from descriptive observation to decision-making optimization in switchgear simulation analysis, and ensuring the scientific output of optimal air duct configuration parameters.

[0073] Figure 3 By comparing and analyzing the steady-state temperature rise data of key monitoring points before and after optimization, the actual improvement effect of the method in this embodiment on heat dissipation performance is intuitively demonstrated. The horizontal axis lists five representative monitoring points for the three phases of the current-carrying conductors (phase A, phase B, and phase C) and the air box space (top area and middle area), while the vertical axis represents the steady-state temperature rise (K). Orange bars represent the traditional symmetrical heat dissipation air duct scheme, and green bars represent the asymmetrical heat dissipation air duct scheme optimized by the method in this embodiment. As can be seen from the comparison, under the same rated load conditions, the steady-state temperature rise of the asymmetrical optimized air duct scheme at each monitoring point is significantly lower than that of the traditional symmetrical scheme: the temperature rise of the three phases of the current-carrying conductors decreases from 85K~88K in the traditional scheme to 72K~73K, an absolute reduction of 13K~15K; the temperature rise in the top area of ​​the air box decreases from approximately 65K to 58K, and in the middle area from approximately 55K to 48K. This is thanks to the method in this embodiment, which accurately identifies the dominant rising airflow region and the stagnant return region based on the buoyancy drive intensity distribution map. Based on this, the air inlet and outlet are configured asymmetrically, so that the buoyancy drive airflow circulates along the optimal path, effectively eliminating the heat accumulation dead zone. This reduces the steady-state temperature rise of key parts while ensuring heat dissipation efficiency, and verifies the effectiveness of the precise air duct design based on buoyancy drive intensity zoning in improving the natural convection heat dissipation performance of switchgear.

[0074] Example 2: As Figure 4 As shown, a buoyancy-driven airflow circulation simulation system for a switchgear includes the following modules connected in sequence: The simulation boundary setting module is used to construct the geometric model of the switchgear gas box and set the simulation boundary conditions according to the actual operating conditions of the switchgear gas box. The buoyancy partition extraction module is used to map the local temperature gradient value and local feature scale value at any spatial location in the switchgear air box to the buoyancy drive strength coefficient, and to construct the buoyancy drive strength distribution map inside the switchgear air box, and extract the functional partition of the buoyancy drive flow field. The asymmetric air duct simulation module is used to configure the air box ventilation structure asymmetrically according to the functional zoning of the buoyancy-driven flow field, construct an asymmetric heat dissipation air duct simulation scheme in the switch cabinet, and obtain the simulation field distribution data of the asymmetric heat dissipation air duct. The efficiency evaluation and optimization module is used to quantify the simulation field distribution data into buoyancy drive effective coefficient, evaluate the ventilation efficiency of the asymmetric heat dissipation air duct, and determine the optimal air duct configuration parameters to guide the ventilation structure design of the switch cabinet.

[0075] Example 3: A buoyancy-driven switch cabinet airflow circulation simulation device includes a memory, a processor, and a computer program stored in the memory and run on the processor. The processor executes the program to implement the method in Example 1.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A simulation method for airflow circulation in a buoyancy-driven switchgear, characterized by the following steps: include: S1. Construct the geometric model of the switchgear air box and set the simulation boundary conditions according to the actual operating conditions of the switchgear air box. S2. Map the local temperature gradient value and local characteristic scale value at any spatial location in the switchgear air box to the buoyancy drive strength coefficient, construct the buoyancy drive strength distribution map inside the switchgear air box, and extract the functional partition of the buoyancy drive flow field. S3. Based on the functional zoning of the buoyancy-driven flow field, the structure of the air box ventilation port is configured asymmetrically to construct a simulation scheme for the asymmetrical heat dissipation air duct in the switch cabinet, and the simulation field distribution data of the asymmetrical heat dissipation air duct is obtained. S4. Quantify the simulation field distribution data into buoyancy drive effective coefficient, evaluate the ventilation efficiency of the asymmetric heat dissipation air duct, and determine the optimal air duct configuration parameters to guide the ventilation structure design of the switchgear.

2. The buoyancy-driven airflow circulation simulation method for switchgear as described in claim 1, characterized in that, In S1, a geometric model of the switchgear gas box is constructed, and simulation boundary conditions are set according to the actual operating conditions of the switchgear gas box, including: A geometric model is established based on the spatial geometric configuration parameters of the switchgear gas box; Based on the internal space boundary enclosed by the cabinet walls in the geometric model, the area occupied by the internal solids excluding the switch cabinet is defined as the solution region. The physical properties of dry air within the operating temperature range of the switchgear air box are set as the fluid domain physical properties of the closed cavity inside the air box. The Joule heat loss value of the internal current-carrying conductor in the switchgear gas box under rated load is set as the body heat source. The ambient temperature and convective heat transfer coefficient of the external environment of the switchgear gas box are set as the wall heat transfer boundary conditions of the outer surface of the switchgear cabinet wall, and the flow mode of the closed cavity inside the gas box is set as natural convection mode.

3. The buoyancy-driven airflow circulation simulation method for switchgear as described in claim 1, characterized in that, In S2, mapping the local temperature gradient value and local characteristic scale value at any spatial location within the switchgear air box to a buoyancy driving strength coefficient includes: The temperature field is solved by applying simulation boundary conditions to the geometric model of the switchgear gas box, and the temperature distribution data of each spatial location inside the switchgear gas box are obtained. Numerical difference is performed on the temperature distribution data along the spatial coordinate direction to obtain the local temperature gradient value at any spatial location inside the switchgear air box; The distance between the target spatial location and the nearest wall or the nearest internal entity is used as the local feature scale value of the target spatial location; Mapping local temperature gradient values ​​to local characteristic scale values ​​into buoyancy driving strength coefficients: ； In the formula, Indicates spatial location Buoyancy driving strength coefficient at the location, The coefficient of thermal expansion of dry air, where g is the acceleration due to gravity. Indicates spatial location The local temperature gradient value at that location, Indicates spatial location Local feature scale value at the location, This indicates the kinematic viscosity of dry air.

4. The buoyancy-driven airflow circulation simulation method for switchgear as described in claim 3, characterized in that, The temperature distribution data of various spatial locations inside the switchgear air box were obtained, including: The fluid domain and solid domain of the geometric model are meshed separately to obtain a computational mesh containing the fluid domain mesh and the solid domain mesh; The simulation boundary conditions are applied to the corresponding nodes of the computational grid, and the energy balance relationship at each node is iteratively analyzed until convergence, thus obtaining the temperature values ​​at each node of the computational grid. Based on the temperature values ​​at each node of the computational grid, temperature distribution data for each spatial location inside the switchgear gas box is generated.

5. The buoyancy-driven airflow circulation simulation method for switchgear as described in claim 4, characterized in that, In S2, a buoyancy-driven intensity distribution map is constructed inside the switchgear air box, and the functional partitions of the buoyancy-driven flow field are extracted, including: The buoyancy drive strength coefficient is used as the spectral value of each spatial location inside the switchgear air box. The spectral values ​​are arranged into a three-dimensional spatial coordinate system according to the coordinates of each spatial location to generate a buoyancy drive strength distribution map. In the buoyancy-driven intensity distribution map, continuous spatial regions with map values ​​higher than a first preset threshold are marked as airflow uplift-dominant regions, and continuous spatial regions with map values ​​lower than a second preset threshold are marked as backflow stagnation regions. The region dominated by rising airflow and the region of stagnant backflow are used as functional zones for the buoyancy-driven flow field.

6. The buoyancy-driven airflow circulation simulation method for switchgear as described in claim 1, characterized in that, In S3, an asymmetric heat dissipation duct simulation scheme is constructed in the switchgear to obtain the simulation field distribution data of the asymmetric heat dissipation duct, including: Based on the orientation of the central axis of the dominant area of ​​rising airflow in the buoyancy-driven flow field functional zoning, determine the location of the air outlet at the top of the switchgear air box; Based on the spatial location of the backflow retention area in the buoyancy-driven flow field functional zoning, determine the location of the air inlet at the bottom of the switchgear air box; Based on the location of the air outlet and air inlet, the ventilation structure of the air box of the switchgear is configured asymmetrically, with the air inlet and air outlet respectively opened on the wall of the switchgear air box, to construct an asymmetrical heat dissipation air duct simulation scheme. Simulation boundary conditions were applied to the asymmetric heat dissipation duct simulation scheme, and fluid-structure interaction simulation was performed to obtain simulation field distribution data.

7. The buoyancy-driven airflow circulation simulation method for switchgear as described in claim 1, characterized in that, In S4, the simulation field distribution data is quantized into buoyancy-driven effective coefficients, including: Based on the simulation field distribution data of the asymmetric heat dissipation air duct, the airflow velocity vector data of each spatial location inside the switch cabinet air box are obtained; When the angle between the airflow velocity vector data of a spatial location and the direction of gravity is greater than 90 degrees, the weighting factor of the spatial location takes a positive value. Based on the buoyancy drive strength coefficient at each spatial location, the internal enclosed cavity volume and included angle of the switchgear air box, the effective buoyancy drive coefficient of the asymmetric heat dissipation air duct is quantified.

8. The buoyancy-driven airflow circulation simulation method for switchgear as described in claim 7, characterized in that, The formula for calculating the effective coefficient of buoyancy drive is: ； In the formula, This represents the effective coefficient of buoyancy drive for asymmetric heat dissipation air channels. Indicates spatial location Buoyancy driving strength coefficient at the location, Indicates spatial location The angle between the airflow velocity vector and the direction of gravity at that location. Indicates spatial location The weighting factor at the location, V represents the internal enclosed cavity volume of the switchgear air box.

9. The buoyancy-driven airflow circulation simulation method for switchgear as described in claim 8, characterized in that, In step S4, the ventilation efficiency of the asymmetric heat dissipation duct is evaluated, and the optimal duct configuration parameters are determined to guide the ventilation structure design of the switchgear, including: The effective coefficient of buoyancy drive of the asymmetric heat dissipation air duct is used as the quantitative evaluation result of the ventilation efficiency of the asymmetric heat dissipation air duct simulation scheme. By changing the configuration parameters of the air inlet and the air outlet, multiple different simulation schemes of asymmetric heat dissipation air ducts were obtained, and the effective coefficient of buoyancy drive corresponding to each scheme was obtained. The inlet and outlet configuration parameters corresponding to the simulation scheme of the asymmetric heat dissipation air duct with the largest effective coefficient of buoyancy drive are used as the alternative air duct configuration parameters. Update the configuration of the air inlet and outlet according to the alternative air duct configuration parameters, and repeat the alternative air duct configuration parameter selection operation until the change in the effective coefficient of buoyancy drive in two adjacent selections is less than the preset convergence threshold, and stop the iteration to obtain the optimal air duct configuration parameters. The ventilation structure design of the switchgear is guided by the optimal air duct configuration parameters.

10. A buoyancy-driven airflow circulation simulation system for switchgear, used to implement the method described in any one of claims 1 to 9, characterized in that, include: The simulation boundary setting module is used to construct the geometric model of the switchgear gas box and set the simulation boundary conditions according to the actual operating conditions of the switchgear gas box. The buoyancy partition extraction module is used to map the local temperature gradient value and local feature scale value at any spatial location in the switchgear air box to the buoyancy drive strength coefficient, and to construct the buoyancy drive strength distribution map inside the switchgear air box, and extract the functional partition of the buoyancy drive flow field. The asymmetric air duct simulation module is used to configure the air box ventilation structure asymmetrically according to the functional zoning of the buoyancy-driven flow field, construct an asymmetric heat dissipation air duct simulation scheme in the switch cabinet, and obtain the simulation field distribution data of the asymmetric heat dissipation air duct. The efficiency evaluation and optimization module is used to quantify the simulation field distribution data into buoyancy drive effective coefficient, evaluate the ventilation efficiency of the asymmetric heat dissipation air duct, and determine the optimal air duct configuration parameters to guide the ventilation structure design of the switch cabinet.

Images