A method and system for simulating an HGIS device considering multi-physical field coupling

Abstract

This invention relates to the field of CAE software simulation and analysis technology, and proposes a simulation method and system for HGIS equipment considering multi-physics coupling. The method includes: establishing a 3D model based on the CAD drawings of the HGIS equipment; conducting single-physics simulation, acquiring and matching a baseline dataset and an adjusted dataset, obtaining the coupling level of each unit based on the matching results, and adjusting the mesh; performing a first interpolation and a second interpolation on the baseline and adjusted datasets, and iteratively optimizing the second interpolation until the accuracy requirements are met; and obtaining the simulation results of the HGIS equipment based on the iterative optimization results. This invention can improve the accuracy of data interpolation results for different physical fields, thereby improving the reliability of HGIS equipment simulation results.

Description

Technical Field

[0001] This invention relates to the field of CAE software simulation and analysis technology, specifically to a simulation method and system for HGIS equipment that considers multi-physics coupling. Background Technology

[0002] HGIS (Hybrid Gas Insulated Switchgear) is a key power equipment that combines the compactness of GIS gas-insulated switchgear with the economic efficiency of AIS air-insulated switchgear, and is widely used in high-voltage and ultra-high-voltage transmission systems. The operation of HGIS involves the interaction of multiple physical fields, including electric, magnetic, thermal, and flow fields. Multiphysics coupling simulation is a core technology for the research, development, optimization, and operation and maintenance of HGIS. The integrated HGIS dual-circuit breaker is a compact and integrated HGIS device with a complex structure, numerous parts, and intricate coupling mechanisms. The difficulty in simulating HGIS using multiphysics coupling lies in establishing a simulation model that couples the electric, thermal, and flow fields.

[0003] In the simulation process of HGIS equipment, the general model adopts a "one-size-fits-all" grid strategy to build the grid. In the high coupling area, the grid is prone to being too coarse, which can lead to interpolation distortion. In the low coupling area, the grid is prone to being too dense, which can lead to computational redundancy. Moreover, the grid type, cell size and solution discretization method of different physical fields often differ. Directly transmitting the interpolation results as data can lead to "interface breakage", making the simulation results built from the data unreliable. Summary of the Invention

[0004] This invention provides a simulation method and system for HGIS equipment that considers multi-physics coupling, in order to solve the problem of unreliable HGIS equipment simulation results caused by data interpolation distortion in different physical fields. The specific technical solution adopted is as follows:

[0005] In a first aspect, one embodiment of the present invention provides a simulation method for HGIS equipment considering multi-physics coupling, the method comprising the following steps:

[0006] Create a 3D model based on the CAD drawings of the HGIS equipment;

[0007] Single-physics field simulations of electric field, thermal field and flow field are carried out to obtain the reference datasets of each single physics field. Different combination schemes are set to obtain the corresponding adjustment datasets of the combination schemes. Data alignment and matching are performed on the reference dataset and the adjustment dataset. Based on the matching results of the reference cells in the reference dataset and the cells in the adjustment dataset, the field difference value and the comprehensive coupling sensitivity index are calculated. The coupling level of each cell is obtained based on the comprehensive coupling sensitivity index, and the mesh is adjusted according to the coupling level.

[0008] For the simulation dataset composed of the benchmark dataset and the adjustment dataset, the initial interpolation weights of each physical field are determined based on the correlation coefficients between the data of different physical fields. The first interpolation is performed, the electric field update interval coefficient is preset, and the second interpolation is performed on adjacent elements with the same physical field type, coupling level and material properties. The corresponding weight values ​​are corrected based on the field difference values ​​of different physical fields. The corrected weights are then interpolated again and the results are fused until the field difference values ​​meet the accuracy requirements, thus realizing the iterative optimization of the second interpolation.

[0009] The simulation results of the HGIS equipment are obtained based on the iterative optimization results.

[0010] Furthermore, the specific methods for aligning and matching the baseline dataset and the adjusted dataset include:

[0011] When exporting the adjusted dataset, ensure that the format of the adjusted dataset is completely consistent with that of the baseline dataset;

[0012] Based on the coordinate deviation between the adjusted dataset and the baseline dataset, the baseline cells in the baseline dataset are matched with the cells in each adjusted dataset, and cells that fail to match are removed.

[0013] Furthermore, the specific method for matching the reference cells in the reference dataset with the cells in each adjusted dataset based on the coordinate deviation between the adjusted dataset and the reference dataset includes:

[0014] If the coordinate deviation between the reference cell in the reference dataset and the cell in each adjustment dataset is less than or equal to 0.2 mm, the matching is considered successful.

[0015] Furthermore, the specific calculation methods for the field difference value and the comprehensive coupling sensitivity index are as follows:

[0016] The formula for calculating the field difference value is:

[0017]

[0018] in, Indicates the field difference value; To adjust the field values ​​of the elements in the data that centralize the electric field, thermal field, and flow field; The field quantity values ​​of the reference element for the centralized electric, thermal, and flow fields in the reference data;

[0019] The maximum value of the field quantity of the element that adjusts the electric field, thermal field and flow field in the dataset is used as the comprehensive coupling sensitivity index of that element.

[0020] Furthermore, the coupling hierarchy specifically includes:

[0021] Extremely strong coupling region, strong coupling region, medium coupling region, and weak coupling region.

[0022] Furthermore, the specific method for determining the initial interpolation weights of each physical field based on the correlation coefficients between the data of different physical fields and performing the first interpolation includes:

[0023] The arithmetic mean of the Pearson correlation coefficients between a single physical field and two other physical fields in the same unit with respect to time is denoted as the contribution of the single physical field.

[0024] When the average slope of the data change of a single physical field in the same unit is greater than 0.5, it is determined that the data of the single physical field has effective fluctuations.

[0025] If there is a valid fluctuation in the data of only a single physical field at the coupling level, the interpolation priority is represented by the fluctuation amplitude of the single physical field;

[0026] If there are valid fluctuations in the data of two or three physical fields at the coupling level, the initial interpolation weights are corrected. The initial interpolation weights are the average slope of the data fluctuations. The formula for calculating the corrected initial interpolation weights is as follows:

[0027]

[0028] in, Indicates the contribution of the physical field. This represents the initial interpolation weights, which are the average slope of the data fluctuations. This represents the corrected initial interpolation weights.

[0029] Furthermore, the specific steps for correcting the weight values ​​based on the field difference values ​​of different physical fields include:

[0030] The field differences between the interpolation results of the electric field, thermal field, and flow field and the measured reference values ​​are denoted as electric field error, thermal field error, and flow field error, respectively, and their corresponding weights are obtained.

[0031] When the relative error of any physical field is greater than 10%, interpolation weight correction is triggered. The proportion of the field error to the sum of all field errors is used as the weight adjustment coefficient. The average of the weight adjustment coefficients of electric field error, thermal field error and flow field error is recorded as the average weight adjustment coefficient. The weights corresponding to the field errors are corrected according to the weight adjustment coefficients and the average weight adjustment coefficient to obtain the corrected weights.

[0032] Furthermore, the process of performing a second interpolation on the corrected weights and fusing the results until the field difference value meets the accuracy requirements, thereby achieving iterative optimization of the second interpolation, includes the following specific methods:

[0033] The corrected weights are re-interpolated and the results are fused. If the relative errors of all physical fields are less than or equal to 0.1, the accuracy requirement is met and the iteration is stopped.

[0034] If the accuracy requirement is not met, repeat the second interpolation and iteration until the accuracy requirement is met.

[0035] Furthermore, the simulation results of the HGIS equipment include:

[0036] There are four types of data files: electric field core data, thermal field core data, flow field core data, and interpolation accuracy report.

[0037] Secondly, embodiments of the present invention also provide an HGIS equipment simulation system considering multi-physics coupling, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0038] The beneficial effects of this invention are:

[0039] This application first conducts single-physics field simulations of electric, thermal, and flow fields to obtain benchmark datasets, ensuring that the simulation parameters and solution logic of each physics field conform to professional standards, thus laying a precise single-field data foundation for multi-field coupling. Then, the adjusted dataset and the benchmark dataset are used as comparison datasets to intuitively reflect the interconnected impact of layout parameter changes on the electric, thermal, and flow fields, resolving the "space-cell" mismatch problem in multiple datasets and providing a core basis for subsequent coupling layer classification. Coupling layer classification is completed based on comprehensive coupling sensitivity indicators, breaking away from the "one-size-fits-all" mesh strategy. High-precision meshes are used in extremely strong coupling regions to ensure field gradient capture, while coarser meshes are used in weak coupling regions to reduce computational costs, balancing simulation accuracy and efficiency. Simultaneously, mesh types are customized for the physical characteristics of different coupling regions to eliminate "interface breaks" in inter-field data interpolation, improving the stability of collaborative solutions for multi-field coupling. Furthermore, the first interpolation process dynamically allocates interpolation values ​​according to the characteristics of the coupling region. The first step involves assigning high weights to dominant fields and low weights to secondary fields, avoiding core data distortion caused by "average weighting." This achieves the first-ever collaborative integration of multi-field data, building a data transfer bridge for coupled simulations. The second interpolation and weight iteration optimization process introduces an electric field update interval coefficient, constructing a multi-scale time coupling mechanism. This solves the problem of time scale mismatch between multiple fields. The electric field update interval coefficient enables time step coordination between the electric field and the thermal-fluid field, ensuring the temporal rationality of the coupled simulation. Simultaneously, the second interpolation corrects grid discretization errors, fits the nonlinear gradient changes of field quantities, and then, through measured data calibration and weight iteration, controls field quantity errors within the engineering allowable range, significantly improving the reliability of the coupled simulation. Finally, the iteratively optimized interpolated data is integrated to output standardized simulation results that can directly guide engineering practice, solving the problem of data interpolation distortion in different physical fields leading to unreliable HGIS equipment simulation results. Attached Figure Description

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a flowchart illustrating a simulation method for HGIS equipment considering multi-physics coupling, provided as an embodiment of the present invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Please see Figure 1 The diagram illustrates a flowchart of an HGIS equipment simulation method considering multi-physics coupling according to an embodiment of the present invention. The method includes the following steps:

[0044] Step S001: Create a 3D model based on the CAD drawings of the HGIS equipment.

[0045] Import the CAD drawings of the HGIS equipment into the ANSYS Design Modeler module of the ANSYS software, set up three layers: conductor layer, insulation layer and structure layer, and delete redundant auxiliary lines such as dimension annotations and technical specifications.

[0046] The conductor layer mainly includes the arc-extinguishing chamber and busbars. The steps for modeling the conductor layer are as follows:

[0047] (1) Use the “Sketch Rotation” method to generate a three-dimensional solid. The rotation axis is the central axis of the arc extinguishing chamber. The sketch drawing accuracy needs to be controlled within ±0.05mm. It is merged with the disconnecting switch operating mechanism using “Boolean Unite”, and the axial overlap length is set to 150mm.

[0048] (2) Busbar: adopts "multi-segment line stretching", with a radius of 15mm at the bend to avoid electric field concentration, L-shaped bend angle of 60°, and a 30mm wide SF6 heat dissipation channel reserved.

[0049] The insulation layer mainly includes bushings, corrugated pipes, etc. The steps for modeling the insulation layer are as follows:

[0050] (1) Sleeve: It is constructed by a combination of "rotation modeling + shelling". After shelling, the inner wall thickness of the sleeve needs to be controlled to 20mm. The connection between the sleeve and the self-balancing expansion joint needs to use the "Glue (bind)" command. This operation only retains the contact interface between the two and does not merge the entities, so as to ensure that the contact boundary can be accurately identified during subsequent simulation.

[0051] (2) Corrugated pipe: First, generate the center spiral trajectory of the corrugated pipe through "Helix Curve", and then perform "scan modeling" along the trajectory. The wall thickness of the corrugated pipe is set to 3mm. Its design expansion and contraction must meet the displacement compensation requirement of ±20mm, and the pitch of the spiral trajectory must match the expansion and contraction requirements.

[0052] The structural layer should include the remaining supporting structures of the HGIS equipment. The steps for modeling the structural layer are as follows:

[0053] The installation spacing of the double corrugated pipes is 150mm; the intermediate support component is generated by "cylinder stretching" with a cylinder diameter of 50mm; the assembly of the support component and the equipment housing must be processed by the "Boolean Intersect" command to ensure that the support structure and the housing contact surface are completely fitted without gaps or interference deviations.

[0054] To obtain a 3D model of the HGIS equipment, the following steps were taken: a new "parametric model" project was created in ANSYS Design Modeler, DWG format CAD 2D drawings were imported, and the modeling of all components was completed using "sketch extrusion + Boolean operation" as the core method. Specifically, the arc-extinguishing chamber was modeled with a fine sketch accuracy, the overall conductor extrusion accuracy was ±0.1mm, the insulator was modeled using "rotation", and the SF6 fluid domain was generated using "bounding box subtraction operation".

[0055] The 3D model is validated. Specifically, the "Measure" tool is used to measure key dimensions, and the "Interference Check" tool is used to eliminate structural interference. Key dimensions include contact areas of contacts greater than or equal to 500 mm²; after eliminating structural interference, the interference amount is less than or equal to 0.

[0056] At this point, the 3D model of the HGIS equipment has been obtained.

[0057] Step S002: Conduct single-physics field simulations of electric field, thermal field, and flow field, obtain the benchmark dataset for each single-physics field, set different combination schemes, obtain the adjustment dataset corresponding to the combination schemes, perform data alignment and matching between the benchmark dataset and the adjustment dataset, calculate the field difference value and the comprehensive coupling sensitivity index based on the matching results of the benchmark unit in the benchmark dataset and the unit in the adjustment dataset, obtain the coupling level of each unit based on the comprehensive coupling sensitivity index, and adjust the mesh according to the coupling level.

[0058] The safety and reliability of HGIS equipment hinges on its heat dissipation performance. However, the miniaturization process, which severely compresses internal space, significantly exacerbates heat accumulation in key components such as circuit breakers and disconnectors, posing a serious challenge to heat dissipation capabilities. The quality of the heat dissipation design directly determines the safe and reliable operation of miniaturized HGIS dual circuit breaker equipment. The compact structure leads to a highly dense internal component layout, making heat accumulation even more prominent and placing higher demands on the heat dissipation capabilities of core components like circuit breakers and disconnectors. Efficient heat dissipation design must focus on the uniformity of the thermal field distribution and the control of hotspot temperature rise, especially ensuring that the steady-state temperature rise meets standards during long-term operation. To this end, electromagnetic-thermal-airflow coupling simulation models need to be constructed for circuit breakers and disconnectors. While ensuring that the equipment temperature rise does not exceed standard limits, a compact and efficient heat dissipation solution that balances heat dissipation efficiency and space utilization should be proposed by optimizing the heat dissipation layout and improving the housing heat dissipation structure.

[0059] The core technical challenge in developing this solution lies in constructing a coupled simulation model of the electro-thermal-fluid fields. On one hand, the HGIS equipment has a complex structure and numerous components, with interwoven field coupling mechanisms, significantly increasing the difficulty of model building. On the other hand, the time scales of the turbulent flow model and the electrostatic field solver differ greatly, making direct iteration prone to divergence. Therefore, a custom coupling algorithm needs to be developed to further enhance model convergence. Thus, an efficient coupled simulation method is urgently needed. Specifically, the inter-field coupling mechanisms include the conversion of electric field losses into heat sources, the influence of thermal field temperature on electric field resistance, and temperature gradients driving flow. The significant time scale differences between the turbulent flow model and the electrostatic field solver—for example, the turbulent dynamic process is on the millisecond scale, while the electrostatic field solution is a steady-state solution—further complicate this issue.

[0060] In the compact design of HGIS dual circuit breakers, the highly integrated structural layout, such as the overlapping of the arc-extinguishing chamber and the operating mechanism, and the sharing of bushings, leads to significant regional differences in inter-field coupling relationships. Specifically, the electric field loss, thermal field temperature, and flow field velocity in the contact area are strongly coupled, while the shell area is dominated by only a single thermal field. During simulation, a mesh model of the equipment must be constructed first. If a uniform mesh strategy is adopted, a double dilemma arises—the mesh in the high-coupling area is too coarse, leading to interpolation distortion; the mesh in the low-coupling area is too dense, causing computational redundancy. The essence of multi-physics coupling is "inter-field data interaction," such as electric field loss being transferred to the thermal field as a heat source, and thermal field temperature being fed back to the electric field, affecting resistance parameters. However, different physical fields have inherent differences in mesh type, element size, and solution discretization method. Direct data transfer easily leads to "interface breakage" problems. These inherent differences include, for example, the boundary element method is commonly used for electric fields, while the finite volume method is commonly used for thermal-fluid fields. Therefore, ensuring seamless and distortion-free data transmission between fields is a core prerequisite for three-field coupling. For HGIS equipment simulation, the quality of data interpolation directly determines the reliability and engineering applicability of the simulation results. Therefore, this application focuses on improving the quality of data interpolation.

[0061] First, single-physics simulations of electric, thermal, and flow fields were conducted using Maxwell, Mechanical, and Fluent, respectively. It is understood that Maxwell, Mechanical, and Fluent are all core professional modules under the ANSYS simulation platform, which are used for single-physics simulations in the fields of electromagnetics, structural mechanics, and fluid mechanics, respectively. They are the basic tools for multi-physics coupling analysis in engineering and scientific research.

[0062] The specific process and parameter settings for single-physics simulation are as follows.

[0063] The Maxwell electric field simulation and parameter setting process includes: setting the solution type to "electrostatic field"; configuring the boundary conditions as follows: applying a rated voltage of 252kV to the busbar and setting the equipment casing as the ground boundary; using uniform cell size of 2mm for mesh generation; and setting the convergence criterion as a convergence residual less than or equal to 1e-8, stopping the iteration after this condition is met.

[0064] The mechanical thermal field simulation and parameter setting process includes: the analysis type is "steady-state thermal analysis"; the boundary condition is set to an ambient temperature of 25℃ and the convective heat transfer coefficient of the equipment surface is 15W / (m²・K); the thermal load is applied as the initial loss value, which is calculated by multiplying the square of the rated operating current of the HGIS equipment conductor component by the DC resistance of the corresponding conductor component in the HGIS equipment, wherein the rated operating current is 3150A.

[0065] The Fluent flow field simulation and parameter setting process includes: the analysis type is "steady-state flow field analysis"; the turbulence model is the k-ωSST model, which is suitable for the turbulence characteristics of SF6 gas; the boundary conditions are configured such that the SF6 gas inlet pressure is equal to 0.6 MPa and the outlet pressure is equal to 0.59 MPa, forming a stable pressure gradient to drive the airflow.

[0066] After completing the simulations of each single physics field and confirming the convergence of the results, export the full model mesh element data. The data format is uniformly standardized as "element number-coordinate (XYZ)-field value", where the field value must correspond to each physics field type: electric field simulation exports "field strength" data, thermal field simulation exports "temperature" data, and flow field simulation exports "flow velocity" data, forming the benchmark datasets for the three single physics fields.

[0067] Furthermore, considering the structural characteristics of HGIS equipment, to avoid interference from invalid variables and focus on core influencing factors, three types of layout parameters that have the most significant impact on multi-physics coupling effects were selected as fine-tuning objects, and an adjustment dataset was obtained. The layout parameters include busbar parameters, insulation parameters, and heat dissipation parameters. The fine-tuning range of the layout parameters is as follows: the benchmark value of the bending angle of the busbar parameter is 60°, and the fine-tuning range is ±5°; the benchmark value of the bushing spacing of the insulation parameter is 350mm, and the fine-tuning range is ±10mm; the benchmark value of the heat dissipation channel width of the heat dissipation parameter is 30mm, and the fine-tuning range is ±5mm.

[0068] Based on the above parameters and fine-tuning range, five sets of layout fine-tuning combinations were designed and generated, as follows:

[0069] Combination Scheme 1: Busbar 65° + Bushing 360mm + Channel 35mm.

[0070] Combination Scheme 2: Busbar 55° + Bushing 360mm + Channel 25mm.

[0071] Combination Scheme 3: Busbar 60° + Bushing 340mm + Channel 35mm.

[0072] Combination Scheme 4: Busbar 65° + Bushing 340mm + Channel 25mm.

[0073] Combination Scheme 5: Busbar 55° + Bushing 350mm + Channel 30mm.

[0074] It is important to understand that each layout scheme can define a layout adjustment model.

[0075] To quantify the impact of layout fine-tuning on multiphysics, it is necessary to compare the simulation data of the baseline model and the fine-tuned model through element matching and field difference calculation. The specific process includes simulation data alignment, element matching rules, field difference calculation, and outlier handling. The simulation data of the baseline model is the baseline dataset, and the simulation data of the fine-tuned model is the adjustment dataset.

[0076] Specifically, simulation data alignment includes: repeatedly performing single-physics simulations on the layout adjustment models determined by the five layout schemes, and exporting an adjusted dataset with a format completely consistent with the benchmark dataset. It is necessary to ensure that the correspondence between "cell number-coordinate" in the data is globally unique, laying the foundation for subsequent cell matching. The single-physics simulations include Maxwell electric field simulation, Mechanical thermal field simulation, and Fluent flow field simulation.

[0077] Specifically, the element matching rules include: using a coordinate deviation of less than or equal to 0.2 mm as the matching criterion, mapping and matching the baseline elements in the baseline dataset with the elements in each adjustment dataset one by one; if the matching criterion is met, the matching is considered successful; elements that fail to match are directly removed to ensure that the elements participating in the difference calculation have consistent spatial positions. Among them, elements that fail to match include newly added boundary elements and redundant elements generated by structural fine-tuning.

[0078] Specifically, the field difference value calculation includes: for each successfully matched unit, calculating the field difference values ​​of the electric field, thermal field, and flow field according to the following formula, and then taking the maximum value of the three field difference values ​​as the comprehensive coupling sensitivity index of the unit. When setting the comprehensive coupling sensitivity index, since changes in a single physical field can easily trigger multi-physics field linkages, using the maximum value to determine the comprehensive coupling sensitivity index can more intuitively reflect the degree of influence of the parameters on the coupling effect; the comprehensive coupling sensitivity index is the calculation result of the combined field difference values ​​of three different physical fields.

[0079]

[0080] in, Indicates the field difference value; To adjust the field values ​​of the cells in the dataset, the units of the electric field, thermal field, and flow field values ​​are / kV·mm. -1 / ℃ and / m·s -1 ; The field quantity values ​​of the reference cells in the reference dataset.

[0081] Specifically, outlier handling includes: pre-removal unit, The unit is a low field strength region to avoid the problem of "division by zero error" or abnormal amplification of difference value in the calculation. For extreme field difference values ​​greater than 0.5 after calculation, the convergence state of the corresponding simulation needs to be manually checked. The focus is on checking whether the residual meets the preset standard. If the simulation result is confirmed to be valid, the data is retained. Otherwise, the data is removed and the simulation is carried out again.

[0082] To achieve accuracy and efficiency in multiphysics coupling simulation, it is necessary to classify the coupling layer based on comprehensive coupling sensitivity index and to perform differentiated mesh adjustments for different coupling regions obtained from the coupling layer classification. The specific process includes setting the classification threshold and mesh division, which consists of two parts.

[0083] Specifically, the purpose of setting the hierarchical threshold is to obtain the coupling level: Specifically, in combination with the engineering design requirements of HGIS equipment, the comprehensive coupling sensitivity index of each unit is used as the core index, and the entire domain unit is divided into 4 coupling levels. The specific hierarchical standards are shown in Table 1 below.

[0084]

[0085] Table 1 Grading Standards

[0086] Among them, the larger the comprehensive coupling sensitivity index of the unit, the higher the sensitivity of the unit to changes in layout parameters, and the stronger the inter-field coupling effect.

[0087] Specifically, mesh generation includes adjusting the mesh based on the coupling level and material properties of the elements.

[0088] Among them, the same type of unit must meet the requirements of the same coupling level and the same material properties. The material properties are defined in advance by the "Engineering Data" module in the modeling process to ensure that the material properties can be identified when the mesh is generated. The component materials include copper, epoxy resin, SF6, etc.

[0089] Specifically, the mesh is adjusted according to the coupling level. The specific adjustment method for the mesh is as follows:

[0090] ① Extremely strong coupling region:

[0091] Element type: Select "Tetrahedral", and element order: Select "Quadratic". Tetrahedral is a tetrahedral element, and Quadratic is a quadratic element, which can improve gradient capture capability.

[0092] Size control: Set the cell size range to greater than or equal to 0.8mm and less than or equal to 1.2mm through the “Size” function. Perform “Curvature Refinement” on the contact edge in “Refine”. “Curvature Refinement” is curvature refinement. When the curvature radius is ≤5mm, the mesh will be automatically densified.

[0093] Boundary layer treatment: Three boundary layers are generated on the outer side of the contact surface between SF6 and the conductor. The boundary layer growth rate is set to 1.2, and the y+ value is controlled in the range of 510 to adapt to the Fluent turbulence model solution.

[0094] Understandably, the regional field gradient in the extremely strong coupling region is large and the coupling effect is strong, so it is necessary to prioritize the accuracy of the mesh and the gradient capture capability.

[0095] ②Strongly coupled region:

[0096] Element type: "Hybrid" is used. Regular structures such as conductors use Hexahedral elements, and complex structures such as bellows use Tetrahedral elements. Hybrid is a hybrid mesh, Hexahedral is a hexahedron, and Tetrahedral is a tetrahedron.

[0097] Size control: The element size range is greater than or equal to 1.2 mm and less than or equal to 1.8 mm. Shared Topology is enabled at critical interfaces such as metal-insulation to eliminate the interface gaps in inter-field data interpolation. Shared Topology is a shared-node mesh.

[0098] Quality constraints: Control the mesh distortion rate to ≤0.4, monitor it in real time using the Mesh Metrics function, and manually adjust the "Inflation" parameter to optimize mesh quality for areas exceeding the standard.

[0099] Understandably, the region structure of strongly coupled areas is complex and has multiple media interfaces, requiring a balance between mesh quality and the continuity of data interaction between fields.

[0100] ③Mid-coupling region:

[0101] Element type: Hexahedral Dominant mesh is used, and Sweep mesh technology is used for straight structures with equal-length busbars to improve mesh regularity. Hexahedral Dominant means hexahedral dominance, and Sweep means sweeping.

[0102] Size control: The unit size range is greater than or equal to 2.0 mm and less than or equal to 5.0 mm; five expansion layers are set along the flow direction in the SF6 fluid domain, and the thickness of a single expansion layer is 0.5 mm to enhance the interpolation accuracy of convective heat transfer.

[0103] Mesh coherence: The mesh size transition rate at the inlet and outlet of the heat dissipation channel is ≤1.5 to avoid meaningless abrupt changes in flow rate calculation.

[0104] Understandably, the intermediate coupling region is mainly characterized by flow-thermal coupling, and it is necessary to ensure the adaptability of the flow characteristics of the fluid domain mesh.

[0105] ④Weak coupling area:

[0106] Element type: Coarse Hexahedral element is selected, and the mesh is generated using the Automatic method. Coarse Hexahedral means coarse hexahedral, and Automatic means automatic meshing.

[0107] Size control: The unit size range is greater than or equal to 5.0 mm and less than or equal to 15 mm; for non-critical structures such as fixing bolts, they are hidden by the Suppress function and replaced by equivalent Beam Element, where Suppress means suppression and Beam Element means beam element. The equivalent replacement must meet the stiffness equivalence principle, that is, the EI value before and after equivalence remains consistent.

[0108] It is understandable that the regional field changes are gradual in the weakly coupled region, and computational efficiency can be improved by coarsening the mesh and simplifying the structure accordingly.

[0109] This completes the adjustment of each coupling layer.

[0110] Step S003: For the simulation dataset composed of the benchmark dataset and the adjustment dataset, determine the initial interpolation weights of each physical field based on the correlation coefficients between the data of different physical fields, perform the first interpolation, preset the electric field update interval coefficient, perform the second interpolation for adjacent elements with the same physical field type, coupling level and material properties, and correct the corresponding weight values ​​based on the field difference values ​​of different physical fields. Perform the second interpolation on the corrected weights and fuse the results until the field difference values ​​meet the accuracy requirements, thereby realizing the iterative optimization of the second interpolation.

[0111] Interpolation is the core bridge for cross-domain data transfer between electric, thermal, and flow fields. The dynamic characteristics of the data in the coupling zone are a key factor limiting interpolation accuracy. For example, the electric field loss in the extremely strong coupling zone of an HGIS device increases with temperature due to the rising conductor resistance, causing the electric field loss in the extremely strong coupling zone to increase synchronously with temperature rise. Furthermore, the temperature distribution in the strong coupling zone changes with fluctuations in SF6 gas flow rate. Therefore, the core control logic for interpolation calculation is to assign high interpolation weights to the dominant physical fields at the coupling level and low weights to secondary physical fields. This differentiated weight allocation highlights the interpolation priority of key fields, avoiding distortion of core data due to "averaging" weights. The dataset consisting of the benchmark dataset and the adjusted dataset is used as the simulation dataset. The first interpolation process is performed on the real dataset. The specific operation process and weight binding rules for the first interpolation include three parts: data spatiotemporal alignment preprocessing, inter-field data correlation analysis, and dynamic adjustment rules for interpolation weights.

[0112] Specifically, the data spatiotemporal alignment preprocessing is as follows: For all elements in each coupling level that have completed mesh adjustment, a unified "element number-coordinate" association identifier is set for the elements in the three simulation modules: Maxwell electric field, Mechanical thermal field, and Fluent flow field. Simulation data of the three physical fields are exported at the same time step, with the data format uniformly standardized as "time-element number-electric field strength / loss-thermal field temperature-flow field velocity / pressure," ensuring that the electric field, thermal field, and flow field data are completely aligned in the time and spatial dimensions, laying the foundation for subsequent inter-field correlation analysis. The same time step is, for example, 3 seconds per step under rated operating conditions.

[0113] Specifically, the process of inter-field data correlation analysis is as follows: For the data of the three physical fields in the same unit, change curves of "electric field data-time", "thermal field data-time", and "flow field data-time" are generated respectively; the Pearson correlation coefficient is used to quantify the correlation between the numerical changes of any two physical fields, and the correlation coefficients of the three sets of field quantities—electric field and thermal field, electric field and flow field, and thermal field and flow field—are accurately calculated. The closer the absolute value of the correlation coefficient is to 1, the stronger the linkage between the changes of the two sets of field quantities; the arithmetic mean of the Pearson correlation coefficients of the change curves of a single physical field and the other two physical fields is taken, and this mean is defined as the contribution of that physical field. The higher the contribution, the stronger the driving effect of that field on the overall field distribution of the coupling level, and the more it is the core carrier of inter-field data transmission.

[0114] Specifically, the fluctuation characteristics of the electric field, thermal field, and flow field at the coupling level determine whether the interpolation weight needs to be adjusted through contribution. The specific process of the dynamic adjustment rule of the interpolation weight includes three parts: setting the fluctuation change judgment criteria, setting the weight adjustment trigger conditions, and setting the weight correction formula.

[0115] ① Set criteria for judging fluctuations:

[0116] The average slope of the data change of a single physical field in the same unit is greater than 0.5 as the criterion for determining that the field quantity has significant fluctuations. If the criterion is met, the data of the corresponding physical field is determined to have effective fluctuations.

[0117] ② Set the trigger conditions for weight adjustment:

[0118] If only a single physical field in the coupling layer has valid fluctuations in its data, then there is no need to adjust the weights based on the contribution; the fluctuation amplitude of that physical field can be used to represent the interpolation priority directly. If two or three physical fields in the coupling layer have valid fluctuations in their data, then the initial interpolation weights need to be dynamically adjusted based on the contribution of each field to ensure the interpolation accuracy of the core field.

[0119] ③ Set the weight adjustment formula:

[0120] When the weight adjustment trigger condition is met, the corrected initial interpolation weights are calculated using the following formula:

[0121]

[0122] in, Indicates the contribution of the physical field. This represents the initial interpolation weights, which are the average slope of the data fluctuations. This represents the corrected initial interpolation weights, i.e., the corrected interpolation weights of the physical field.

[0123] This completes the first interpolation.

[0124] Single-pass interpolation data transfer cannot completely eliminate systematic errors such as grid discretization errors and interpolation strategy adaptation deviations. Furthermore, the compact design of HGIS equipment demands extremely high accuracy in simulation data; a simulation error exceeding 5°C in contact temperature may lead to insufficient insulation design margin, while an field strength error exceeding 1kV / mm can cause safety hazards such as insulation breakdown. Simultaneously, multi-physics coupling exhibits significant time-scale differences: the electric field is a steady-state field, while the thermal and flow fields are transient. Therefore, when iteratively adjusting interpolation weights, a multi-scale time coupling mechanism needs to be constructed. A second interpolation calculation is performed based on the initial weights, and the electric field is introduced to update the interval coefficient, continuously evaluating the degree to which the interpolation scheme fits the coupling results.

[0125] In this embodiment, a second interpolation calculation is performed using a thermal field as an example. For any given element, adjacent elements with the same physical field type, coupling level, and material properties are selected. Based on the three field source data, a second interpolation is performed on a single field quantity with adjacent elements. The results of all second interpolations for any given element are weighted and summed according to the corrected interpolation weights of the physical field to calculate the temperature value corresponding to the given element and obtain the result of the second interpolation.

[0126] The three source data are the electric field loss derived temperature, the thermal field directly calculated temperature, and the flow field corrected temperature, respectively; the quadratic interpolation is achieved by performing surface interpolation through bivariate quadratic surface fitting interpolation.

[0127] The second interpolation calculation is iteratively optimized. The iterative optimization process includes four parts: acquisition of measured benchmark data, setting of multi-scale time coupling parameters, error calculation and weight adjustment triggering, and weight optimization and iterative convergence determination.

[0128] Specifically, the process of acquiring measured benchmark data is as follows: fiber optic temperature sensors and partial discharge sensors are installed at key locations such as the contacts and busbars of the HGIS equipment to collect the temperature and field strength under rated operating conditions, which are recorded as measured benchmark data. The measured benchmark data is used as the accuracy verification benchmark for the simulation results.

[0129] Specifically, the process of setting the multi-scale time coupling parameters is as follows: the electric field update interval coefficient N is set to a value range of greater than or equal to 30 and less than or equal to 100, with a step size of 10. The physical meaning of N is: after completing the calculation of N transient time steps of the thermal-fluid field, the steady-state electric field is solved again, and the loss heat source input of the electric field to the thermal field is updated.

[0130] Specifically, the error calculation and weight adjustment triggering process is as follows: referring to the calculation method of field difference value, the field difference values ​​between the interpolation results of the electric field, thermal field, and flow field and the measured reference values ​​are calculated respectively, and recorded as electric field error. Thermal field error and flow field error The corresponding weights are respectively , and If the relative error of any physical field is greater than 10%, the interpolation weight correction process is triggered. The relative error is the field difference value between different physical fields.

[0131] Specifically, the process of weight optimization and iterative convergence determination is as follows:

[0132] ① Calculate the proportion of each field error to the total error. The total error is the sum of all field errors. For example, the weighting adjustment coefficient. The formula for calculation is:

[0133]

[0134] in, It can be any one of E, T, and F, where E is the field strength / loss, T is the temperature, and F is the flow rate / pressure.

[0135] The weights corresponding to the field error are corrected based on the weight adjustment coefficient and the average weight adjustment coefficient to obtain the corrected weights. Specifically, the difference between the weight adjustment coefficient corresponding to the field error and the average weight adjustment coefficient is calculated, and the sum of this difference and the value of 1 is recorded as the corrected weight.

[0136] As an example, taking the correction of the weights corresponding to the electric field error as an example, the corrected weights are... The formula for calculation is:

[0137]

[0138] In the formula, This represents the average weighting adjustment coefficient, which is the average of the weighting adjustment coefficients for electric field error, thermal field error, and flow field error.

[0139] ② Iterative loop: The second interpolation and result fusion are re-executed using the corrected weights. If the relative error of all physical fields is less than or equal to 0.1, the accuracy requirement is met and the iteration stops. If the requirement that the relative error of all physical fields is less than or equal to 0.1 is not met, the accuracy requirement is not met, and the above process is repeated until the accuracy requirement is met.

[0140] This completes the iterative optimization of the second interpolation.

[0141] Step S004: Obtain the simulation results of the HGIS equipment based on the iterative optimization results.

[0142] After the second interpolation iteration optimization, the simulation results of the HGIS equipment that can accurately reflect the coupling law of multi-physics fields are output. The simulation results include four types of data files: electric field core data, thermal field core data, flow field core data, and interpolation accuracy report.

[0143] Specifically, the core electric field data is presented in an Excel spreadsheet. The table includes columns for unit number, location description, electric field strength value, error range, and design recommendations. Location descriptions include phrases like "common bushing location" and "contact edge." Electric field strength values ​​are in kV / mm units. Design recommendations include, for example, "Electric field strength 7.8 kV / mm, the equipotential ring diameter needs to be increased." Key data points for the core electric field are: electric field strength at the common bushing location (6.2 kV / mm), electric field strength at the contact edge (7.8 kV / mm), and electric field strength in the main busbar section (4.5 kV / mm).

[0144] Specifically, the core thermal field data is presented using Tecplot contour plots and an Excel reference table. The Tecplot contour plot of the core thermal field data is a temperature contour plot, colored by grade and with hotspot locations marked: blue for temperatures greater than or equal to 25℃ and less than 50℃, yellow for temperatures greater than or equal to 50℃ and less than 80℃, and orange for temperatures greater than or equal to 80℃ and less than 100℃. The Excel reference table of the core thermal field data shows a maximum contact temperature of 82℃, a main busbar temperature rise of 57K, and a shell temperature rise of 28K, simultaneously indicating the temperature rise limits for the corresponding parts in the GB / T11022 standard.

[0145] Specifically, the core flow field data is presented using Fluent velocity vector screenshots and Excel parameter tables. The Fluent velocity vector screenshots of the core flow field data are velocity distribution cloud maps of the heat dissipation channels, marking the locations of maximum velocities. The Excel parameter tables of the core flow field data include the velocity values ​​and convective heat transfer coefficients for each heat dissipation channel, and the convective heat transfer coefficients meet the design requirement of ≥30W / (m²・K).

[0146] Specifically, the interpolation accuracy report is presented in a Word document. The report includes interpolation errors, conservation errors, and field fluctuation values ​​for each coupling region, and includes screenshots of the original sensor measurement data to verify the reliability and engineering applicability of the simulation results.

[0147] Thus, HGIS equipment simulation was achieved under the premise of considering multi-physics coupling.

[0148] Based on the same inventive concept as the above method, this embodiment of the invention also provides an HGIS equipment simulation system considering multi-physics coupling, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described HGIS equipment simulation methods considering multi-physics coupling.

[0149] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A simulation method for HGIS equipment considering multiphysics coupling, characterized in that, The method includes the following steps: Create a 3D model based on the CAD drawings of the HGIS equipment; Single-physics field simulations of electric field, thermal field and flow field are carried out to obtain the reference datasets of each single physics field. Different combination schemes are set to obtain the corresponding adjustment datasets of the combination schemes. Data alignment and matching are performed on the reference dataset and the adjustment dataset. Based on the matching results of the reference cells in the reference dataset and the cells in the adjustment dataset, the field difference value and the comprehensive coupling sensitivity index are calculated. The coupling level of each cell is obtained based on the comprehensive coupling sensitivity index, and the mesh is adjusted according to the coupling level. For the simulation dataset composed of the benchmark dataset and the adjustment dataset, the initial interpolation weights of each physical field are determined based on the correlation coefficients between the data of different physical fields. The first interpolation is performed, the electric field update interval coefficient is preset, and the second interpolation is performed on adjacent elements with the same physical field type, coupling level and material properties. The corresponding weight values ​​are corrected based on the field difference values ​​of different physical fields. The corrected weights are then interpolated again and the results are fused until the field difference values ​​meet the accuracy requirements, thus realizing the iterative optimization of the second interpolation. The simulation results of the HGIS equipment are obtained based on the iterative optimization results; The adjustment dataset is obtained by selecting layout parameters, including bus parameters, insulation parameters, and heat dissipation parameters, as the fine-tuning objects. The combined scheme includes: Combination Scheme 1: Busbar 65° + Bushing 360mm + Channel 35mm; Combination Scheme 2: Busbar 55° + Bushing 360mm + Channel 25mm; Combination Scheme 3: Busbar 60° + Bushing 340mm + Channel 35mm; Combination Scheme 4: Busbar 65° + Bushing 340mm + Channel 25mm Combination Scheme 5: Busbar 55° + Bushing 350mm + Channel 30mm; The specific calculation methods for the field difference value and the comprehensive coupling sensitivity index are as follows: The formula for calculating the field difference value is: in, Indicates the field difference value; To adjust the field values ​​of the elements in the data that centralize the electric field, thermal field, and flow field; The field quantity values ​​of the reference element for the centralized electric, thermal, and flow fields in the reference data; The maximum value of the field quantity of the element that adjusts the electric field, thermal field and flow field in the dataset is used as the comprehensive coupling sensitivity index of that element. The specific steps for correcting the weight value based on the field difference value of different physical fields are as follows: The field differences between the interpolation results of the electric field, thermal field, and flow field and the measured reference values ​​are denoted as electric field error, thermal field error, and flow field error, respectively, and their corresponding weights are obtained. When the relative error of any physical field is greater than 10%, interpolation weight correction is triggered. The proportion of the field error to the sum of all field errors is used as the weight adjustment coefficient. The average of the weight adjustment coefficients of electric field error, thermal field error and flow field error is recorded as the average weight adjustment coefficient. The weights corresponding to the field errors are corrected according to the weight adjustment coefficients and the average weight adjustment coefficient to obtain the corrected weights.

2. The HGIS equipment simulation method considering multi-physics coupling according to claim 1, characterized in that, The specific methods for aligning and matching the baseline dataset and the adjusted dataset are as follows: When exporting the adjusted dataset, ensure that the format of the adjusted dataset is completely consistent with that of the baseline dataset; Based on the coordinate deviation between the adjusted dataset and the baseline dataset, the baseline cells in the baseline dataset are matched with the cells in each adjusted dataset, and cells that fail to match are removed.

3. The HGIS equipment simulation method considering multiphysics coupling according to claim 2, characterized in that, The specific method for matching the reference cells in the reference dataset with the cells in each adjusted dataset based on the coordinate deviation between the adjusted dataset and the reference dataset includes: If the coordinate deviation between the reference cell in the reference dataset and the cell in each adjustment dataset is less than or equal to 0.2 mm, the matching is considered successful.

4. The HGIS equipment simulation method considering multi-physics coupling according to claim 1, characterized in that, The coupling hierarchy specifically includes: Extremely strong coupling region, strong coupling region, medium coupling region, and weak coupling region.

5. The HGIS equipment simulation method considering multi-physics coupling according to claim 1, characterized in that, The method for determining the initial interpolation weights of each physical field based on the correlation coefficients between data from different physical fields, and performing the first interpolation, includes the following specific methods: The arithmetic mean of the Pearson correlation coefficients between a single physical field and two other physical fields in the same unit with respect to time is denoted as the contribution of the single physical field. When the average slope of the data change of a single physical field in the same unit is greater than 0.5, it is determined that the data of the single physical field has effective fluctuations. If there is a valid fluctuation in the data of only a single physical field at the coupling level, the interpolation priority is represented by the fluctuation amplitude of that single physical field; If there are valid fluctuations in the data of two or three physical fields at the coupling level, the initial interpolation weights are corrected. The initial interpolation weights are the average slope of the data fluctuations. The formula for calculating the corrected initial interpolation weights is as follows: in, Indicates the contribution of the physical field. This represents the initial interpolation weights, which are the average slope of the data fluctuations. This represents the corrected initial interpolation weights.

6. The HGIS equipment simulation method considering multi-physics coupling according to claim 1, characterized in that, The process of performing a second interpolation on the corrected weights and fusing the results until the field difference value meets the accuracy requirements, thereby achieving iterative optimization of the second interpolation, includes the following specific methods: The corrected weights are re-interpolated and the results are fused. If the relative errors of all physical fields are less than or equal to 0.1, the accuracy requirement is met and the iteration is stopped. If the accuracy requirement is not met, repeat the second interpolation and iteration until the accuracy requirement is met.

7. The HGIS equipment simulation method considering multi-physics coupling according to claim 1, characterized in that, The simulation results of the HGIS equipment include: There are four types of data files: electric field core data, thermal field core data, flow field core data, and interpolation accuracy report.

8. A simulation system for HGIS equipment considering multiphysics coupling, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as claimed in any one of claims 1-7.

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