Vacuum pressure impregnation process optimization method, device, equipment, medium and product

Abstract

The invention relates to the technical field of superconducting magnet manufacturing and insulation impregnation process optimization, and discloses a vacuum pressure impregnation process optimization method, device, equipment, medium and product, and the method comprises the steps: inputting a target porosity and a target permeability into an insulation layer homogenization fluid domain model to obtain a macroscopic homogeneous model; according to the macroscopic homogeneous model, a first corresponding relation between multiple technological parameters and the impregnation performance parameters under the boundary condition is determined through simulation, and the multiple technological parameters comprise at least two of the temperature, the glue injection parameter, the vacuum pressure, the resin viscosity and the resin curing kinetic parameter; and according to the plurality of first corresponding relations, optimizing the plurality of process parameters by taking the shortest filling time, the highest uniformity and the lowest residual bubble rate as optimization objectives to obtain an optimal process parameter combination of the vacuum pressure impregnation process. According to the method, the multiple process parameters are optimized, the multi-parameter optimization database can be established, and the optimal process combination scheme is obtained.

Description

Technical Field

[0001] This invention relates to the field of superconducting magnet manufacturing and insulation impregnation process optimization technology, specifically to a vacuum pressure impregnation process optimization method, apparatus, equipment, medium, and product. Background Technology

[0002] As a core component of high-end equipment such as fusion devices, particle accelerators, and nuclear magnetic resonance imaging, superconducting magnets require extremely high insulation performance and structural reliability for long-term stable operation. To ensure the electrical insulation strength and overall mechanical properties of the magnet coils, vacuum pressure impregnation (VPI) is typically performed on the insulation layer after winding. This process involves removing the gas inside the insulation layer in a vacuum environment and then injecting epoxy resin under external pressure, allowing the resin to fully penetrate into the pores of the insulation layer, thereby forming a dense and complete insulation system.

[0003] Current VPI (Vacuum Injection Process) designs for superconducting magnet insulation layers rely on simple simulation analysis. Researchers typically treat the insulation layer as a porous medium and, based on Darcy's law or resin curing kinetic models, use finite element analysis (FEM) or computational fluid dynamics (CFD) to simulate resin flow and curing processes, thereby obtaining the corresponding process parameters. Existing studies often only adjust single process parameters (such as vacuum level or injection pressure), lacking systematic optimization methods. This makes it difficult to comprehensively consider the coupled effects of multiple process parameters, such as injection port arrangement, vacuum pressure, resin viscosity, and temperature distribution, resulting in an overall process that is difficult to optimize. Summary of the Invention

[0004] This invention provides a method, apparatus, equipment, medium, and product for optimizing the vacuum pressure impregnation process, in order to solve the problem that multiple process factors are difficult to systematically optimize in the VPI process of superconducting magnet insulation layers.

[0005] In a first aspect, the present invention provides a method for optimizing a vacuum pressure impregnation process. The method includes: inputting a target porosity and a target permeability into a homogenized fluid domain model of an insulating layer to obtain a macroscopic homogenized model; based on the macroscopic homogenized model, simulating and determining a first correspondence between multiple process parameters and impregnation performance parameters under boundary conditions, wherein the impregnation performance parameters include at least one of impregnation time, impregnation uniformity, and residual bubble rate, and the multiple process parameters include at least two of temperature, injection parameters, vacuum pressure, resin viscosity, and resin curing kinetic parameters; based on the multiple first correspondences, optimizing the multiple process parameters with the shortest filling time, highest uniformity, and lowest residual bubble rate as optimization objectives to obtain the optimal combination of process parameters for the vacuum pressure impregnation process.

[0006] The vacuum pressure impregnation process optimization method provided in this embodiment integrates the influence of various process factors on resin wetting effect, impregnation time and insulation density, establishes a multi-parameter optimization database, and outputs the optimal VPI process parameter combination to ensure the uniformity and density of the superconducting magnet insulation layer during the impregnation process, thereby significantly improving the electrical insulation performance and operational reliability of the superconducting magnet.

[0007] In an optional implementation, before inputting the target porosity and target permeability into the homogenized fluid domain model of the insulating layer, the method further includes: obtaining a first porosity and a first permeability, wherein the first porosity is the porosity calculated by theoretical formula under known insulating layer material parameters, and the first permeability is the permeability obtained by unidirectional flow experimental testing; obtaining a second porosity and a second permeability, wherein the second porosity is the porosity determined by fluid dynamics model simulation, and the second permeability is the permeability determined by fluid dynamics model simulation; when the deviation between the first porosity and the second porosity is less than or equal to a preset deviation value, and the deviation between the first permeability and the second permeability is less than or equal to the preset deviation value, determining the second porosity as the target porosity and the second permeability as the target permeability.

[0008] In this embodiment, the first porosity and first permeability are obtained experimentally, and the second porosity and second permeability are obtained by combining them with a three-dimensional model. Cross-validation between experimental and simulation data ensures the reliability of the input parameters, achieving dual verification and parameter assimilation of experimental and simulation results. This method is more scientific and accurate than traditional methods that rely on experience or a single approach to estimate the porosity and permeability of the insulating layer.

[0009] In one alternative implementation, the theoretical formula is as follows:

[0010] in, Indicates the first porosity. This indicates the areal density of the glass fiber layer in the insulation layer. This indicates the density of the glass fiber material. Indicates the area to be calculated. This indicates the total thickness of the insulation layer.

[0011] In one alternative implementation, obtaining the second porosity and the second permeability includes: obtaining the second porosity by voxel statistics or volume fraction methods; and obtaining the second permeability using Darcy's law.

[0012] In one optional implementation, the target porosity and target permeability are input into the homogenization fluid domain model of the insulation layer to obtain a macroscopic homogenization model, including: inputting the target porosity, target permeability, and resin dynamic viscosity into the homogenization fluid domain model of the insulation layer to obtain a macroscopic homogenization model, wherein the resin dynamic viscosity varies with temperature.

[0013] In one alternative embodiment, the expression for the change of resin dynamic viscosity with temperature is as follows:

[0014] in, Indicates the resin at absolute temperature The dynamic viscosity below, Represents the reference viscosity constant. As the apparent activation energy, This is the universal gas constant.

[0015] In one alternative embodiment, the method further includes: determining the actual resin front position and the actual viscosity change value using a sensor during vacuum pressure impregnation with an optimal combination of process parameters; and adjusting multiple process parameters based on the actual resin front position and the actual viscosity change value.

[0016] Secondly, the present invention provides a vacuum pressure impregnation process optimization device, comprising: a model building module for inputting target porosity and target permeability into an insulating layer homogenization fluid domain model to obtain a macroscopic homogenization model; a simulation module for simulating and determining a first correspondence between multiple process parameters and impregnation performance parameters under boundary conditions based on the macroscopic homogenization model, wherein the impregnation performance parameters include at least one of impregnation time, impregnation uniformity, and residual bubble rate, and the multiple process parameters include at least two of temperature, injection parameters, vacuum pressure, resin viscosity, and resin curing kinetic parameters; and an optimization module for optimizing multiple process parameters based on the multiple first correspondences, with the shortest filling time, highest uniformity, and lowest residual bubble rate as optimization objectives, to obtain the optimal combination of process parameters for the vacuum pressure impregnation process.

[0017] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the vacuum pressure impregnation process optimization method of the first aspect or any corresponding embodiment described above.

[0018] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the vacuum pressure impregnation process optimization method of the first aspect or any corresponding embodiment described above.

[0019] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the vacuum pressure impregnation process optimization method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the specific embodiments or related technologies of the present invention, the accompanying drawings used in the description of the specific embodiments or related technologies will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is a schematic flowchart of a vacuum pressure impregnation process optimization method according to an embodiment of the present invention; Figure 2 This is a schematic flowchart of another vacuum pressure impregnation process optimization method according to an embodiment of the present invention; Figure 3 This is a schematic flowchart of a vacuum pressure impregnation process optimization method based on experimental and CFD coupling according to an embodiment of the present invention. Figure 4 This is a structural block diagram of the apparatus for optimizing the vacuum pressure impregnation process according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0023] Currently, the VPI process for superconducting magnet insulation layers still has significant shortcomings in practical applications. Due to the complex structure and uneven pore distribution of the insulation layer, dead zones often appear in the resin flow during impregnation, leading to insufficient local filling or residual air bubbles, thus affecting the density and electrical insulation performance of the insulation layer. At the same time, the setting of process parameters mainly relies on experience and lacks scientific quantitative basis, especially in the areas of injection port location, vacuum pressure, and temperature control, where systematic optimization is lacking.

[0024] The insulation layer is composed of glass fiber and resin composites, with a complex microstructure. Key physical properties such as porosity and permeability are difficult to measure accurately using traditional methods, which further restricts the precision of process design. In related studies, numerical simulations and experimental verification are often independent and lack organic integration, making it difficult to form a reliable process optimization method.

[0025] Some studies also employ the experiment-error trial method. This typically involves optimizing parameters through step-by-step testing from small samples and model coils to actual coils; a typical example is the development of the VPI process for the HT-7U magnet. While this method can accumulate experience and solve specific process problems, it is time-consuming and costly, lacks versatility, and small-sample or model tests cannot fully reproduce actual operating conditions, resulting in low efficiency and poor scalability.

[0026] In addition, there are prediction methods based on empirical formulas for heat transfer / flow. These methods utilize classical empirical relationships such as Darcy's law or the Arrhenius equation to quickly estimate resin percolation time and curing time windows, providing preliminary references for process design. Their advantages are simplicity and intuitiveness, offering qualitative guidance. However, because they ignore the complexity of the insulation layer structure and the coupling effects of multiple factors, the results often have significant deviations and can only serve as rough references, making it difficult to independently support process optimization.

[0027] In view of this, the present invention provides a method, apparatus, equipment, medium, and product for optimizing a vacuum pressure impregnation (VPI) process. By combining experimentally measured parameters with a CFD homogenization model, the percolation and curing behavior of resin in the insulation layer of a superconducting magnet during the VPI process can be accurately reflected. This avoids the limitations of simple simulations with too many assumptions or experiments that cannot fully cover all aspects, thus improving the accuracy of process simulation. Furthermore, by changing various process parameters such as temperature, vacuum pressure, number and location of injection ports, and resin viscosity, and using a CFD homogenization model system for simulation optimization analysis, a multi-parameter optimization database can be established to obtain the optimal process combination scheme. Compared with traditional empirical and trial-and-error methods, this significantly shortens the process development cycle and reduces cost investment.

[0028] The following is a detailed description of the vacuum pressure impregnation process optimization method provided by the present invention with reference to the accompanying drawings. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] The optimized vacuum pressure impregnation process method provided in this embodiment can be used in mobile terminals, such as electronic devices, servers, or tablet computers. Figure 1 This is a flowchart of an optimization method for a vacuum pressure impregnation process according to an embodiment of the present invention, such as... Figure 1As shown, the process includes the following steps: Step S101: Input the target porosity and target permeability into the homogenization fluid domain model of the insulation layer to obtain a macroscopic homogenization model.

[0030] Among them, the homogenized fluid domain model of the insulating layer refers to the geometric model of the superconducting magnet coil established in 3D modeling software (fluid dynamics software), and the insulating layer region of the superconducting magnet coil is abstracted as a homogenized porous medium; the macro-homogeneous model is the homogenized fluid domain model of the insulating layer with target porosity and target permeability set. The macro-homogeneous model is used to predict the flow behavior of the impregnating agent in the insulating layer.

[0031] Step S102: Based on the macroscopic homogeneous model, the simulation determines the first correspondence between multiple process parameters and impregnation performance parameters under boundary conditions.

[0032] The impregnation performance parameters include at least one of impregnation time, impregnation uniformity, and residual bubble rate. The multiple process parameters include at least two of temperature, dispensing parameters, vacuum pressure, resin viscosity, and resin curing kinetics parameters. The dispensing parameters include the dispensing port arrangement position, dispensing port arrangement method, and number of dispensing ports.

[0033] Impregnation time includes filling time, which refers to the total time required for the impregnating agent (such as resin) to completely fill the internal pores of the workpiece (such as an insulating layer) under set vacuum and pressure parameters. Impregnation uniformity describes the distribution of the impregnating agent in different areas within the workpiece, i.e., whether it penetrates evenly into all the micropores and gaps that need to be impregnated. Residual bubble rate refers to the percentage of unfilled bubbles remaining inside the workpiece after impregnation, relative to the total pore volume of the workpiece. Boundary conditions define the range of values ​​for process parameters; these boundary conditions can be determined by the designer.

[0034] Specifically, the relationship between temperature and resin front propagation speed can be simulated and determined based on the macroscopic homogeneous model. and total filling time The correspondences between the injection parameters and the filling uniformity and wetting time, the correspondence between the vacuum pressure and the residual bubble rate, and the correspondences between the resin viscosity, resin curing kinetic parameters, and impregnation performance parameters.

[0035] For example, temperature simulation includes: setting different temperature boundary conditions in a CFD model (macroscopic homogeneous model); running percolation simulation at each temperature and recording the resin front advance velocity. and total filling time ; Calculate the operable time window (Determined by the resin viscosity curve) This refers to the time interval from the initial injection of resin into the insulating layer until its viscosity rises to the point of loss of fluidity. Within this time range, the resin maintains a sufficiently low viscosity to smoothly penetrate and fill the pores of the insulating layer under applied vacuum pressure; comparisons are made under various temperature conditions. In this case, determine the optimal immersion temperature range.

[0036] Simulation optimization of dispensing parameters includes: setting different dispensing nozzle arrangements in the CFD model: single-point dispensing, symmetrical double-point dispensing, and ring-shaped dispensing; and applying the same pressure differential. The flow process of resin in the insulation layer is simulated; the flow field distribution is extracted and the existence of flow dead zones or stagnant areas is analyzed; the optimal number and location of injection ports are determined using filling uniformity and wetting time as evaluation indicators.

[0037] Vacuum pressure optimization includes: setting different initial vacuum pressures in the simulation and applying a constant dispensing pressure; simulating the residual bubble volume fraction under different vacuum levels. With resin flow rate The changes were evaluated; the degassing efficiency and wetting time under different vacuum levels were assessed, and a vacuum pressure range that could both fully degas and ensure rapid permeation was selected.

[0038] The coupling optimization of resin viscosity and curing kinetics includes: inputting resin curing kinetic parameters into the CFD model; introducing a coupling function of viscosity with temperature and time in the simulation to simulate the evolution of resin viscosity under different process curves; and comparing the time when the resin viscosity reaches the critical value under different process parameters to determine whether the process can complete impregnation before the resin loses its fluidity.

[0039] Step S103: Based on multiple first correspondences, with the shortest filling time, highest uniformity and lowest residual bubble rate as optimization objectives, multiple process parameters are optimized to obtain the optimal combination of process parameters for the vacuum pressure impregnation process.

[0040] Specifically, multiple first correspondences are used as objective functions. Multi-objective optimization algorithms, including non-dominated sorting genetic algorithm, multi-objective particle swarm optimization algorithm, and multi-objective ant colony optimization algorithm, are employed to optimize multiple process parameters with the shortest filling time, highest uniformity, and lowest residual bubble rate as optimization objectives, thereby obtaining the optimal combination of process parameters for the vacuum pressure impregnation process. Simultaneously, a process database is established to guide the VPI impregnation process of actual superconducting magnet coils.

[0041] The vacuum pressure impregnation process optimization method provided in this embodiment integrates the influence of various process factors on resin wetting effect, impregnation time and insulation density, establishes a multi-parameter optimization database, and outputs the optimal VPI process parameter combination to ensure the uniformity and density of the superconducting magnet insulation layer during the impregnation process, thereby significantly improving the electrical insulation performance and operational reliability of the superconducting magnet.

[0042] In one implementation, such as Figure 2 As shown, prior to step S101 above, the vacuum pressure impregnation process optimization method further includes the following steps: Step S201: Obtain the first porosity and the first permeability.

[0043] Among them, the first porosity is the porosity calculated by theoretical formula under the condition of known insulating layer material parameters, and the first permeability is the permeability obtained by unidirectional flow experiment.

[0044] Specifically, given the known parameters of the insulating layer material, the porosity can be calculated using the theoretical formula (1):

[0045] in, 1 represents the first porosity (dimensionless, ranging from 0 to 1); The areal density of the insulating glass fiber layer (unit: kg / m^2). Density of glass fiber material (unit: kg / m^3); To calculate the area (unit: m^2); Total thickness of the insulation layer (unit: m).

[0046] This formula obtains the porosity within the fiber layer by comparing the difference between the total volume of the insulation layer and the solid volume of the glass fiber, which is then used for subsequent calculations of permeation characteristics.

[0047] The experimental steps for determining permeability using the unidirectional flow method are as follows: (1) Lay the glass fiber flat on the epoxy board, cover it with a transparent sealing bag and seal it with sealant to form a vacuum cavity.

[0048] (2) A resin injection port is provided at one end and a vacuum extraction port is provided at the other end to form a unidirectional flow environment.

[0049] (3) Record the position of the resin at the flow front. ,time and the corresponding pressure difference The permeability is calculated using Darcy's law as shown in formula (2):

[0050] in, Volumetric flow rate (unit: m^3 / s); 1 represents the first permeability (unit: m^2); The cross-sectional area of ​​the flow (unit: m^2); Resin dynamic viscosity (unit: ); The pressure difference across the resin (unit: Pa); This represents the seepage path length (in meters).

[0051] Based on the relationship between the position of the resin front and the progression over time, it can be obtained through integral calculation. 1. Used to characterize the permeability of insulating layers.

[0052] Step S202: Obtain the second porosity and the second permeability.

[0053] The second porosity is the porosity determined by simulation using a fluid dynamics model, and the second permeability is the permeability determined by simulation using a fluid dynamics model.

[0054] Specifically, the wound glass fiber insulation layer is first sampled and scanned using FIB-SEM or X-ray micro-CT to reconstruct its three-dimensional microporous structure. Then, the three-dimensional model is imported into CFD software, and different pressure differentials are applied... The process of resin seepage in pores was simulated, and the effective permeability (i.e., the second permeability) and porosity (i.e., the second porosity) were calculated using the same Darcy's Law formula. The results were then compared and verified with experimental data.

[0055] For example, the steps for determining the second porosity and second permeability through three-dimensional microstructure modeling and numerical extraction include: (1) Sample preparation: A representative sample was taken from the insulating layer of the superconducting magnet and necessary pretreatment was performed to ensure that the microstructure was completely preserved in subsequent scans.

[0056] (2) Microstructure scanning: The three-dimensional image data of the distribution of glass fibers and pores inside the insulating layer are obtained by using FIB-SEM layer-by-layer slicing imaging or micron-level X-ray CT non-destructive scanning.

[0057] (3) Image processing and reconstruction: The acquired image sequence is registered, denoised and segmented to distinguish the fiber phase and the pore phase, and a three-dimensional pore network model is reconstructed.

[0058] (4) Porosity calculation: The porosity (i.e., the second porosity) of the insulating layer is obtained by calculating the pore volume ratio in the fiber layer through voxel statistics or volume fraction method.

[0059] (5) Fluid domain extraction and mesh generation: The pore portion is extracted as the fluid computational domain, a numerical mesh suitable for CFD solution is generated, and corresponding boundary conditions are applied.

[0060] (6) Numerical simulation and permeability extraction: Apply pressure difference or flow conditions to the model to simulate the seepage process of resin in the pore network, and calculate the permeability tensor (i.e., the second permeability) based on Darcy's law or the volume average velocity method.

[0061] In step S203, when the deviation between the first porosity and the second porosity is less than or equal to a preset deviation value, and the deviation between the first permeability and the second permeability is less than or equal to a preset deviation value, the second porosity is determined as the target porosity, and the second permeability is determined as the target permeability.

[0062] The preset deviation value is defined by the designer; for example, the preset deviation value can be 5%.

[0063] Specifically, the porosity and permeability extracted from the microstructure are compared and verified with the experimentally measured data. When the porosity and permeability extracted from the microstructure are consistent with the experimentally measured data (deviation less than 5%), the parameters are mapped to the macroscopic homogeneous model to provide reliable input for VPI process simulation.

[0064] In this embodiment, the first porosity and first permeability are obtained experimentally, and the second porosity and second permeability are obtained by combining them with a three-dimensional model. Cross-validation between experimental and simulation data ensures the reliability of the input parameters, achieving dual verification and parameter assimilation of experimental and simulation results. This method is more scientific and accurate than traditional methods that rely on experience or a single approach to estimate the porosity and permeability of the insulating layer.

[0065] For example, the first porosity and the first permeability can be determined based on a unidirectional flow experiment. The determination of the first porosity and the first permeability using the unidirectional flow method includes: Step 1: Sample preparation, prepare the glass fiber insulation material with a total thickness of ,area areal density Prepared as a sample, with the material density set as follows: .

[0066] Step 2: Porosity calculation. The porosity of the insulating layer is calculated by the relationship between mass and volume (i.e., the above formula (1)).

[0067] Step 3: Set up the experimental setup. Fix the insulation sample in a sealed container. Set one end as the resin injection port and the other end as the vacuum extraction port to form a unidirectional flow experimental environment.

[0068] Step 4: Experimental procedure, apply a pressure difference across the two ends of the sample. Record time With the position of the resin flow front .

[0069] Step 5: Permeability calculation. According to Darcy's law, the volumetric flow rate of the resin in the insulation layer is calculated. With pressure difference The relationship can be shown in the above formula (2).

[0070] Step 6: The leading edge propulsion model, by examining the change of the leading edge position over time and combining Darcy's law and the law of conservation of mass, yields the following formula (3):

[0071] Where x represents the leading edge position, the first permeability of the insulating layer can be deduced from the curve fitted based on experimental data. 1.

[0072] Step 7: Output the experimental results and record the obtained first porosity and first permeability for subsequent comparison and verification.

[0073] In this embodiment, determining the second porosity and second permeability based on three-dimensional microstructure modeling includes: (1) Sample preparation: Representative samples were taken from the superconducting magnet insulating layer and subjected to necessary pretreatment to ensure that the microstructure was completely preserved in subsequent scanning. Representative samples were cut from the wound superconducting magnet insulating layer (dry fiber preform), and the sample size was set as ( , , Pretreatment smooths the sample surface, preserving the fiber and pore structure.

[0074] (2) Microstructure scanning: The three-dimensional image sequence of the sample interior was obtained by FIB-SEM layer-by-layer slicing imaging or micron-level X-ray CT non-destructive scanning. The obtained three-dimensional images were reconstructed in a three-dimensional coordinate system to obtain the spatial distribution of the fibrous phase and the porous phase of the insulating layer.

[0075] (3) Image processing and reconstruction: The three-dimensional image is registered, denoised and thresholded to obtain binary voxel data, in which the fiber phase is recorded as "0" and the pore phase is recorded as "1". The three-dimensional pore network structure of the insulating layer is reconstructed by voxel stacking.

[0076] (4) Porosity calculation: Statistical analysis of the number of porous phase voxels With the total number of voxels The second porosity is calculated using the following formula (4). :

[0077] (5) Fluid domain extraction and mesh generation: The pore portion is extracted as a fluid domain, a numerical simulation model is established, and inlet and outlet planes are set at the boundary. The fluid domain is divided into a finite volume mesh to ensure the stability and accuracy of the seepage calculation.

[0078] (6) Numerical simulation and permeability extraction: Apply pressure differential or flow rate conditions to the model to simulate the resin seepage process in the pore network, and calculate the insulation layer permeability (second permeability) based on Darcy's law or the volume average velocity method. Apply pressure differential between the inlet and outlet. Based on the dynamic viscosity of the resin (A fixed value) is used as a fluid property parameter to simulate the steady-state seepage process of resin in a porous network. The fluid volumetric flow rate is calculated. Or take the volume average velocity in the flow field .

[0079] According to Darcy's law, the second permeability The calculation formula is shown in formula (5), or the volume average velocity method can be used to calculate the second permeability using formula (6). .

[0080]

[0081] If the consistency between the first porosity and the second porosity, and the consistency between the first permeability and the second permeability are good, they are used as reliable parameters to be input into the subsequent CFD macroscopic homogenization model for VPI process simulation and optimization.

[0082] When the permeability results obtained from microscopic modeling are inconsistent with the results of unidirectional flow experiments, first check whether the sealing performance, pressure difference stability, and resin viscosity values ​​at the experimental end are reasonable. At the same time, check whether the image segmentation, mesh generation, and representative volume element (REV) at the numerical modeling end meet the requirements, and ensure that the experimental direction corresponds to the simulation direction. If the deviation still exists, it can be corrected by multi-zone sampling statistics or empirical formula verification. Finally, the permeability is calibrated by parameter inversion or assimilation methods to obtain equivalent and reliable porosity and permeability inputs into the macroscopic CFD model, thereby ensuring the accuracy of subsequent VPI process simulation.

[0083] In one embodiment, step S101 specifically involves inputting the target porosity, target permeability, and resin dynamic viscosity into the homogenization fluid domain model of the insulating layer to obtain a macroscopic homogenization model.

[0084] In the model, the resin is set as a non-Newtonian fluid, and the dynamic viscosity of the resin changes with temperature. The relationship between the dynamic viscosity of the resin and temperature can be expressed by the Arrhenius formula, as shown in formula (7):

[0085] in, For the resin at temperature Dynamic viscosity at (unit: ); Reference viscosity constant (unit: ); Apparent activation energy (unit: J / mol); The universal gas constant ( ); Absolute temperature (unit: K).

[0086] In this embodiment, the key parameters in the homogenized insulating layer fluid domain model are obtained through cross-comparison of experimental data and simulation data, ensuring the reliability and universality of the parameter input.

[0087] In some embodiments, the vacuum pressure impregnation process optimization method further includes: determining the actual resin front position and the actual viscosity change value by a sensor when performing the vacuum pressure impregnation process with the optimal combination of process parameters; and adjusting multiple process parameters according to the actual resin front position and the actual viscosity change value.

[0088] Specifically, this invention can combine online monitoring methods (such as optical sensing, acoustic emission detection, and real-time pressure / flow monitoring) to track the resin front position and viscosity changes in real time during the VPI process, and compare and correct with the simulation model to achieve dynamic control of the process.

[0089] Optionally, the present invention can also introduce machine learning or intelligent algorithms to train experimental data and simulation results on the basis of a multi-parameter optimization database, thereby realizing automated prediction and optimization and improving the search efficiency and accuracy of process parameter combinations.

[0090] Optionally, the present invention can also combine the results of macroscopic process optimization with the microstructure design of the insulation layer, and improve the anisotropic uniformity and permeability of the insulation layer by optimizing the arrangement of glass fibers or the pore structure of the preform, thereby further improving the process stability.

[0091] Optionally, the present invention can also consider the multi-physics field coupling effect of resin curing exothermics and temperature distribution while optimizing process parameters, establish a heat-fluid-solid integrated model, and optimize the resin impregnation and curing process as a whole to avoid local overheating or uneven curing.

[0092] To achieve systematic optimization of the VPI process for superconducting magnets, this invention, based on the establishment of a homogenization model for the insulating layer, employs numerical simulation platforms (such as Fluent, STAR-CCM+, etc.) for multi-factor process optimization.

[0093] The process of multi-factor optimization of VPI process based on homogenization model may include the following steps: Step 1: Model Establishment. A geometric model of the superconducting magnet coil is created in 3D modeling software, abstracting the insulating layer region as a homogeneous porous medium. The main parameter of this porous medium is porosity. With penetration rate .

[0094] Step 2: Input physical property parameters, porosity With penetration rate The dynamic viscosity of the resin was obtained through experimental measurement and numerical extraction using microscopic modeling. It changes with temperature.

[0095] Step 3: Set boundary conditions. Set a glue injection port at the model inlet and a vacuum port at the outlet, and apply pressure to each. and The vacuum degree is Pressure difference Thermal boundary conditions are applied to the outer surface of the insulation layer to define the temperature distribution. The resin is initially set to a cavity and is gradually filled during injection.

[0096] Step 4: Temperature control process simulation at different temperatures ( , , Under boundary conditions, the resin permeation process in the insulation layer is simulated.

[0097] Run seepage simulations at each temperature and record the resin front advance velocity. and total filling time And calculate the operable time window. (Determined by resin viscosity curve); Compare under different temperature conditions In this case, determine the optimal immersion temperature range.

[0098] Step 5: Optimize the layout of the injection ports by changing the number, diameter, and location distribution of the injection ports while applying the same pressure differential. The flow process of resin in the insulation layer is simulated; the flow field distribution is extracted and the existence of flow dead zones or stagnant areas is analyzed; the optimal number and location of injection ports are determined using filling uniformity and wetting time as evaluation indicators.

[0099] Step 6: Vacuum pressure optimization at different vacuum levels ( , , Simulations were performed to calculate the residual bubble volume fraction. Simultaneously record the immersion time. The changes in [the parameters] were used to evaluate the balance between degassing efficiency and filling speed. [A selection was made]. Lowest and Vacuum pressure parameters within an acceptable range.

[0100] Step 7: Resin curing kinetics coupling optimization. In the simulation, the resin curing kinetics model shown in formula (8) is introduced:

[0101] in, This refers to the degree of curing. The reaction order is [number]. Let be the rate constant, satisfying the Arrhenius relation as shown in equation (9):

[0102] pass With curing degree The relationship between viscosity and curing process was obtained, and the dynamic evolution of viscosity was determined. The critical viscosity at curing was confirmed. Before reaching the target area, the resin can complete the impregnation.

[0103] Step 8: Comprehensive optimization of process parameters. The simulation results of temperature control, nozzle arrangement, vacuum pressure, and resin curing kinetics are input into the multi-objective optimization module. The optimization objectives are: shortest impregnation time, highest wetting uniformity, and lowest residual bubble rate. Through comprehensive analysis of multiple factors, the optimal combination of process parameters is output.

[0104] In yet another embodiment, such as Figure 3 As shown, the VPI process optimization method based on the coupling of experimentation and CFD includes: Step 1: Obtaining Experimental Parameters The permeability of the insulating layer was measured using a unidirectional flow experiment. With porosity And record the resin front advancement curve. Rheological experiments were conducted on the resin to obtain the variation of dynamic viscosity with temperature and time. Furthermore, curing kinetic parameters were obtained using curing experiments.

[0105] Step 2: Numerical Modeling and Prediction A homogenized CFD model of the insulating layer was established, and the porosity extracted from the microscopic modeling was input as input. With penetration rate Set process boundary conditions (injection port arrangement, vacuum pressure, heating temperature curve) to simulate the resin permeation and curing process; calculate the resin filling time and the critical time for residual bubble volume viscosity.

[0106] Step 3: Experiment-Simulation Consistency Verification Comparison of frontier curves measured in experiments curves compared to simulation predictions If the deviation is within the allowable range, the simulation model is confirmed to be reliable; if the deviation exceeds the limit, adjust the permeability and porosity or introduce a Forchheimer correction factor. Parameter assimilation is performed to obtain equivalent calibration parameters ( , , ).

[0107] Step 4: Closed-loop correction and re-simulation Input the calibrated equivalent parameters into the simulation model and re-simulate the seepage process; check the consistency between the front curve, seepage velocity and experimental results to ensure that the differences meet the design requirements; if there are still deviations, further optimize the mesh generation and boundary condition settings until the model converges and stabilizes.

[0108] Step 5: Multi-factor optimization of the process In the calibrated model, the process parameters were changed respectively: temperature distribution, injection port layout, vacuum degree, and injection pressure difference; multiple simulations were run, and the filling time, wetting uniformity, and residual bubble rate under each working condition were output; the optimal parameter combination was selected using a multi-objective optimization method.

[0109] Step 6: Outputting the optimal solution and establishing the database The final optimized combination of process parameters is taken as the optimal process scheme for VPI (Variable Induction Phase) superconducting magnet. At the same time, experimental data, simulation results and the optimal parameter combination are entered into a multi-parameter process database to guide the process design of different types of superconducting magnet coils. In subsequent new designs, suitable VPI process parameters can be quickly obtained through database matching and simulation correction, so as to achieve the standardization and scalability of process optimization.

[0110] The process optimization method provided in this embodiment has the following advantages: (1) Accurately obtain key physical property parameters This invention obtains the porosity and permeability of the insulating layer through two methods: unidirectional flow experiments and three-dimensional modeling of the microstructure. The reliability of the input parameters is ensured through cross-validation of experimental and numerical results. This is more scientific and accurate than traditional methods that rely on experience or a single approach for estimation.

[0111] (2) Coupling of experiment and CFD simulation By combining the experimentally measured parameters with the CFD homogenization model, the percolation and curing behavior of the resin in the superconducting magnet insulation layer during the VPI process can be accurately reflected. This avoids the limitations of simple simulation with too many assumptions or experiments that cannot fully cover all aspects, thus improving the accuracy of process simulation.

[0112] (3) Multi-factor process optimization This invention utilizes a systematic simulation optimization analysis to modify various process factors such as temperature, vacuum pressure, number and location of injection nozzles, and resin viscosity. This allows for the establishment of a multi-parameter optimization database and the acquisition of the optimal process combination. Compared to traditional empirical and trial-and-error methods, this significantly shortens the process development cycle and reduces costs.

[0113] (4) Ensure the uniformity and density of the insulation layer impregnation. By comprehensively analyzing the wetting effect, penetration time, and resin distribution uniformity, this invention can ensure that the superconducting magnet insulation layer is free of bubbles and dry spots during the impregnation process, thereby significantly improving the electrical insulation performance and operational reliability of the coil.

[0114] (5) Strong scalability and scalability This method is not only applicable to the VPI process of current specific superconducting magnet coils, but can also be extended to other magnet impregnation processes with complex structures or different material systems, and has broad adaptability and practical value.

[0115] This embodiment also provides a vacuum pressure impregnation process optimization apparatus, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0116] This embodiment provides a vacuum pressure impregnation process optimization device, such as... Figure 4 As shown, it includes: The model building module 401 is used to input the target porosity and target permeability into the homogenization fluid domain model of the insulation layer to obtain a macroscopic homogenization model; The simulation module 402 is used to simulate and determine the first correspondence between multiple process parameters and impregnation performance parameters under boundary conditions based on the macroscopic homogeneous model. The impregnation performance parameters include at least one of impregnation time, impregnation uniformity and residual bubble rate. The multiple process parameters include at least two of temperature, dispensing parameters, vacuum pressure, resin viscosity and resin curing kinetic parameters. The optimization module 403 is used to optimize multiple process parameters based on multiple first correspondences, with the shortest filling time, the highest uniformity and the lowest residual bubble rate as optimization objectives, to obtain the best combination of process parameters for the vacuum pressure impregnation process.

[0117] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0118] In this embodiment, the vacuum pressure impregnation process optimization device is presented in the form of a functional unit. Here, a unit refers to an application-specific integrated circuit (ASIC), a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0119] This invention also provides an electronic device, such as... Figure 5 As shown, the electronic device includes one or more processors 510, a memory 520, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 5 Take the 510 processor as an example.

[0120] Processor 510 may be a central processing unit, a network processor, or a combination thereof. Processor 510 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0121] The memory 520 stores instructions executable by at least one processor 510 to cause the at least one processor 510 to perform the method shown in the above embodiments.

[0122] The memory 520 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device. Furthermore, the memory 520 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 520 may optionally include memory remotely located relative to the processor 510, and these remote memories may be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0123] The memory 520 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 520 may also include a combination of the above types of memory.

[0124] The electronic device also includes a communication interface 530 for communicating with other devices or communication networks.

[0125] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0126] A portion of this invention can be applied to computer program products, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installation program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0127] In the description of this specification, the references to terms such as "this embodiment," "an embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0128] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0129] Although embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the present invention.

Claims

1. An optimized method for vacuum pressure impregnation process, characterized in that, The method includes: Input the target porosity and target permeability into the homogenization fluid domain model of the insulation layer to obtain a macroscopic homogenization model; Based on the macroscopic homogeneous model, simulations determine the first correspondence between multiple process parameters and impregnation performance parameters under boundary conditions. The impregnation performance parameters include at least one of impregnation time, impregnation uniformity, and residual bubble rate. The multiple process parameters include at least two of temperature, dispensing parameters, vacuum pressure, resin viscosity, and resin curing kinetics parameters. Based on multiple first correspondences, with the shortest filling time, highest uniformity, and lowest residual bubble rate as optimization objectives, the multiple process parameters are optimized to obtain the optimal combination of process parameters for the vacuum pressure impregnation process.

2. The method according to claim 1, characterized in that, Before inputting the target porosity and target permeability into the homogenized fluid domain model of the insulating layer, the method further includes: The first porosity and the first permeability are obtained, wherein the first porosity is the porosity calculated by theoretical formula under known insulating layer material parameters, and the first permeability is the permeability obtained by unidirectional flow experiment. Obtain a second porosity and a second permeability, wherein the second porosity is the porosity determined by simulation using a fluid dynamics model, and the second permeability is the permeability determined by simulation using the fluid dynamics model. When the deviation between the first porosity and the second porosity is less than or equal to a preset deviation value, and the deviation between the first permeability and the second permeability is less than or equal to the preset deviation value, the second porosity is determined as the target porosity, and the second permeability is determined as the target permeability.

3. The method according to claim 2, characterized in that, The theoretical formula is as follows: in, This represents the first porosity. This indicates the areal density of the glass fiber layer in the insulation layer. This indicates the density of the glass fiber material. Indicates the area to be calculated. This indicates the total thickness of the insulation layer.

4. The method according to claim 3, characterized in that, The process of obtaining the second porosity and the second permeability includes: The second porosity is obtained by voxel statistics or volume fraction methods; The second permeability is obtained using Darcy's law.

5. The method according to any one of claims 1 to 4, characterized in that, The step of inputting the target porosity and target permeability into the homogenization fluid domain model of the insulating layer to obtain a macroscopic homogenization model includes: The target porosity, the target permeability, and the resin dynamic viscosity are input into the homogenization fluid domain model of the insulating layer to obtain the macroscopic homogenization model, wherein the resin dynamic viscosity varies with temperature.

6. The method according to claim 5, characterized in that, The expression for the change of the dynamic viscosity of the resin with temperature is as follows: in, Indicates the resin at absolute temperature The dynamic viscosity below, Represents the reference viscosity constant. As the apparent activation energy, This is the universal gas constant.

7. The method according to any one of claims 1 to 4, characterized in that, The method further includes: When performing vacuum pressure impregnation with the optimal combination of process parameters, the actual resin front position and the actual viscosity change value are determined by sensors. The multiple process parameters are adjusted based on the actual resin front position and the actual viscosity change value.

8. A vacuum pressure impregnation process optimization device, characterized in that, The device includes: The model building module is used to input the target porosity and target permeability into the homogenization fluid domain model of the insulation layer to obtain a macroscopic homogenization model; The simulation module is used to simulate and determine the first correspondence between multiple process parameters and impregnation performance parameters under boundary conditions based on the macroscopic homogeneous model. The impregnation performance parameters include at least one of impregnation time, impregnation uniformity, and residual bubble rate. The multiple process parameters include at least two of temperature, dispensing parameters, vacuum pressure, resin viscosity, and resin curing kinetics parameters. The optimization module is used to optimize the multiple process parameters based on multiple first correspondences, with the optimization objectives of shortest filling time, highest uniformity and lowest residual bubble rate, to obtain the optimal combination of process parameters for vacuum pressure impregnation process.

9. An electronic device, characterized in that, include: A memory and a processor are interconnected, the memory storing computer instructions, and the processor executing the computer instructions to perform the vacuum pressure impregnation process optimization method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to execute the vacuum pressure impregnation process optimization method according to any one of claims 1 to 7.

11. A computer program product, characterized in that, Includes computer instructions for causing a computer to execute the vacuum pressure impregnation process optimization method according to any one of claims 1 to 7.

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