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

Abstract

The application 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.The method comprises the following steps: inputting target porosity and target permeability into an insulation layer homogenization fluid domain model to obtain a macroscopic homogenization model; according to the macroscopic homogenization model, a first corresponding relationship between a plurality of process parameters and impregnation performance parameters under boundary conditions is simulated and determined, the plurality of process parameters comprising at least two of temperature, glue injection parameters, vacuum pressure, resin viscosity and resin curing kinetics parameters; according to the plurality of first corresponding relationships, a plurality of process parameters are optimized with the shortest filling time, the highest uniformity and the lowest residual bubble rate as optimization targets, so that the best process parameter combination of the vacuum pressure impregnation process is obtained. Through optimization of the plurality of process parameters, a multi-parameter optimization database can be established, and the optimal process combination scheme is obtained.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of superconducting magnet manufacturing and insulation impregnation process optimization, and particularly relates to a vacuum pressure impregnation process optimization method, device, equipment, medium and product. BACKGROUND

[0002] As the core component of high-end equipment such as fusion devices, particle accelerators and nuclear magnetic resonance imaging, the long-term stable operation of superconducting magnets has very high requirements on insulation performance and structural reliability. In order to ensure the electrical insulation strength and overall mechanical performance of the magnet coil, the insulation layer usually needs to be treated by vacuum pressure impregnation (VPI) after winding. This process removes the gas inside the insulation layer under vacuum, and then injects epoxy resin under external pressure, so that the resin fully penetrates into the insulation layer pores, thereby forming a dense and complete insulation system.

[0003] The design scheme of the existing VPI process of the insulation layer of the superconducting magnet is a simple simulation analysis method. Researchers usually regard the insulation layer as a porous medium, based on Darcy's law or resin curing kinetics model, use finite element or computational fluid dynamics (CFD) means to simulate the resin flow and curing process, and thus obtain the corresponding process parameters. The existing researches often only adjust a single process parameter (such as vacuum degree or injection pressure), lack of systematic optimization means, and it is difficult to comprehensively consider the coupling effect of injection port arrangement, vacuum pressure, resin viscosity and temperature distribution and other multi-process parameters, so that the overall process is difficult to achieve optimization. SUMMARY

[0004] The present application provides a vacuum pressure impregnation process optimization method, device, equipment, medium and product to solve the problem that multiple process factors are difficult to be systematically optimized in the VPI process of the insulation layer of the superconducting magnet.

[0005] In the first aspect, the present application provides a vacuum pressure impregnation process optimization method, which comprises: inputting a target porosity and a target permeability into an insulation layer homogenization fluid domain model to obtain a macro-homogeneous model; simulating to determine a first correspondence relationship between a plurality of process parameters and impregnation performance parameters under boundary conditions according to the macro-homogeneous model, wherein the impregnation performance parameters include at least one of impregnation time, impregnation uniformity and residual bubble rate, and the plurality of process parameters include at least two of temperature, injection parameter, vacuum pressure, resin viscosity and resin curing kinetics parameter; according to a plurality of first correspondence relationships, taking the shortest filling time, the highest uniformity and the lowest residual bubble rate as optimization objectives, optimizing the plurality of process parameters to obtain the best process parameter combination of the vacuum pressure impregnation process.

[0006] The vacuum pressure impregnation process optimization method provided by the embodiment comprehensively considers the influence of various process factors on the resin infiltration effect, impregnation time and insulation density, establishes a multi-parameter optimization database, outputs the optimal VPI process parameter combination, ensures the uniformity and density of the insulation layer of the superconducting magnet during the impregnation process, and thus significantly improves the electrical insulation performance and operation reliability of the superconducting magnet.

[0007] In an optional embodiment, before the target porosity and the target permeability are input into the insulation layer homogenization fluid domain model, the method further comprises: obtaining a first porosity and a first permeability, wherein the first porosity is a porosity calculated by a theoretical formula under the condition of known insulation layer material parameters, and the first permeability is a permeability obtained by a one-way flow experiment; obtaining a second porosity and a second permeability, wherein the second porosity is a porosity determined by a fluid mechanics model simulation, and the second permeability is a permeability determined by the fluid mechanics model simulation; when a deviation between the first porosity and the second porosity is less than or equal to a preset deviation value, and a deviation between the first permeability and the second permeability is less than or equal to a preset deviation value, determining the second porosity as the target porosity, and determining the second permeability as the target permeability.

[0008] In the embodiment, the first porosity and the first permeability are obtained by experiments, the second porosity and the second permeability are obtained in combination with a three-dimensional model, and the reliability of the input parameters is ensured through cross verification of experimental data and simulation data, so as to realize double checking of experimental results and simulation results and parameter assimilation. Compared with a traditional method of estimating the porosity and the permeability of the insulation layer depending on experience or a single means, the method is more scientific and accurate.

[0009] In an optional embodiment, the theoretical formula is as follows:

[0010]

[0011] wherein, the first porosity is represented by p 1, the surface density of the glass fiber layer in the insulation layer is represented by s, the density of the glass fiber material is represented by r f, the calculation area is represented by A, the total thickness of the insulation layer is represented by h.

[0012] In an optional embodiment, obtaining the second porosity and the second permeability comprises: obtaining the second porosity by a voxel statistics or volume fraction method; and obtaining the second permeability by Darcy's law.

[0013] In an alternative embodiment, the target porosity and the target permeability are input into the insulation layer homogenization fluid domain model to obtain the macro-homogenization model, comprising: inputting the target porosity, the target permeability and the resin dynamic viscosity into the insulation layer homogenization fluid domain model to obtain the macro-homogenization model, wherein the resin dynamic viscosity varies with temperature.

[0014] In an alternative embodiment, the expression of the resin dynamic viscosity varying with temperature is as follows:

[0015]

[0016] wherein, represents the dynamic viscosity of the resin at an absolute temperature represents a reference viscosity constant, is an apparent activation energy, is a universal gas constant.

[0017] In an alternative embodiment, the method further comprises: determining the actual resin front position and the actual viscosity change value by the sensor when the vacuum pressure impregnation process is processed with the optimal process parameter combination; and adjusting the plurality of process parameters according to the actual resin front position and the actual viscosity change value.

[0018] In a second aspect, the present application provides a vacuum pressure impregnation process optimization device, the device comprising: a model construction module for inputting the target porosity and the target permeability into the insulation layer homogenization fluid domain model to obtain the macro-homogenization model; a simulation module for simulating and determining a first correspondence relationship between the plurality of process parameters and the impregnation performance parameters under the boundary condition according to the macro-homogenization model, wherein the impregnation performance parameters include at least one of the impregnation time, the impregnation uniformity and the residual bubble rate, and the plurality of process parameters include at least two of the temperature, the resin injection parameter, the vacuum pressure, the resin viscosity and the resin curing dynamics parameter; and an optimization module for optimizing the plurality of process parameters according to the plurality of first correspondence relationships, taking the shortest filling time, the highest uniformity and the lowest residual bubble rate as the optimization target, to obtain the optimal process parameter combination of the vacuum pressure impregnation process.

[0019] In a third aspect, the present application provides an electronic device, comprising: a memory and a processor, which are in communication connection with each other, and 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 of the corresponding embodiments thereof.

[0020] ​In a fourth aspect, the present application provides a computer readable storage medium having stored thereon computer instructions for causing a computer to execute the vacuum pressure impregnation process optimization method of the first aspect or any of the corresponding embodiments thereof.

[0021] In a fifth aspect, the present application provides a computer program product comprising computer instructions for causing a computer to execute the vacuum pressure impregnation process optimization method of the first aspect or any of the corresponding embodiments thereof. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the specific embodiments or the related art, the drawings needed to be used in the specific embodiments or the related art description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0023] Figure 1 is a flowchart of a vacuum pressure impregnation process optimization method according to an embodiment of the present application;

[0024] Figure 2 is a flowchart of another vacuum pressure impregnation process optimization method according to an embodiment of the present application;

[0025] Figure 3 is a flowchart of a vacuum pressure impregnation process optimization method based on coupling of experiments and CFD according to an embodiment of the present application;

[0026] Figure 4 is a structural block diagram of a vacuum pressure impregnation process optimization method device according to an embodiment of the present application;

[0027] Figure 5 is a hardware structure schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, but not all the embodiments. According to the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0029] At present, the VPI process of the insulation layer of the superconducting magnet still has obvious deficiencies in practical application. Due to the complex structure of the insulation layer and uneven distribution of pores, resin often appears flow dead zone in the impregnation process, resulting in local insufficient filling or residual bubbles, thereby affecting the density and electrical insulation performance of the insulation layer. At the same time, the setting of process parameters mainly depends on experience, and lacks scientific quantitative basis, especially in the aspects of glue injection port position, vacuum pressure and temperature control, there is a lack of systematic optimization.

[0030] The insulation layer is composed of glass fibers and resin, and its microstructure is complex, and key physical parameters such as porosity and permeability are difficult to accurately measure by traditional methods, which further restricts the accuracy of process design. Numerical simulation and experimental verification in related researches are often independent of each other, lack of organic combination, and it is difficult to form a reliable process optimization method.

[0031] Some studies use trial-and-error methods. Usually, through small samples, model coils to actual coils, step-by-step experiments are used to optimize parameters, and typical cases are the development of the VPI process of the HT-7U magnet. This method can accumulate experience and solve specific process problems, but it is time-consuming and costly, and the generalization is poor, and small samples or model tests are difficult to completely reproduce the actual working conditions, which has the problems of low efficiency and poor generalization.

[0032] In addition, there are prediction methods based on heat transfer / flow empirical formula. This method uses Darcy's law or Arrhenius formula and other classical empirical relationships to quickly estimate the resin seepage time, curing time window and other parameters, providing preliminary reference for process design. Its advantage is simple and intuitive, and can give qualitative guidance, but due to the neglect of the complexity of the insulation layer structure and the multi-factor coupling effect, the result often has a large deviation, and can only be used as a rough reference, and it is difficult to independently support process optimization.

[0033] Therefore, the present application provides a vacuum pressure impregnation process optimization method, device, equipment, medium and product, which combines the experimentally measured parameters with the CFD homogenization model, can truly reflect the seepage and curing behavior of the resin in the VPI process of the insulation layer of the superconducting magnet, thereby avoiding the limitations of too many simulation assumptions or experimental difficulties in comprehensive coverage, and improving the accuracy of process simulation. At the same time, by changing the temperature, vacuum pressure, number and position of glue injection ports, resin viscosity and other process parameters, the CFD homogenization model is used to carry out simulation optimization analysis, a multi-parameter optimization database can be established, and the optimal process combination scheme can be obtained, compared with the traditional experience method and trial-and-error method, the process development cycle is significantly shortened, and the cost investment is reduced.

[0034] The vacuum pressure impregnation process optimization method provided by the embodiment of the present application can be used for mobile terminals such as electronic devices, servers or tablet computers, and the like.

[0035] The vacuum pressure impregnation process optimization method provided by the embodiment of the present application can be used for mobile terminals such as electronic devices, servers or tablet computers, and the like. Figure 1 The vacuum pressure impregnation process optimization method provided by the embodiment of the present application can be used for mobile terminals such as electronic devices, servers or tablet computers, and the like. Figure 1 The vacuum pressure impregnation process optimization method provided by the embodiment of the present application can be used for mobile terminals such as electronic devices, servers or tablet computers, and the like.

[0036] In step S101, the target porosity and the target permeability are input into the insulation layer homogenization fluid domain model to obtain a macro-homogeneous model.

[0037] The insulation layer homogenization fluid domain model refers to a geometric model of the superconducting magnet coil established in a three-dimensional modeling software (fluid mechanics software), and the insulation layer region of the superconducting magnet coil is abstracted as a homogenized porous medium; the macro-homogeneous model is the insulation layer homogenization fluid domain model with the target porosity and the target permeability set, and the macro-homogeneous model is used to predict the flow behavior of the impregnating agent in the insulation layer.

[0038] In step S102, a first correspondence between the plurality of process parameters and the impregnation performance parameters under the boundary condition is simulated and determined according to the macro-homogeneous model.

[0039] The impregnation performance parameters include at least one of the impregnation time, the impregnation uniformity and the residual bubble rate, and the plurality of process parameters include at least two of the temperature, the glue injection parameter, the vacuum pressure, the resin viscosity and the resin curing kinetics parameter, and the glue injection parameter includes the glue injection port arrangement position, the glue injection port arrangement mode and the glue injection port number.

[0040] The impregnation time includes the 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 the insulation layer) under the set vacuum and pressure parameters. The impregnation uniformity describes the distribution state of the impregnating agent in different regions inside the workpiece, i.e., whether it uniformly penetrates into all the small pores and gaps that need to be impregnated. The residual bubble rate refers to the percentage of the volume of the bubbles remaining in the workpiece after impregnation, which is not filled by the impregnating agent, to the total pore volume of the workpiece. The boundary condition defines the value range of the process parameters, which can be determined by the designer.

[0041] Specifically, according to the macro-homogeneous model, the temperature and the resin front advancing speed and the total filling time corresponding relationship between the injection parameter and the filling uniformity, the corresponding relationship between the injection parameter and the infiltration time, the corresponding relationship between the vacuum pressure and the residual bubble rate, and the corresponding relationship among the resin viscosity, the resin curing kinetics parameter, and the impregnation performance parameter.

[0042] For example, the temperature simulation includes: setting different temperature boundary conditions in the CFD model (macro-homogeneous model); running the percolation simulation at each temperature, recording the resin front advancing speed and the total filling time ; calculating the operable time window (determined by the resin viscosity curve), which refers to the time interval from the start of resin injection into the insulation layer to the time when the viscosity of the resin rises to lose fluidity. Within this time range, the resin maintains a sufficiently low viscosity and can smoothly penetrate and fill the pores of the insulation layer under the driving of the applied vacuum pressure. By comparing the cases under different temperature conditions , the optimal impregnation temperature range is determined.

[0043] The injection parameter simulation optimization includes: setting different injection port arrangements in the CFD model: single-point injection, symmetric double-point injection, ring-distributed injection, etc.; applying the same pressure difference , simulating the resin flow process in the insulation layer; extracting the flow field distribution, analyzing whether there are flow dead zones or stagnant areas; taking the filling uniformity and infiltration time as evaluation indexes, determining the optimal number and position of injection ports.

[0044] The vacuum pressure optimization includes: setting different initial vacuum pressures in the simulation and applying a constant injection pressure; simulating the changes of the residual bubble volume fraction and the resin flow speed under different vacuum degrees; evaluating the degassing efficiency and infiltration time under different vacuum degrees, and selecting a vacuum pressure range that can both sufficiently degas and ensure rapid percolation.

[0045] The resin viscosity and curing kinetics coupling optimization includes: inputting the resin curing kinetics parameters in the CFD model; introducing the viscosity coupling function with temperature and time in the simulation to simulate the evolution of resin viscosity under different process curves; 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 fluidity.

[0046] In step S103, according to the plurality of first corresponding relationships, the plurality of process parameters are optimized with the shortest filling time, the highest uniformity, and the lowest residual bubble rate as the optimization targets, to obtain the optimal process parameter combination of the vacuum pressure impregnation process.

[0047] Specifically, a plurality of first corresponding relationships are taken as a target function, and a plurality of process parameters are optimized by a multi-objective optimization algorithm such as a non-dominated sorting genetic algorithm, a multi-objective particle swarm optimization algorithm, and a multi-objective ant colony optimization algorithm, so as to obtain a best process parameter combination of the vacuum pressure impregnation process, with the shortest filling time, the highest uniformity, and the lowest residual bubble rate as optimization targets. Meanwhile, a process database is established to guide the VPI impregnation process of an actual superconducting magnet coil.

[0048] The vacuum pressure impregnation process optimization method provided in the embodiment comprehensively considers the influence of various process factors on the resin impregnation effect, the impregnation time, and the insulation density, establishes a multi-parameter optimization database, outputs an optimal VPI process parameter combination, ensures the uniformity and density of the insulation layer of the superconducting magnet in the impregnation process, and thus significantly improves the electrical insulation performance and operation reliability of the superconducting magnet.

[0049] In one embodiment, as shown in Figure 2 Before the step S101, the vacuum pressure impregnation process optimization method further includes the following steps:

[0050] In step S201, a first porosity and a first permeability are obtained.

[0051] The first porosity is a porosity obtained by a theoretical formula under the condition of known insulation layer material parameters, and the first permeability is a permeability obtained by a one-way flow experiment.

[0052] Specifically, under the condition of known insulation layer material parameters, the porosity can be calculated by a theoretical formula (1):

[0053]

[0054] wherein, 1 is the first porosity (dimensionless, with a value range of 0-1); is the areal density of the glass fiber layer of the insulation layer (unit: kg / m^2); is the density of the glass fiber material (unit: kg / m^3); is the calculation area (unit: m^2); is the total thickness of the insulation layer (unit: m).

[0055] The formula obtains the porosity in the fiber layer by comparing the difference between the total volume of the insulation layer and the volume of the glass fiber entity, which is used for subsequent seepage characteristic calculation.

[0056] The experimental steps for measuring the permeability by the one-way flow method are as follows:

[0057] (1) The glass fiber is laid on the epoxy plate, and a transparent sealing bag is covered and sealed to form a vacuum cavity.

[0058] (2) One end of the resin injection port, the other end of the vacuum exhaust port, forming a one-way flow environment.

[0059] (3) Record the resin in the flow front position , time and corresponding pressure difference , using the Darcy law as shown in equation (2) to calculate the permeability:

[0060]

[0061] Where, is the volume flow rate (unit: m^3 / s); 1 is the first permeability (unit: m^2); is the flow cross-sectional area (unit: m^2); is the resin dynamic viscosity (unit: ); is the pressure difference between the two ends of the resin (unit: Pa); is the seepage path length (unit: m).

[0062] Combined with the relationship between the resin front position and time, the integral calculation can be obtained 1, used to represent the permeability of the insulation layer.

[0063] Step S202, obtaining a second porosity and a second permeability.

[0064] Wherein, the second porosity is the porosity determined by fluid mechanics model simulation, and the second permeability is the permeability determined by fluid mechanics model simulation.

[0065] Specifically, first, sample the wound glass fiber insulation layer, scan using FIB-SEM or X-ray micro-CT, and reconstruct the three-dimensional micro-pore structure of the insulation layer. Then import the three-dimensional model into CFD software, simulate the seepage process of the resin in the pore by applying different pressure differences , calculate the effective permeability (i.e. the second permeability) and the porosity (i.e. the second porosity) using the same Darcy law formula, and compare and check the results with experimental data.

[0066] For example, the steps of determining the second porosity and the second permeability by three-dimensional modeling of microstructure and numerical extraction include:

[0067] (1) Sample preparation: representative samples are taken from the superconducting magnet insulation layer, and necessary pretreatment is carried out to ensure that the microstructure is completely preserved in the subsequent scanning.

[0068] (2) Microstructure scanning: Obtain the 3D image data of the glass fiber and pore distribution inside the insulation layer by using FIB-SEM layer-by-layer section imaging or micron-level X-ray CT non-destructive scanning method.

[0069] (3) Image processing and reconstruction: Register, denoise and segment the obtained image sequence, distinguish the fiber phase and pore phase, and reconstruct the 3D pore network model.

[0070] (4) Porosity calculation: Calculate the pore volume ratio in the fiber layer by voxel statistics or volume fraction method, and obtain the porosity of the insulation layer (i.e. the second porosity).

[0071] (5) Fluid domain extraction and meshing: Extract the pore part as the fluid calculation domain, generate numerical mesh suitable for CFD solution, and apply the corresponding boundary conditions.

[0072] (6) Numerical simulation and permeability extraction: Apply pressure difference or flow rate conditions in the model to simulate the seepage process of resin in the pore network, and calculate the permeability tensor based on Darcy's law or volume average velocity method (i.e. the second permeability).

[0073] Step S203, when the deviation of the first porosity and the second porosity is less than or equal to a preset deviation value, and the deviation of 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.

[0074] The preset deviation value is defined by the designer, for example, the preset deviation value can be 5% or the like.

[0075] Specifically, the porosity and permeability extracted by the microstructure are compared and checked with the experimental data, and when the porosity and permeability extracted by the microstructure are consistent with the experimental data (deviation less than 5%), the parameters are mapped into the macro-homogeneous model to provide reliable input for VPI process simulation.

[0076] In this embodiment, the first porosity and the first permeability are obtained by experiment, the second porosity and the second permeability are obtained by combining the three-dimensional model, the reliability of the input parameters is ensured through cross-validation of experimental data and simulation data, and double checking and parameter assimilation of experimental results and simulation results are realized. Compared with the traditional way of estimating the porosity and permeability of the insulation layer depending on experience or single means, the present method is more scientific and accurate.

[0077] For example, the first porosity and the first permeability can be determined based on a one-way flow experiment. The one-way flow method for determining the first porosity and the first permeability includes:

[0078] Step 1: Sample preparation, cut the glass fiber insulation layer material into a total thickness of 2 mm , area , areal density The material density is set as .

[0079] Step 2: Calculate the porosity of the insulation layer by the relationship between mass and volume (i.e., formula (1) above).

[0080] Step 3: Build the experimental device. Fix the insulation layer sample in a sealed container. One end is set as the resin injection port, and the other end is set as the vacuum exhaust port, forming a one-way flow experimental environment.

[0081] Step 4: Experimental operation. Apply pressure difference to both ends of the sample Record the time and the position of the resin flow front .

[0082] Step 5: Calculate the permeability. According to Darcy's law, the relationship between the volume flow rate of resin in the insulation layer and the pressure difference can be shown in formula (2) above.

[0083] Step 6: Front advance model. Through the change of the front position with time, combined with Darcy's law and mass conservation, the following formula (3) can be obtained:

[0084]

[0085] where x is the front position. According to the experimental data fitting curve, the first permeability 1.

[0086] Step 7: Output the experimental results. Record the first porosity and the first permeability obtained, which are used for subsequent comparison and verification.

[0087] In this embodiment, determining the second porosity and the second permeability based on the three-dimensional modeling of the microstructure includes:

[0088] (1) Sample preparation: representative samples are taken from the insulation layer of the superconducting magnet, and necessary pretreatment is carried out to ensure that the microstructure is completely preserved in the subsequent scanning. Representative samples are taken from the superconducting magnet insulation layer (dry fiber preform) that has been wound. The sample size is set to (L, W, H) , , Through pretreatment, the sample surface is smooth, and the fiber and pore structure are not damaged.

[0089] (2) Microstructure scanning: Obtain the 3D image sequence of the sample by using FIB-SEM layer-by-layer sectioning imaging or micron-level X-ray CT non-destructive scanning method. The obtained 3D image is reconstructed in a three-dimensional coordinate system to obtain the spatial distribution of the fiber phase and the pore phase of the insulating layer.

[0090] (3) Image processing and reconstruction: The 3D image is registered, denoised and threshold segmented to obtain binary voxel data, wherein 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 superposition.

[0091] (4) Porosity calculation: Count the number of pore phase voxels and the total number of voxels , and calculate the second porosity by formula (4) as follows:

[0092]

[0093] (5) Fluid domain extraction and meshing: Extract the pore part as the fluid domain, establish a numerical simulation calculation model, and set the inlet and outlet planes on the boundary. Divide the fluid domain into finite volume meshes to ensure the stability and accuracy of the seepage calculation.

[0094] (6) Numerical simulation and permeability extraction: Apply pressure difference or flow rate conditions in the model to simulate the seepage process of the resin in the pore network, and calculate the insulating layer permeability (second permeability) based on Darcy's law or volume average velocity method. Apply pressure difference at the inlet and outlet , take the dynamic viscosity of the resin (as a fixed value) as the fluid property parameter, simulate the steady-state seepage process of the resin in the pore network, calculate the fluid volume flow rate , or take the volume average velocity in the flow field .

[0095] According to Darcy's law, the calculation formula of the second permeability is shown in formula (5), or the volume average velocity method is used to calculate the second permeability by formula (6).

[0096]

[0097] If the consistency of the first porosity and the second porosity and the consistency of the first permeability and the second permeability are good, they are taken as reliable parameters input into the subsequent CFD macro-homogenization model for VPI process simulation and optimization.

[0098] When the permeability results obtained by microscopic modeling are inconsistent with the one-way flow experimental results, first check the sealing of the experimental end, the stability of the pressure difference, and whether the resin viscosity value is reasonable, and at the same time check whether the image segmentation, grid division and representative volume unit (REV) of the numerical modeling end meet the requirements, and ensure that the experimental direction and the simulation direction correspond to each other; if the deviation still exists, the multi-zone sampling statistics or the empirical formula correction can be used for correction, and finally the permeability is calibrated through parameter inversion or assimilation method, to obtain equivalent and reliable porosity and permeability input macro CFD model, so as to ensure the accuracy of subsequent VPI process simulation.

[0099] In an embodiment, the step S101 is specifically: inputting the target porosity, the target permeability and the resin dynamic viscosity into the insulation layer homogenization fluid domain model to obtain a macro homogenization model.

[0100] In the model, the resin is set as a non-Newtonian fluid, the resin dynamic viscosity changes with temperature, and the relationship between the resin dynamic viscosity and the temperature can be represented by the Arrhenius formula, as shown in formula (7):

[0101]

[0102] wherein, is the dynamic viscosity of the resin at temperature (unit: Pa·s); is a reference viscosity constant (unit: Pa·s); is an apparent activation energy (unit: J / mol); is a universal gas constant (R=8.314 J / mol·K); is an absolute temperature (unit: K). In the embodiment, the key parameters in the homogenization insulation layer fluid domain model are obtained through cross comparison of experimental data and simulation data, which ensures the reliability and universality of the parameter input. In some embodiments, the vacuum pressure impregnation process optimization method further comprises: determining an actual resin front position and an actual viscosity change value by a sensor when the vacuum pressure impregnation process is processed with the optimal process parameter combination; and adjusting the plurality of process parameters according to the actual resin front position and the actual viscosity change value.

[0103] Specifically, the present application can combine online monitoring means (such as optical sensing, acoustic emission detection, pressure / flow real-time monitoring), track the resin front position and viscosity change in real time during the VPI process, and compare and correct with the simulation model to realize dynamic regulation of the process.

[0104]

[0105] ​​​

[0106] Optionally, the present application can also introduce machine learning or intelligent algorithms to train experimental data and simulation results based on a multi-parameter optimization database, thereby realizing automatic prediction and optimization and improving the search efficiency and accuracy of process parameter combinations.

[0107] Optionally, the present application can also combine macro process optimization results with insulation layer microstructure design, improve the uniformity and permeability of the insulation layer by optimizing the glass fiber arrangement mode or the preform pore structure, and further improve the process stability. Optionally, the present application can also consider the multi-physical field coupling effect of resin curing exothermic and temperature distribution while optimizing process parameters, establish a heat-flow-solid integrated model, and optimize the resin impregnation and curing process as a whole to avoid local overheating or uneven curing.

[0109] To realize the systematic optimization of the VPI process of superconducting magnets, the present application uses a numerical simulation platform (such as Fluent, STAR-CCM+, etc.) to perform multi-factor process optimization based on the establishment of the insulation layer homogenization model.

[0110] The process of multi-factor optimization of VPI process based on the homogenization model can include the following steps:

[0111] Step 1: Model establishment, a geometric model of the superconducting magnet coil is established in a three-dimensional modeling software, and the insulation layer area is abstracted as a homogenized porous medium. The main parameters of the porous medium are porosity and permeability .

[0112] Step 2: Physical property parameter input, the porosity and permeability are obtained by experimental measurement and microscopic modeling numerical extraction, and the resin dynamic viscosity changes with temperature.

[0113] Step 3: Boundary condition setting, the inlet of the model is set as a glue injection port, and the outlet is set as a vacuum extraction port, and pressure and are applied respectively, wherein the vacuum degree is , and the pressure difference is . A thermal boundary condition is applied to the outer surface of the insulation layer, and the temperature distribution is set as . The initial state of the resin is set as a cavity, which is gradually filled when injected.

[0114] Step 4: Temperature control process simulation, the resin flow process in the insulation layer is simulated under different temperature , , boundary conditions.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Step 7: Resin curing kinetics coupling optimization. In the simulation, the resin curing kinetics model shown in formula (8) is introduced:

[0119]

[0120] 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):

[0121]

[0122] 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.

[0123] 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.

[0124] In yet another embodiment, such as Figure 3 As shown, the VPI process optimization method based on the coupling of experimentation and CFD includes:

[0125] Step 1: Obtaining Experimental Parameters

[0126] 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.

[0127] Step 2: Numerical Modeling and Prediction

[0128] 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.

[0129] Step 3: Experiment-Simulation Consistency Verification

[0130] 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 ( , , ).

[0131] Step 4: Closed-loop correction and re-simulation

[0132] 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.

[0133] Step 5: Multi-factor optimization of the process

[0134] 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.

[0135] Step 6: Optimal solution output and database establishment

[0136] The final optimized process parameter combination is used as the optimal process scheme for the superconducting magnet VPI; meanwhile, the experimental data, simulation results, and optimal parameter combination are entered into the multi-parameter process database to guide the process design of subsequent different types of superconducting magnet coils; in subsequent new designs, suitable VPI process parameters can be quickly obtained through database matching and simulation correction, realizing the standardization and generalizability of process optimization.

[0137] The process optimization method provided by the embodiment has the following advantages:

[0138] (1) Accurate acquisition of key physical parameters

[0139] The present application obtains the porosity and permeability of the insulating layer through one-way flow experiment and three-dimensional modeling of microstructure, respectively, and ensures the reliability of the input parameters through cross-validation of experimental and numerical results. This is more scientific and accurate than the traditional method of relying on experience or single means estimation.

[0140] (2) Coupling of experiment and CFD simulation

[0141] By combining the parameters measured by the experiment with the CFD homogenization model, the percolation and solidification behavior of the resin in the VPI process of the superconducting magnet insulating layer can be truly reflected, thereby avoiding the limitations of excessive simulation assumptions or experimental difficulty in comprehensive coverage, and improving the accuracy of process simulation.

[0142] (3) Multi-factor process optimization

[0143] The present application can establish a multi-parameter optimization database and obtain an optimal process combination scheme by changing temperature, vacuum pressure, number and position of resin injection ports, resin viscosity, and other process factors, and systematically carrying out simulation optimization analysis. Compared with the traditional experience method and trial-and-error method, the process development cycle is significantly shortened, and the cost investment is reduced.

[0144] (4) Ensure the uniformity and density of the insulating layer impregnation

[0145] Through comprehensive analysis of the impregnation effect, permeation time, and resin distribution uniformity, the present application can ensure that there are no bubbles and dry spots in the insulating layer of the superconducting magnet during the impregnation process, thereby greatly improving the electrical insulation performance and operation reliability of the coil.

[0146] (5) Strong scalability and generalizability

[0147] This method is not only suitable for the VPI process of the current specific type of superconducting magnet coil, but also can be extended to other magnet impregnation processes with complex structure or different material systems, and has wide adaptability and practical value.

[0148] A vacuum pressure impregnation process optimization apparatus is also provided in the present embodiment, which is used to implement the above-mentioned embodiments and preferred embodiments, which have been described above and will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

[0149] The present embodiment provides a vacuum pressure impregnation process optimization apparatus, as shown in Figure 4 comprises:

[0150] A model construction module 401 is configured to input target porosity and target permeability into an insulation layer homogenization fluid domain model to obtain a macro-homogeneous model.

[0151] A simulation module 402 is configured to simulate a first correspondence between a plurality of process parameters and impregnation performance parameters under boundary conditions according to the macro-homogeneous model, wherein the impregnation performance parameters include at least one of impregnation time, impregnation uniformity and residual bubble rate, and the plurality of process parameters include at least two of temperature, glue injection parameters, vacuum pressure, resin viscosity and resin curing kinetics parameters.

[0152] An optimization module 403 is configured to optimize the plurality of process parameters according to a plurality of first correspondences, with the optimization target being the shortest filling time, the highest uniformity and the lowest residual bubble rate, to obtain an optimal process parameter combination of the vacuum pressure impregnation process.

[0153] Further function descriptions of the above-mentioned modules and units are the same as those of the corresponding embodiments described above, and will not be repeated here.

[0154] The vacuum pressure impregnation process optimization apparatus in the present embodiment is presented in the form of functional units, where the units refer to Application Specific Integrated Circuits (ASIC), processors and memories that execute one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0155] The present embodiment also provides an electronic device, as shown in Figure 5As shown, the electronic device includes one or more processors 510, memory 520, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components communicate through the different buses and can be mounted on a common motherboard or in other manners as appropriate. The processor can process instructions for execution within the electronic device, including instructions stored in the memory or on the storage device to display graphical information for a GUI on an external input / output device, such as a display device coupled to the high-speed interface. In some alternative implementations, multiple processors and / or multiple buses can be employed as appropriate, as well as multiple memories and types of memory. Also, various components can be connected by various interfaces, not only the bus. In some implementations, for example, the high-speed interface can be implemented using a video graphics adapter (VGA) interface. Figure 5 The processor 510 is taken as an example in the embodiment.

[0156] The processor 510 can be a central processing unit, a network processor, or a combination thereof. The processor 510 can further include a hardware chip. The hardware chip can be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device can be a complex programmable logic device, a field programmable logic device, a general array logic, or any combination thereof.

[0157] The memory 520 stores instructions executable by the at least one processor 510 to cause the at least one processor 510 to perform the methods illustrated in the above embodiments.

[0158] The memory 520 can include a program storage area and a data storage area. The program storage area can store an operating system, application programs required by at least one function, and the like. The data storage area can store data created according to the use of the electronic device, and the like. In addition, the memory 520 can include a high-speed random access memory, and can further include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid state storage device. In some alternative implementations, the memory 520 can optionally include a memory disposed remotely from the processor 510, and these remote memories can be connected to the electronic device through a network. Examples of the network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0159] The memory 520 can include a volatile memory, such as a random access memory, and can also include a non-volatile memory, such as a flash memory, a hard disk, or a solid state disk. The memory 520 can also include a combination of the above-mentioned types of memories.

[0160] The electronic device further includes a communication interface 530 for communication of the electronic device with other devices or communication networks.

[0161] The embodiments of the present application further provide a computer readable storage medium, and the method according to the embodiments of the present application can be implemented in hardware, firmware, or recorded in a storage medium, or stored in a remote storage medium or a non-transitory machine readable storage medium and downloaded to a local storage medium through network and then stored in the local storage medium, so that the method described herein can be processed by such software on a storage medium using a general purpose computer, a special purpose processor, or programmable or special hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk, or a solid state disk, etc. Further, the storage medium can also include a combination of the above-mentioned memories. It can be understood that the computer, the processor, the microprocessor controller, or the programmable hardware includes a storage component that can store or receive software or computer code, which, when accessed and executed by the computer, the processor, or the hardware, implements the method shown in the above embodiments.

[0162] Part of the present application can be applied as a computer program product, for example, computer program instructions, when executed by a computer, through the operation of the computer, the method and / or technical solutions according to the present application can be invoked or provided. Those skilled in the art should understand that the form of computer program instructions in a computer readable medium includes but is not limited to source files, executable files, installation package files, etc. Correspondingly, the way of executing computer program instructions by a computer includes but is not limited to: the computer directly executes the instructions, or the computer executes the corresponding compiled program after compiling the instructions, or the computer reads and executes the instructions, or the computer reads and installs the instructions and then executes the corresponding installation program. Here, the computer readable medium can be any available computer readable storage medium or communication medium accessible to the computer.

[0163] In the description of the present specification, the description of the terms "the embodiment", "one embodiment", "some embodiments", "example", "specific example" or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any suitable manner in any one or more embodiments or examples. In addition, the skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0164] Furthermore, the terms "first", "second", etc. are used only for descriptive purposes and do not connote or imply relative importance or a quantity of the indicated technical features. Thus, features defined with "first", "second" etc. can include at least one of the features implicitly or explicitly. In the description of the application, the meaning of "a plurality" is at least two, for example two, three or the like, unless explicitly specified otherwise.

[0165] Although embodiments of the present application have been described in conjunction with the drawings, various modifications and changes can be suggested by those skilled in the art, and it is intended that the present application encompass such modifications and changes as fall within the scope of the appended claims.

Claims

1. A method of optimizing a vacuum pressure impregnation process, characterized in that, The method comprises: inputting target porosity and target permeability into an insulation layer homogenization fluid domain model to obtain a macro-homogeneous model; determining a first correspondence between a plurality of process parameters and impregnation performance parameters under boundary conditions by simulation according to the macro-homogeneous model, wherein the impregnation performance parameters include at least one of impregnation time, impregnation uniformity and residual bubble rate, the plurality of process parameters include at least two of temperature, glue injection parameter, vacuum pressure, resin viscosity and resin curing kinetics parameter, and the plurality of first correspondences include a correspondence between the temperature and resin front advance speed and total filling time, a correspondence between the glue injection parameter and filling uniformity and the impregnation time, a correspondence between the vacuum pressure and the residual bubble rate, and a correspondence between the resin viscosity, the resin curing kinetics parameter and the impregnation performance parameter; optimizing the plurality of process parameters to obtain an optimal process parameter combination of the vacuum pressure impregnation process with the shortest filling time, the highest uniformity and the lowest residual bubble rate as optimization targets according to the plurality of first correspondences; temperature optimization includes: setting different temperature boundary conditions in the macro-homogeneous model; running seepage simulation at each temperature to record resin front advance speed and total filling time; calculating an operable time window, which refers to a time interval from the beginning of resin injection into the insulation layer to the time when the viscosity of the resin rises to lose fluidity, within which the resin successfully penetrates and fills the pores of the insulation layer under the driving of the applied vacuum pressure; comparing the total filling time under each temperature condition to the operable time window to determine an optimal impregnation temperature range; glue injection parameter optimization includes: setting different glue injection port arrangements in the macro-homogeneous model, including single-point glue injection, symmetric double-point glue injection or ring-distributed glue injection; applying the same pressure difference to simulate the flow process of the resin in the insulation layer; extracting the flow field distribution to analyze whether there is a flow dead zone or stagnant area; determining the optimal number and position of glue injection ports with filling uniformity and impregnation time as evaluation indexes; vacuum pressure optimization includes: setting different initial vacuum pressures in the macro-homogeneous model and applying a constant glue injection pressure; simulating the changes of residual bubble volume fraction and resin flow speed under different vacuum degrees; evaluating the degassing efficiency and impregnation time under different vacuum degrees to select a vacuum pressure range that meets the degassing and seepage requirements; resin viscosity and resin curing kinetics parameter optimization includes: inputting resin curing kinetics parameters into the macro-homogeneous model; introducing a viscosity coupling function with temperature and time in the simulation to simulate the evolution process of resin viscosity under different process curves; comparing the time when resin viscosity reaches a critical value under different process parameters to determine whether the process can complete impregnation before the resin loses fluidity.

2. The method of claim 1, wherein, Before the inputting of the target porosity and the target permeability into the insulation layer homogenization fluid domain model, the method further comprises: obtaining a first porosity and a first permeability, wherein the first porosity is a porosity calculated by a theoretical formula under a condition of known insulation layer material parameters, and the first permeability is a permeability obtained by a unidirectional flow experiment; obtaining a second porosity and a second permeability, wherein the second porosity is a porosity determined by a fluid mechanics model simulation, and the second permeability is a permeability determined by the fluid mechanics model simulation; when a deviation between the first porosity and the second porosity is less than or equal to a preset deviation value, and a 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 determining the second permeability as the target permeability.

3. The method of claim 2, wherein, The theoretical formula is as follows: wherein, represents the first porosity, represents the areal density of the glass fiber layer in the insulation layer, represents the glass fiber material density, represents the calculation area, represents the total thickness of the insulation layer.

4. The method of claim 3, wherein, The obtaining of the second porosity and the second permeability comprises: obtaining the second porosity by a voxel statistics or volume fraction method; obtaining the second permeability by using Darcy's law.

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

6. The method of claim 5, wherein, The expression of the resin dynamic viscosity changing with temperature is as follows: wherein, η represents the dynamic viscosity of the resin at an absolute temperature T, K represents the reference viscosity constant, Ea represents the apparent activation energy, R represents the universal gas constant.

7. The method according to any one of claims 1 to 4, characterized in that, The method further comprises: when a vacuum pressure impregnation process is performed by using the optimal process parameter combination, determining an actual resin front position and an actual viscosity change value by a sensor; according to the actual resin front position and the actual viscosity change value, adjusting and controlling the plurality of process parameters.

8. A vacuum pressure impregnation process optimization device, characterized by, The device comprises: a model construction module configured to input a target porosity and a target permeability into an insulation layer homogenization fluid domain model to obtain a macroscopic homogenization model; a simulation module configured to simulate a first correspondence relationship between a plurality of process parameters and impregnation performance parameters under a boundary condition according to the macroscopic homogenization model, wherein the impregnation performance parameters comprise at least one of impregnation time, impregnation uniformity and residual bubble rate, the plurality of process parameters comprise at least two of temperature, glue injection parameter, vacuum pressure, resin viscosity and resin curing kinetics parameter, and the plurality of first correspondence relationships comprise a correspondence relationship between the temperature and resin front advancing speed and total filling time, a correspondence relationship between the glue injection parameter and filling uniformity and the impregnation time, a correspondence relationship between the vacuum pressure and the residual bubble rate, and a correspondence relationship among the resin viscosity, the resin curing kinetics parameter and the impregnation performance parameters; an optimization module configured to optimize the plurality of process parameters according to the plurality of first correspondence relationships, so as to obtain an optimal process parameter combination of a vacuum pressure impregnation process, with the shortest filling time, the highest uniformity and the lowest residual bubble rate as optimization targets. The temperature optimization comprises: setting different temperature boundary conditions in the macro-homogeneous model; running seepage simulation at each temperature, recording resin front advancing speed and total filling time; calculating an operable time window, which is the time interval from the beginning of resin injection into the insulation layer to the viscosity rising to lose fluidity, within which the resin successfully penetrates and fills the pores of the insulation layer under the driving of the applied vacuum pressure; comparing the total filling time under each temperature condition to the operable time window to determine the optimal impregnation temperature range; The glue injection parameter optimization comprises: setting different glue injection port arrangements in the macro-homogeneous model, including single-point glue injection, symmetric double-point glue injection or ring-distributed glue injection; applying the same pressure difference to simulate the resin flow process in the insulation layer; extracting the flow field distribution to analyze whether there is a flow dead zone or stagnant area; taking filling uniformity and impregnation time as evaluation indexes to determine the optimal number and position of glue injection ports; The vacuum pressure optimization comprises: setting different initial vacuum pressures in the macro-homogeneous model and applying a constant glue injection pressure; simulating the changes of residual bubble volume fraction and resin flow speed under different vacuum degrees; evaluating the degassing efficiency and impregnation time under different vacuum degrees to select a vacuum pressure range that meets the degassing and seepage requirements; The resin viscosity and resin curing kinetics parameter optimization comprises: inputting resin curing kinetics parameters in the macro-homogeneous model; introducing a viscosity coupling function with temperature and time in the simulation to simulate the evolution process of resin viscosity under different process curves; comparing the time when resin viscosity reaches the critical value under different process parameters to determine whether the process can complete impregnation before the resin loses fluidity.

9. An electronic device, comprising: Comprises: A memory and a processor, which are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the vacuum pressure impregnation process optimization method of any one of claims 1-7.

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

11. A computer program product, characterised in that, Comprises computer instructions for making a computer execute the vacuum pressure impregnation process optimization method of any one of claims 1-7.

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