Method for predicting air trapping area in LCM filling process based on compressible two-phase flow model

Abstract

The invention belongs to the technical field of simulation defect prediction, and particularly discloses an LCM filling process air trapping area prediction method based on a compressible two-phase flow model, which comprises the following steps: establishing the compressible two-phase flow model for predicting the air trapping area evolution in the LCM filling process, and determining a control equation set in the LCM filling process based on the compressible two-phase flow model; performing numerical discretization of a time domain and a space domain on the compressible two-phase flow model by adopting a finite volume method to obtain a discretized algebraic equation set corresponding to the control equation set; and solving the discretization algebraic equation set based on a pressure implicit operator splitting algorithm to obtain pressure values, speed values and unit phase fraction values of the resin fluid and the air fluid in the LCM filling process, and determining a three-dimensional numerical simulation result of air trapping area evolution based on the pressure values, the speed values and the unit phase fraction values. According to the invention, the prediction accuracy and effectiveness of the air trapping area in the LCM filling process can be improved.

Description

Technical Field

[0001] This application belongs to the field of simulation defect prediction technology, and more specifically, relates to a method for predicting trapped gas regions in the LCM filling process based on a compressible two-phase flow model. Background Technology

[0002] Liquid molding (LCM) of composite materials has been widely used in the manufacture of complex components in aerospace, marine, automotive, and infrastructure industries due to its high efficiency, cost advantages, advanced manufacturing characteristics, and excellent component performance. However, void defects generated during LCM molding can adversely affect the mechanical properties of composite components, especially reducing interlaminar shear strength, flexural strength, and compressive strength.

[0003] In LCM (Liquid Crystal Molding) processes, mechanical air trapping is considered a major cause of void-type defects. In actual molding, complex preforms typically contain fiber fabrics with varying permeability, flow media, and mold edges that may contain channels. When the resin flow splits and merges in low-permeability areas, and venting is lacking, air becomes trapped in these areas, forming trapped air zones and leading to dry spots. Dry spots are the most concerning void-type molding defect in LCM, and their final size and location are influenced by the evolution of trapped air zones in the flow field. Dry spots caused by the macroscopic flow complexity of the preform can usually be avoided through process optimization. In actual production, the fluid pressure in the filling area is typically adjusted by alternately opening and closing each injection port until the trapped air zones are pushed to the venting ports for discharge. Simulating and predicting the evolution of trapped air zones into dry spots is crucial for optimizing the process to avoid dry spot defects.

[0004] The existing method for predicting trapped air regions follows this process: After each advance of the flow front, a loop is executed to search for unfilled control volumes. Based on the inter-cell topology, control volumes that cannot be connected to the exhaust port are identified and marked as dry point regions. As the filling process continues, the size and pressure changes of the dry point regions are updated according to the ideal gas law. Because the compression and flow characteristics of air are not realistically considered in the governing equations, the trapped air region can only continuously shrink inward under the pressure of the resin according to the ideal gas law until it approaches the pressure of the surrounding resin, after which it no longer changes. When the flow field pressure distribution suddenly changes (a new exhaust port opens), the size and location of the trapped air region no longer change.

[0005] Therefore, existing technologies are insufficient to accurately and effectively predict the actual evolution of trapped gas regions, and a more accurate and effective method for predicting the evolution of trapped gas regions is urgently needed. Summary of the Invention

[0006] In view of the shortcomings of the prior art, the purpose of this application is to provide a method for predicting the trapped gas region in the LCM filling process based on a compressible two-phase flow model, which aims to solve the problem of insufficient accuracy and effectiveness in predicting the trapped gas region in the prior art.

[0007] To achieve the above objectives, in a first aspect, this application provides a method for predicting trapped gas regions during LCM filling processes based on a compressible two-phase flow model, comprising: A compressible two-phase flow model is established to predict the evolution of trapped gas regions during the LCM filling process. Based on the compressible two-phase flow model, a set of governing equations for the LCM filling process is determined. The set of governing equations includes momentum conservation equations, mass conservation equations, unit phase fraction convection equations, and gas state equations. The compressible two-phase flow model is numerically discretized in both the time and spatial domains using the finite volume method to obtain a set of discretized algebraic equations corresponding to the governing equations. Solving the discretized algebraic equations yields the pressure, velocity, and element phase separation values ​​of the resin fluid and air fluid during LCM filling. Based on these pressure, velocity, and element phase separation values, the three-dimensional numerical simulation results of the gas trapping region evolution are determined.

[0008] Optionally, the method for constructing the momentum conservation equation includes: The rate of change of momentum of the resin fluid unit per unit time is determined based on the density of the resin fluid unit and the velocity of the resin flow. The gravity density variable is determined based on the gravitational acceleration and the density; the pressure gradient is determined as the pressure variable based on the resin pressure; and the viscosity variable is determined based on the resin viscosity, fiber porosity, and the permeability tensor of the fiber preform, combined with the velocity. Based on the equilibrium relationship established between the rate of change of momentum and the variables of gravity density, pressure, and viscosity, the momentum conservation equation is obtained.

[0009] Optionally, the method for constructing the mass conservation equation includes: The phase fraction of a unit is determined based on the ratio of the volume of resin fluid in the resin fluid unit to the total volume of the unit. Determine the rate of change of air density per unit time, and determine the derivative of air density based on the rate of change of air density, the flow rate of resin, and the air density gradient; The mass conservation equation is obtained by establishing a balance relationship between the velocity field divergence, the phase fraction of the unit, the air density, and the derivative of the air density.

[0010] Optionally, the method for constructing the unit phase fractional convection equation includes: Determine the rate of change of the unit phase fraction per unit time, and determine the unit phase fraction convection term based on the velocity field divergence and the unit phase fraction; Based on the phase fraction change rate, the unit phase fraction convection term, and the unit phase fraction, air density, and air density derivative, an equilibrium relationship is established to obtain the unit phase fraction convection equation; Wherein, if the number of phases of the unit is If the phase fraction is 1, it means the unit is entirely air; if the phase fraction is 1, it means the unit is entirely resin; if the phase fraction is between 0 and 1, it means the unit contains both resin and air.

[0011] Optionally, the method for constructing the gas equation of state includes: The gas equation of state is obtained by establishing an equilibrium relationship between the molar mass, pressure, and temperature of air and the ideal gas constant, along with the air density.

[0012] Optionally, the discretization process of the momentum conservation equation includes: Determine the resin velocity in cell p at the current time and the resin velocity in cell p at the previous time to determine the rate of velocity change, and determine the discrete momentum change rate based on the rate of velocity change and density; The discrete viscosity variable is determined based on the resin velocity and viscosity variable in the current time unit p; Based on the equilibrium relationship established between the momentum change rate and the gravity density variable, pressure variable, and discrete viscosity variable, the discrete format of the momentum conservation equation is determined. Based on the rate of change of the trace binding density of tensor M, the discrete coefficients of the momentum conservation equation are obtained. The source term coefficients are obtained by combining the discrete scheme of the momentum conservation equation with the trace of the tensor M; Based on the resin velocity in unit p at the current moment, and combined with the discrete coefficients, source term coefficients, and pressure gradient, the discretized momentum conservation equation is obtained.

[0013] Optionally, the tensor M is determined based on the resin viscosity, fiber porosity, and fiber preform permeability tensor, as shown in the following formula:

[0014] in, It is the viscosity of the resin. For fiber porosity, It is the permeability tensor of the fiber preform. It is the trace of tensor M. It is a second-order tensor of the standard unit. refer to The anisotropic part is explicitly processed. refer to It is the isotropic part, which is implicitly processed.

[0015] Optionally, the discretization method for the mass conservation equation includes: The volumetric flow rate is obtained by summing the velocity on the unit surface and the area vector of the unit P surface. Based on the volumetric flow rate, the unit phase fraction, air density, and air density derivative, an equilibrium relationship is constructed, and the discrete format of the mass conservation equation is determined. The discretized momentum conservation equation is rearranged and combined with the discretized format of the mass conservation equation to obtain the discretized mass conservation equation.

[0016] Optionally, the discrete formula for the unit phase fraction includes:

[0017] in, The phase fraction on the unit interface. It is the phase fraction of unit P at the current time. It is the phase fraction of unit P in the previous time step; The velocity on the unit surface, It is a unit P The area vector of the surface.

[0018] Optionally, the solution process of the discretized algebraic equation system includes: Solve the unit phase fraction convection equation to obtain the unit phase fraction field in the three-dimensional numerical simulation results, so as to update the flow front interface between resin and air; The pressure equation derived from the coupling of the momentum conservation equation and the mass conservation equation is solved by the PISO algorithm to obtain the pressure field in the three-dimensional numerical simulation results. The pressure field is used to solve the discretized momentum conservation equation to obtain the velocity field in the three-dimensional numerical simulation results; The gas equation of state is solved using the iterative process of the PISO algorithm to obtain the updated air density value; Repeat the process of solving the pressure field, velocity field, and air density values ​​until the solutions for the pressure field and velocity field reach the preset convergence criteria, and until the entire filling process is simulated, to obtain the three-dimensional numerical simulation results.

[0019] Secondly, this application provides a system for predicting trapped gas regions during LCM filling process based on a compressible two-phase flow model, comprising: The model building module is used to build a compressible two-phase flow model for predicting the evolution of trapped gas regions during the LCM filling process. Based on the compressible two-phase flow model, the control equations for the LCM filling process are determined. The control equations include the momentum conservation equation, the mass conservation equation, the unit phase fraction convection equation, and the gas state equation. The discretization module is used to perform numerical discretization of the compressible two-phase flow model in the time and spatial domains using the finite volume method, so as to obtain a discretized algebraic equation set corresponding to the control equation set. The prediction module is used to solve the discretized algebraic equations to obtain the pressure, velocity, and unit phase separation values ​​of the resin fluid and air fluid during the LCM filling process. Based on the pressure, velocity, and unit phase separation values, the three-dimensional numerical simulation results of the evolution of the trapped gas region are determined.

[0020] Thirdly, this application provides an electronic device, comprising: at least one memory for storing a program; and at least one processor for executing the program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to execute the method described in the first aspect or any possible implementation thereof.

[0021] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to perform the method described in the first aspect or any possible implementation thereof.

[0022] Fifthly, this application provides a computer program product that, when run on a processor, causes the processor to perform the method described in the first aspect or any possible implementation thereof.

[0023] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0024] Overall, the technical solutions conceived in this application have the following beneficial effects compared with the prior art: (1) Based on the compressible two-phase flow model, this application defines resin as an incompressible fluid and air as a compressible fluid. The compressibility characteristics and flow behavior of air are directly incorporated into the control equation. By constructing a set of equations and solving them discretely, the prediction results can accurately reflect the entire process of the complex dynamic evolution of the trapped air region after its formation. This allows for a more accurate prediction of the evolution of the trapped air region during LCM filling, thereby improving the accuracy and effectiveness of the prediction.

[0025] (2) This application greatly simplifies the prediction process of the trapped gas region. The three-dimensional numerical simulation results obtained can directly reflect the evolution of the trapped gas region. There is no need to cyclically search the topological relationship between the unfilled unit and the exhaust port unit in each time step, which significantly improves the prediction efficiency and reliability of the trapped gas region evolution in complex molding scenarios. Attached Figure Description

[0026] Figure 1 This is one of the flowcharts illustrating the method for predicting trapped gas regions in the LCM filling process based on a compressible two-phase flow model provided in this application embodiment; Figure 2 This is a schematic diagram of the computational domain unit provided in an embodiment of this application; Figure 3 This is the second flowchart illustrating the method for predicting trapped gas regions in the LCM filling process based on a compressible two-phase flow model provided in this application embodiment. Figure 4 This is a schematic diagram of the material composition and filling process of the flat plate infusion experiment provided in the embodiments of this application; Figure 5 This is a schematic diagram showing the specific dimensions of each part of the flat plate infusion experiment provided in the embodiments of this application; Figure 6 This is a comparative schematic diagram of the upper surface flow front at different filling times in the embodiments of this application; Figure 7 The different filling times Sim1 and Sim2 in the embodiments of this application are Figure 5 Schematic diagram of the flow front on the DD section; Figure 8 This is a schematic diagram of the pressure distribution on the upper surfaces of Sim1 and Sim2 at different filling times in the embodiments of this application; Figure 9 This is a schematic diagram of the pressure distribution of Sim1 and Sim2 at different filling times on the DD section in the embodiments of this application; Figure 10 This is a schematic diagram comparing the pressure changes of Sim1 and Sim2 at monitoring points P1, P2, and P3 over time in an embodiment of this application. Figure 11 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0028] In this article, the term "and / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. The symbol " / " in this article indicates that the related objects are in an "or" relationship; for example, A / B means A or B.

[0029] The terms "first" and "second," etc., used in the specification and claims herein are used to distinguish different objects, not to describe a specific order of objects. For example, "first response message" and "second response message," etc., are used to distinguish different response messages, not to describe a specific order of response messages.

[0030] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0031] In the description of the embodiments of this application, unless otherwise stated, "multiple" means two or more, for example, multiple processing units means two or more processing units, multiple elements means two or more elements, etc.

[0032] The embodiments of this application are described below with reference to the accompanying drawings.

[0033] Reference Figure 1 This application provides a method for predicting trapped gas regions during LCM filling based on a compressible two-phase flow model, including: S101. Establish a compressible two-phase flow model for predicting the evolution of trapped gas regions during the LCM filling process. Based on the compressible two-phase flow model, determine the set of governing equations for the LCM filling process. The set of governing equations includes the momentum conservation equation, the mass conservation equation, the unit phase fraction convection equation, and the gas state equation. S102. The finite volume method is used to numerically discretize the compressible two-phase flow model in the time and space domains to obtain a set of discretized algebraic equations corresponding to the governing equations. S103. Based on the pressure implicit operator splitting algorithm, the discretized algebraic equations are solved to obtain the pressure, velocity and element phase separation values ​​of the resin fluid and air fluid during the LCM filling process. Based on the pressure, velocity and element phase separation values, the three-dimensional numerical simulation results of the evolution of the trapped air region are determined.

[0034] Specifically, the core of this step is to construct a more physically complete mathematical model to replace the simplified treatment of the trapped air region in traditional methods. By establishing a two-phase flow model that includes a compressible air phase and an incompressible resin phase, the resin filling process is described as a dynamic process of two fluids.

[0035] The governing equations of the model include: a momentum conservation equation to describe the motion of the fluid in the porous medium; a mass conservation equation coupled with the air compressibility effect to ensure the conservation of total mass between the two phases; a unit phase fractional convection equation to accurately track the frontal flow of resin and air; and an ideal gas equation of state that correlates air density, pressure, and temperature. These tightly coupled governing equations together constitute a system that can realistically reflect the physical mechanism of the dynamic evolution of the trapped gas region.

[0036] In step S102, after establishing the continuous set of control equations, the aim is to transform them into a computer-solvable algebraic form using numerical methods. The finite volume method is used as the discretization tool. This method transforms the continuous differential equations into a discrete set of algebraic equations by dividing the computational domain into a large number of tiny control volumes and integrating the partial differential control equations on each control volume.

[0037] After this step, the originally complex set of partial differential control equations is systematically transformed into a series of linear algebraic equations concerning unit pressure, velocity, and phase fraction.

[0038] Finally, step S103 utilizes a pressure-implicit operator splitting algorithm to iteratively solve the discrete equations, ultimately outputting visualized 3D simulation results. Within each computation time step, the solution process follows a rigorous procedure: first, the discrete phase fractional equations are solved, using the latest velocity field as input to predict and update the new position of the resin-air interface; then, the iterative loop of the pressure-implicit operator splitting algorithm is initiated, sequentially solving the pressure equations, explicitly updating the velocity field, and simultaneously using the latest pressure to correct the air density through the gas state equation until the flow field solution converges. By cyclically executing the above time-stepping process, the entire process of the cavity from an empty state to complete filling can be dynamically reproduced. The final output is a 3D numerical simulation result of the evolution of the trapped air region. (Refer to...) Figure 2 , Figure 2 This is a schematic diagram of the computational domain unit in an embodiment of this application.

[0039] Optionally, the method for constructing the momentum conservation equation includes: The rate of change of momentum of the resin fluid unit per unit time is determined based on the density of the resin fluid unit and the velocity of the resin flow. The gravity density variable is determined based on the gravitational acceleration and the density; the pressure gradient is determined as the pressure variable based on the resin pressure; and the viscosity variable is determined based on the resin viscosity, fiber porosity, and the permeability tensor of the fiber preform, combined with the velocity. Based on the equilibrium relationship established between the rate of change of momentum and the variables of gravity density, pressure, and viscosity, the momentum conservation equation is obtained.

[0040] Specifically, as shown in the following formula: (1) in, The density of the fluid element, The speed at which the resin flows. It is the pressure of the resin. It is a pressure gradient. It is the acceleration due to gravity. For the flow of time, It is the viscosity of the resin. For fiber porosity, It is the permeability tensor of the fiber preform.

[0041] It should be noted that, for ease of explanation, in this embodiment, the variable on the left side of the equation is named the rate of change of momentum, and the variables added and subtracted on the right side of the equation are the gravitational density variable, the pressure variable, and the viscosity variable, respectively. These variables may not exist in actual applications; this embodiment is only for ease of explanation and understanding.

[0042] Optionally, the method for constructing the mass conservation equation includes: The phase fraction of a unit is determined based on the ratio of the volume of resin fluid in the resin fluid unit to the total volume of the unit. Determine the rate of change of air density per unit time, and determine the derivative of air density based on the rate of change of air density, the flow rate of resin, and the air density gradient; The mass conservation equation is obtained by establishing a balance relationship between the velocity field divergence, the phase fraction of the unit, the air density, and the derivative of the air density.

[0043] Specifically, as shown in the following formula: (2) in, Let the velocity field divergence be... The unit phase fraction represents the ratio of the resin fluid volume in a unit cell to the total volume of the unit cell. For the density of air, The rate of change of air density is given by the following formula: Where, is the rate of change of air density. This represents the air density gradient.

[0044] Optionally, the method for constructing the unit phase fractional convection equation includes: Determine the rate of change of the unit phase fraction per unit time, and determine the unit phase fraction convection term based on the velocity field divergence and the unit phase fraction; Based on the phase fraction change rate, the unit phase fraction convection term, and the unit phase fraction, air density, and air density derivative, an equilibrium relationship is established to obtain the unit phase fraction convection equation; Wherein, if the number of phases of the unit is If the phase fraction is 1, it means the unit is entirely air; if the phase fraction is 1, it means the unit is entirely resin; if the phase fraction is between 0 and 1, it means the unit contains both resin and air.

[0045] Specifically, the unit phase fractional convection equation is: (3) Among them, if If so, it means the unit is entirely air; if If so, it means that the unit is entirely made of resin; if A value between 0 and 1 indicates that the cell contains both resin and air.

[0046] Optionally, the method for constructing the gas equation of state includes: The gas equation of state is obtained by establishing an equilibrium relationship between the molar mass, pressure, and temperature of air and the ideal gas constant, along with the air density.

[0047] (4) in, The molar mass of air, For air pressure, Let be the ideal gas constant. The temperature of the air.

[0048] Optionally, the discretization process of the momentum conservation equation includes: Determine the resin velocity in cell p at the current time and the resin velocity in cell p at the previous time to determine the rate of velocity change, and determine the discrete momentum change rate based on the rate of velocity change and density; The discrete viscosity variable is determined based on the resin velocity and viscosity variable in the current time unit p; Based on the equilibrium relationship established between the momentum change rate and the gravity density variable, pressure variable, and discrete viscosity variable, the discrete format of the momentum conservation equation is determined. Based on the rate of change of the trace binding density of tensor M, the discrete coefficients of the momentum conservation equation are obtained. The source term coefficients are obtained by combining the discrete scheme of the momentum conservation equation with the trace of the tensor M; Based on the resin velocity in unit p at the current moment, and combined with the discrete coefficients, source term coefficients, and pressure gradient, the discretized momentum conservation equation is obtained.

[0049] Specifically, after numerical discretization using the finite volume method, the discretization scheme of the momentum conservation equation is as follows: (5) in, It is the unit at the current moment. P The rate of resin mixing, It is the unit of the previous moment. P The rate of resin mixing, It is a time interval. It is a unit P The viscosity value on the surface; Define a tensor ,right Perform anisotropic decomposition: (6) in, It is the viscosity of the resin. For fiber porosity, It is the permeability tensor of the fiber preform. It is the trace of tensor M. It is a second-order tensor of the standard unit. refer to The anisotropic part is explicitly processed. refer to It is the isotropic part, which is implicitly processed.

[0050] The formulas for calculating the coefficient of variation and the source term coefficient are as follows: (7) (8) in, For discrete coefficients, These are the coefficients of the source term; Discretized algebraic equations are obtained using the discrete coefficients and source term coefficients: (9) in, The current resin velocity in cell p. This represents the pressure gradient.

[0051] Optionally, the discretization method for the mass conservation equation includes: The volumetric flow rate is obtained by summing the velocity on the unit surface and the area vector of the unit P surface. Based on the volumetric flow rate, the unit phase fraction, air density, and air density derivative, an equilibrium relationship is constructed, and the discrete format of the mass conservation equation is determined. The discretized momentum conservation equation is rearranged and combined with the discretized format of the mass conservation equation to obtain the discretized mass conservation equation.

[0052] Specifically, the discrete format of the mass conservation equation is as follows: (10) in, Represents the velocity on the surface of the unit cell The value on the cell surface, It is a unit P Surface area vector It is a unit P Summing the values ​​across all surfaces, the result is named the volumetric flow rate, i.e. .

[0053] At the current moment, the discretized momentum conservation equation can be rearranged as: (11) Substituting into equation (10) above, we get: (12) in, It is a unit P On the surface , It is a unit P On the surface Formula (12) is the discretized mass conservation equation.

[0054] Optionally, the discrete formula for the unit phase fraction includes: (13) in, The phase fraction on the unit interface. It is the phase fraction of unit P at the current time. It is the phase fraction of unit P in the previous time step; The velocity on the unit surface, It is a unit P Surface area vector It is the volume of unit P.

[0055] In summary, Equation (9) is the discretized momentum conservation equation, Equation (13) is the discretized mass conservation equation, and the discretized cell phase fraction convection equation in Equation (13) is the discretized algebraic equation set obtained after numerical discretization of the control equation set.

[0056] Optionally, the solution process of the discretized algebraic equation set includes: Solving the cell phase fraction convection equation to obtain the cell phase fraction field in the three-dimensional numerical simulation result, so as to update the flow front interface between the resin and the air; Solving the pressure equation derived by coupling the momentum conservation equation and the mass conservation equation through the PISO algorithm to obtain the pressure field in the three-dimensional numerical simulation result; Using the pressure field to solve the discretized momentum conservation equation to obtain the velocity field in the three-dimensional numerical simulation result; Using the iterative process of the PISO algorithm to solve the gas state equation to obtain an updated air density value; Repeatedly execute the solution processes of the pressure field, the velocity field, and the air density value until the solutions of the pressure field and the velocity field reach a preset convergence criterion, until the simulation of the entire filling process is completed, and the three-dimensional numerical simulation result is obtained.

[0057] Refer to Figure 3 , the process of solving the equation set in this embodiment is as follows: At the beginning of each time step, first solve the discretized cell phase fraction convection equation. The essence of this equation is a transport equation, which is used to track the volume fraction distribution of the resin phase in the computational domain. Solving this equation means calculating the volume ratio (F value) filled with resin in each grid cell at the current time step. According to the calculation result (F = 1 for pure resin, F = 0 for pure air, 0 < F < 1 for the two-phase interface), clarify the spatial ranges occupied by the resin and the air at the current moment.

[0058] After clarifying the phase distribution, the process enters the internal iteration loop of the PISO algorithm. Its primary task is to solve the pressure field: based on the updated phase fraction field and the velocity field of the previous step, solve the equation derived by coupling the momentum equation and the mass conservation equation, and the solution result is to obtain a unified pressure field that can simultaneously coordinate the resin flow and the air compression effect. Using this newly calculated pressure field, immediately update the air density value through the ideal gas state equation.

[0059] After obtaining the new pressure field and air density, the discretized momentum conservation equation needs to be solved to update the velocity field. The newly solved pressure gradient is substituted into the momentum equation as a known quantity to solve for a new velocity field that satisfies the current momentum balance. The new velocity field responds to the driving force of the pressure gradient and also takes into account the drag of the fiber preform and fluid inertia, resulting in an updated fluid motion state corresponding to the latest pressure field.

[0060] Using the above steps, an internal iterative loop of PISO is constructed. The pressure field is solved again using the latest obtained velocity field and air density, and the density and velocity are updated sequentially until the changes in the pressure field and velocity field are less than the preset convergence criterion. Then, the process is advanced to the next time step, repeating the entire process from the first step until the simulation ends. Finally, a complete three-dimensional numerical simulation result is output, which accurately reveals the evolution of the trapped air region.

[0061] The following is a detailed description with reference to the accompanying drawings of specific embodiments: Reference Figure 4 , Figure 4 This is a schematic diagram of the material composition and filling process of the plate infusion experiment in the embodiments of this application; see reference. Figure 5 , Figure 5 This is a schematic diagram showing the specific dimensions of each part of the flat plate infusion experiment in the embodiments of this application. Further, refer to... Figure 6 , Figure 6 This is a comparative schematic diagram of the upper surface flow front at different filling times in the embodiments of this application; Figure 7 The different filling times Sim1 and Sim2 in the embodiments of this application are Figure 5 Schematic diagram of the flow front on the DD section; Figure 8 This is a schematic diagram of the pressure distribution on the upper surfaces of Sim1 and Sim2 at different filling times in the embodiments of this application; Figure 9 This is a schematic diagram of the pressure distribution of Sim1 and Sim2 at different filling times on the DD section in the embodiments of this application; Figure 10 This is a schematic diagram comparing the pressure changes of Sim1 and Sim2 at monitoring points P1, P2, and P3 over time in an embodiment of this application.

[0062] As can be seen from the above figures, the trapped gas region prediction method (Sim1) based on the compressible two-phase flow model proposed in this application can accurately capture the dynamic evolution behavior of the trapped gas region during the filling process. In particular, after the vent is opened (190 seconds), Sim1 successfully predicted the entire process of the trapped gas region migrating to the vent, shrinking and finally being discharged under pressure. Its prediction results are highly consistent with experimental observations. In contrast, the existing method (Sim2) can only simulate the initial shrinkage of the trapped gas region under resin pressure and cannot respond to sudden changes in flow field pressure (such as opening a new vent). After the vent is opened, the size and position of the trapped gas region hardly change, which is seriously inconsistent with experimental phenomena. This fully demonstrates the significant superiority and effectiveness of the method in predicting the dynamic evolution of the trapped gas region.

[0063] It should be understood that the above-described device is used to execute the methods in the above embodiments. The implementation principle and technical effect of the corresponding program modules in the device are similar to those described in the above methods. The working process of the device can be referred to the corresponding process in the above methods, and will not be repeated here.

[0064] Reference Figure 11 Based on the methods in the above embodiments, this application provides an electronic device that may include: a processor 111, a communications interface 112, a memory 113, and a communication bus 114. The processor 111, communications interface 112, and memory 113 communicate with each other via the communication bus 114. The processor 111 can call logical instructions in the memory 113 to execute the methods in the above embodiments.

[0065] Furthermore, the logical instructions in the aforementioned memory 113 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0066] Based on the methods in the above embodiments, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0067] Based on the methods in the above embodiments, this application provides a computer program product that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0068] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0069] The method steps in this application embodiment can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0070] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0071] It is understood that the various numerical designations used in the embodiments of this application are merely for the convenience of description and are not intended to limit the scope of the embodiments of this application.

[0072] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for predicting trapped gas regions during LCM filling process based on a compressible two-phase flow model, characterized in that, include: A compressible two-phase flow model is established to predict the evolution of trapped gas regions during the LCM filling process. Based on the compressible two-phase flow model, a set of governing equations for the LCM filling process is determined. The set of governing equations includes momentum conservation equations, mass conservation equations, unit phase fraction convection equations, and gas state equations. The compressible two-phase flow model is numerically discretized in both the time and spatial domains using the finite volume method to obtain a set of discretized algebraic equations corresponding to the governing equations. Solving the discretized algebraic equations yields the pressure, velocity, and element phase separation values ​​of the resin fluid and air fluid during LCM filling. Based on these pressure, velocity, and element phase separation values, the three-dimensional numerical simulation results of the gas trapping region evolution are determined.

2. The method for predicting trapped gas regions in the LCM filling process based on a compressible two-phase flow model according to claim 1, characterized in that, The method for constructing the momentum conservation equation includes: The rate of change of momentum of the resin fluid unit per unit time is determined based on the density of the resin fluid unit and the velocity of the resin flow. The gravity density variable is determined based on the gravitational acceleration and the density; the pressure gradient is determined as the pressure variable based on the resin pressure; and the viscosity variable is determined based on the resin viscosity, fiber porosity, and the permeability tensor of the fiber preform, combined with the velocity. Based on the equilibrium relationship established between the rate of change of momentum and the variables of gravity density, pressure, and viscosity, the momentum conservation equation is obtained.

3. The method for predicting trapped gas regions in the LCM filling process based on a compressible two-phase flow model according to claim 1, characterized in that, The method for constructing the mass conservation equation includes: The phase fraction of a unit is determined based on the ratio of the volume of resin fluid in the resin fluid unit to the total volume of the unit. Determine the rate of change of air density per unit time, and determine the derivative of air density based on the rate of change of air density, the flow rate of resin, and the air density gradient; The mass conservation equation is obtained by establishing a balance relationship between the velocity field divergence, the phase fraction of the unit, the air density, and the derivative of the air density.

4. The method for predicting trapped gas regions in the LCM filling process based on a compressible two-phase flow model according to claim 1, characterized in that, The method for constructing the unit phase fraction convection equation includes: Determine the rate of change of the unit phase fraction per unit time, and determine the unit phase fraction convection term based on the velocity field divergence and the unit phase fraction; Based on the phase fraction change rate, the unit phase fraction convection term, and the unit phase fraction, air density, and air density derivative, an equilibrium relationship is established to obtain the unit phase fraction convection equation; Wherein, if the number of phases of the unit is If the phase fraction is 1, it means the unit is entirely air; if the phase fraction is 1, it means the unit is entirely resin; if the phase fraction is between 0 and 1, it means the unit contains both resin and air.

5. The method for predicting trapped gas regions in the LCM filling process based on a compressible two-phase flow model according to claim 1, characterized in that, The method for constructing the gas law includes: The gas equation of state is obtained by establishing an equilibrium relationship between the molar mass, pressure, and temperature of air and the ideal gas constant, along with the air density.

6. The method for predicting trapped gas regions in the LCM filling process based on a compressible two-phase flow model according to claim 2, characterized in that, The discretization process of the momentum conservation equation includes: Determine the resin velocity in cell p at the current time and the resin velocity in cell p at the previous time to determine the rate of velocity change, and determine the discrete momentum change rate based on the rate of velocity change and density; The discrete viscosity variable is determined based on the resin velocity and viscosity variable in the current time unit p; Based on the equilibrium relationship established between the momentum change rate and the gravity density variable, pressure variable, and discrete viscosity variable, the discrete format of the momentum conservation equation is determined. Based on the rate of change of the trace binding density of tensor M, the discrete coefficients of the momentum conservation equation are obtained. The source term coefficients are obtained by combining the discrete scheme of the momentum conservation equation with the trace of the tensor M; Based on the resin velocity in unit p at the current moment, and combined with the discrete coefficients, source term coefficients, and pressure gradient, the discretized momentum conservation equation is obtained.

7. The method for predicting trapped gas regions in the LCM filling process based on a compressible two-phase flow model according to claim 6, characterized in that, The tensor M is determined based on the resin viscosity, fiber porosity, and fiber preform permeability tensors, as shown in the following formula: in, It is the viscosity of the resin. For fiber porosity, It is the permeability tensor of the fiber preform. It is the trace of tensor M. It is a second-order tensor of the standard unit. refer to The anisotropic part is explicitly processed. refer to It is the isotropic part, which is implicitly processed.

8. The method for predicting trapped gas regions in the LCM filling process based on a compressible two-phase flow model according to claim 1, characterized in that, The discretization methods for the mass conservation equation include: The volumetric flow rate is obtained by summing the velocity on the unit surface and the area vector of the unit P surface. Based on the volumetric flow rate, the unit phase fraction, air density, and air density derivative, an equilibrium relationship is constructed, and the discrete format of the mass conservation equation is determined. The discretized momentum conservation equation is rearranged and combined with the discretized format of the mass conservation equation to obtain the discretized mass conservation equation.

9. The method for predicting trapped gas regions in the LCM filling process based on a compressible two-phase flow model according to claim 1, characterized in that, The discrete formula for the unit phase fraction includes: in, The phase fraction on the unit interface. It is the phase fraction of unit P at the current time. It is the phase fraction of unit P in the previous time step; The velocity on the unit surface, It is a unit P The area vector of the surface.

10. The method for predicting trapped gas regions in the LCM filling process based on a compressible two-phase flow model according to claim 8, characterized in that, The process of solving the discretized algebraic equation system includes: Solve the unit phase fraction convection equation to obtain the unit phase fraction field in the three-dimensional numerical simulation results, so as to update the flow front interface between resin and air; The pressure equation derived from the coupling of the momentum conservation equation and the mass conservation equation is solved by the PISO algorithm to obtain the pressure field in the three-dimensional numerical simulation results. The pressure field is used to solve the discretized momentum conservation equation to obtain the velocity field in the three-dimensional numerical simulation results; The gas equation of state is solved using the iterative process of the PISO algorithm to obtain the updated air density value; Repeat the process of solving the pressure field, velocity field, and air density values ​​until the solutions for the pressure field and velocity field reach the preset convergence criteria, and until the entire filling process is simulated, to obtain the three-dimensional numerical simulation results.

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