Kinetics study and degree of cure prediction method for multiphase castable curing system

Abstract

The application discloses a kind of multiphase pouring solidification system dynamics research and solidification degree prediction method, belong to composite material preparation process field.The method includes: to multiphase pouring slurry isothermal rheology test, to obtain the data of storage modulus variation with time characterizing physical structure evolution;According to the data, the autocatalytic reaction kinetics model characterizing solidification process is established;Further, the built kinetics model is implanted as reaction source term in finite element software, and a thermal-chemical coupling model is constructed to simulate and predict the evolution of temperature field and solidification degree field during solidification.The present application gives full play to the advantages of rheology in capturing structure evolution, and the built model can more accurately describe the crosslinking network development behavior in the later stage of pouring system solidification, thereby realizing high-precision prediction of the internal solidification degree field, and providing direct and reliable basis for optimizing the solidification process.

Description

Technical Field

[0001] This invention relates to the field of composite material preparation technology and simulation technology, and in particular to a method for studying the dynamics and predicting the degree of curing of a multiphase casting and curing system based on rheological testing. Background Technology

[0002] The curing process of multiphase casting curing systems is a crucial step in their preparation. This process involves chemical reactions and physical cross-linking, directly affecting the final properties of the material. Therefore, accurately characterizing the curing kinetics and establishing reliable predictive models are essential for optimizing the curing process and controlling product quality.

[0003] Currently, differential scanning calorimetry (DSC) is the most commonly used method for studying curing kinetics. This method derives kinetic parameters such as activation energy and reaction order by monitoring the heat effect of the reaction, and then establishes a model. However, DSC is essentially a thermal analysis method, and its signal mainly originates from the heat changes in the chemical reaction process. This makes it difficult to effectively capture the continuous evolution of material physical rheological properties (such as modulus and viscosity) dominated by the formation of three-dimensional networks in the later stages of curing. For casting systems with high solid content, the behavior in the later stages of curing is often dominated by the evolution of physical structure. At this time, the DSC signal is weak, resulting in insufficient accuracy of models based solely on DSC thermal data in predicting the degree of curing and the final state in the later stages.

[0004] Rheological methods offer a superior solution for comprehensively characterizing the curing behavior of such systems. By monitoring parameters such as storage modulus (G') in real time, this method can directly reflect the structural evolution of materials during the transition from a viscous liquid to an elastic solid, making it a powerful tool for characterizing gelation and network curing. Especially in the later stages of curing, where physical structure evolution dominates, rheological methods can provide crucial information that DSC methods cannot obtain. However, in the study of curing kinetics of cast systems, existing rheological studies mostly focus on obtaining local or stage-specific parameters such as gel time and observing viscosity growth, or only establishing constitutive equations describing the behavior of sample points. They have failed to deeply explore the data potential and effectively transform this data into dynamic models that can be used for finite element simulations at the process scale, thus failing to predict the temperature field and degree of curing field inside the component.

[0005] Therefore, there is an urgent need in this field for a new method that can give full play to the advantages of rheology and directly and effectively transform the physical structure evolution information obtained from it into a high-precision prediction model, so as to solve the problem of accurately predicting the curing process and optimizing the process of complex multiphase casting and curing systems. Summary of the Invention

[0006] This invention aims to provide a method for studying the dynamics and predicting the degree of solidification in multiphase casting and curing systems. The core of this method lies in fully leveraging the unique advantages of rheological techniques in capturing the evolution of physical structures, establishing a dynamic model based on rheological properties, and directly applying it to finite element simulation, thereby achieving accurate prediction of the solidification field within the component.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A method for kinetic study and degree of cure prediction of a multiphase casting and curing system, characterized by the following steps:

[0009] S1: Sample preparation: Mix the multiphase casting grout containing solid filler and binder system uniformly according to the predetermined ratio; the binder system contains prepolymer, curing agent, plasticizer and optional catalyst.

[0010] S2: Rheological testing and data acquisition: Take the multiphase casting grout sample and perform isothermal rheological time scans at different constant temperatures. Within the linear viscoelastic region, monitor the change curve of the storage modulus G' over time.

[0011] S3: Kinetic Model Establishment: Based on the change data of the storage modulus G', an autocatalytic reaction kinetic model based on rheological properties is established; preferably, step S3 specifically includes:

[0012] S3.1: Calculate the degree of curing α at different times according to the formula α=(G't-G'0) / (G'∞-G'0), where G'0, G't and G'∞ represent the storage modulus at the initial time, time t and when the reaction is complete, respectively; convert the change of storage modulus G' with time into the change of degree of curing α with time, and analyze the relationship between curing rate dα / dt and degree of curing α.

[0013] S3.2: The relationship between the curing rate dα / dt and the degree of curing α was fitted using an autocatalytic model, and the corresponding apparent activation energy E and kinetic parameters m and n were obtained. The autocatalytic reaction kinetic equation based on rheological properties was established: dα / dt=k*α^m*(1-α)^n, where the reaction rate constant k follows the Arrhenius equation: k=A*exp(-E / RT).

[0014] S4: Finite Element Simulation Prediction: The autocatalytic reaction kinetic model established in step S3 is used as the curing reaction source term and implanted into the finite element simulation software to construct the finite element model of the multiphase casting curing system. The physical parameters of the material are defined, and the corresponding boundary conditions are set. Running this model allows for simulation calculation and obtains the spatial distribution cloud map and evolution history of the temperature field and degree of curing field of the casting system at any time during the curing process. Attached Figure Description

[0015] Figure 1 : Curves showing the change of energy storage modulus G' over time under different isothermal conditions.

[0016] Figure 2 Curve showing the change in degree of curing α over time.

[0017] Figure 3 Temperature field distribution cloud map at different times during the curing process of the casting system obtained by finite element simulation.

[0018] Figure 4 : Cloud map of the degree of curing field distribution at different times during the curing process of the casting system obtained by finite element simulation.

[0019] Figure 5 Predicted curves of the changes in the core temperature and degree of curing of the component over time, obtained from finite element simulation. Detailed Implementation

[0020] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments. The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the invention.

[0021] Example 1: Rheological testing and establishment of an autocatalytic kinetic model

[0022] This embodiment focuses on a multiphase casting grout containing a solid filler and binder system (including prepolymer, curing agent, plasticizer and catalyst).

[0023] Rheological testing: Using a rotational rheometer with a parallel plate fixture, isothermal time-scan tests were performed on the slurry at multiple set constant temperatures (55℃, 60℃, 65℃). The storage modulus G' as a function of time t was monitored and recorded in real time (e.g., Figure 1 (As shown).

[0024] Data transformation and model fitting:

[0025] First, determine the initial plateau value G'0 and the final plateau value G'∞ for each G'-t curve.

[0026] Based on the formula α=(G't-G'0) / (G'∞-G'0), the original data is converted into curves showing the change of the degree of cure α with time t at different temperatures (e.g., Figure 2(As shown). From the conversion-time curve, the conversion slope-conversion curve (dα / dt) at each time point can be obtained. Analysis shows that the relationship between the curing rate dα / dt and the degree of curing α conforms to the characteristics of autocatalysis. Through fitting, the average apparent activation energy of the system in the stated temperature range is obtained as E=75.86 kJ / mol, and the kinetic parameters are obtained as follows: m=0.077, n=0.440, A=2.713×10^3 s⁻¹. Substituting the obtained kinetic parameters into the autocatalytic model: dα / dt=k*α^m*(1-α)^n, the rheological autocatalytic kinetic equation (model M1) for this specific multiphase casting curing system is finally established:

[0027] dα / dt=2.713×10^3*exp(-9109.76 / T)*α^0.077*(1-α)^0.440

[0028] Example 2: Finite Element Prediction of Curing Degree Based on Rheological Model

[0029] In this embodiment, the kinetic model M1 established in Example 1 is applied to the simulation and prediction of the actual curing process.

[0030] The COMSOL Multiphysics simulation software was used. First, a 3D model was created based on the geometry of the actual component (cylinder) and the mold. In the software's material properties module, the mathematical expression of model M1 was defined as a custom reaction rate equation for the curing reaction, and the corresponding thermophysical parameters (density, thermal conductivity, specific heat capacity) were set. The outer surface of the mold was set to convect heat transfer with air to simulate an oven environment; the curing process parameters were set, with the initial mold temperature set to 25℃ and the ambient temperature kept constant at 60℃.

[0031] After running transient analysis and simulation calculations, the three-dimensional temperature field and degree of curing field distribution inside the component at any time during the curing process can be obtained. Figure 3 The temperature field distribution cloud map at different times during the curing process of the cast system is shown, clearly displaying the internal temperature gradient caused by the exothermic reaction. The model reaches approximately 333.15 K after about 150 minutes. In the early stage of curing, heat transfer occurs from the outside to the inside of the system, forming a ring-shaped temperature field, and the reaction is relatively intense. In the middle stage of curing, the internal exothermic reaction balances with the external heat transfer, and the central temperature reaches its peak and fluctuates slightly. In the later stage of curing, the reaction is basically completed, and the system temperature tends to be in equilibrium with the oven environment.

[0032] Figure 4The distribution contour maps of the degree of curing at different times during the curing process of the casting system are shown. The models predict that the degree of curing field gradually progresses from the mold edge towards the center. In the early stages of curing, due to the coupling of heat transfer and reaction, a significant spatial gradient exists in the degree of curing field, with the curing rate at the edges being significantly faster than at the center, resulting in a clear concentric circle distribution in the contour maps. As curing progresses, the gradient gradually decreases, and the system becomes more homogeneous by 72 hours, with a final predicted degree of curing of 0.932.

[0033] Furthermore, predictive curves of temperature and degree of cure over time at key points (geometric center points) inside the component can be extracted, such as... Figure 5 As shown in the figure, this curve fully illustrates the entire process from the initial heating, through the exothermic peak, to the eventual equilibrium, providing accurate data for assessing the state of the latest curing point.

[0034] This invention establishes a curing reaction kinetic model by analyzing rheological data and uses it for finite element simulation, successfully achieving the simulation and prediction of the curing process. The simulated temperature field and degree of curing field distribution (…) Figure 3 , Figure 4 ) and curing process curve ( Figure 5 This provides a direct basis for process optimization. For example, the heating program or mold design can be adjusted based on the predicted curing non-uniformity, thereby effectively improving the performance uniformity and reliability of the final product.

Claims

1. A method for kinetic study and degree of cure prediction of a multiphase casting and curing system, characterized in that, Includes the following steps: S1: Rheological testing and data acquisition: Isothermal rheological testing was conducted on multiphase casting grout to monitor the change curve of its storage modulus G' over time. S2: Kinetic Model Establishment: Based on the change data of the energy storage modulus G', an autocatalytic reaction kinetic model based on rheological properties is established; S3: Curing process simulation: The autocatalytic reaction kinetic model established in step S2 is used as the curing reaction source term and implanted into the finite element simulation software to construct the thermo-chemical coupling model of the multiphase casting curing system, and to simulate and predict the evolution of its temperature field and degree of curing field during the curing process.

2. The method according to claim 1, characterized in that, Step S2 specifically includes: S2.1: According to the formula α=(G't-G'0) / (G'∞-G'0), the change of storage modulus G' with time is converted into the change of degree of solidification α with time, where G'0, G't, and G'∞ are the storage modulus at the initial time, time t, and when the reaction is complete, respectively. S2.2: The relationship between the curing rate dα / dt and the degree of curing α was fitted using an autocatalytic model to obtain a kinetic equation of the form dα / dt=k*α^m*(1-α)^n, where k=A*exp(-E / RT).

3. The method according to claim 2, characterized in that, The apparent activation energy E in the autocatalytic model was obtained by fitting the linear relationship between ln(dα / dt) and 1 / T at different isothermal temperatures.

4. The method according to claim 1, characterized in that, The solid filler is at least one of inorganic particles, metal powder, or organic particles.

5. The method according to claim 1, characterized in that, The adhesive system includes a prepolymer, a curing agent, and an optional catalyst. The prepolymer is at least one of hydroxyl-terminated polybutadiene, epoxy resin, or polyurethane prepolymer.

6. The method according to claim 1, characterized in that, The autocatalytic reaction kinetic equation established using the method described in claim 2 is used as the source term for the curing reaction and is incorporated into the finite element simulation software to simulate and predict the curing process.

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