Epoxy resin thermodynamic property prediction method and device and computer equipment

Abstract

The invention discloses an epoxy resin thermodynamic property prediction method and device and computer equipment, and relates to the technical field of material simulation design. The epoxy resin thermodynamic property prediction method comprises the following steps: constructing an epoxy resin model, carrying out a crosslinking reaction on epoxy resin molecules and curing agent molecules by adopting a crosslinking script to obtain an epoxy resin model, and carrying out geometric optimization and dynamic optimization on the epoxy resin model; the epoxy resin model is evenly divided into a preset number of layers in the preset direction, the outermost layer is set as a hot layer, and the innermost layer is set as a cold layer; setting an environment simulation temperature, exchanging momentum between the coldest particles in the heat layer and the hottest particles in the cold layer, and averaging for multiple times until the epoxy resin model forms a stable temperature gradient; obtaining temperature gradient and energy flux values of the epoxy resin model, and calculating the heat conductivity of the epoxy resin model according to the Fourier heat transfer law. The theoretical prediction of the thermodynamic property of the epoxy resin model is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of material simulation design, and in particular to a method, a device and a computer equipment for predicting the thermodynamic properties of epoxy resin. Background Art

[0002] Epoxy resin is a widely used thermosetting polymer. After chemical reaction and curing, it forms a stable network cross-linked structure, which makes it have excellent corrosion resistance, dimensional stability and considerable mechanical properties, thus it has been widely used in the engineering field. Accurately predicting the thermodynamic properties of epoxy resin under extreme temperatures and complex thermal environments is of great significance for ensuring the safety and reliability of equipment.

[0003] Experimental measurement is the most direct method to obtain the thermodynamic properties of epoxy resins and can provide relatively direct data, but it is often costly, time-consuming and labor-intensive, and the experimental conditions are difficult to fully simulate the complex working conditions in actual applications, resulting in large limitations in the measurement results. However, related research on the simulation and prediction of the thermodynamic properties of epoxy resins is relatively scarce, which cannot meet the needs of various fields for high-performance epoxy resin materials. Summary of the invention

[0004] The method for predicting the thermodynamic properties of epoxy resin provided by the present invention comprises the following steps:

[0005] S1, constructing an epoxy resin model: using a cross-linking script to perform a cross-linking reaction on epoxy resin molecules and curing agent molecules to obtain an epoxy resin model, and performing geometric optimization and dynamic optimization on the epoxy resin model;

[0006] S2, evenly dividing the epoxy resin model into a preset number of layers along a preset direction, and setting the outermost layer as a hot layer and the innermost layer as a cold layer;

[0007] S3, setting the environment simulation temperature, exchanging the momentum between the coldest particle in the hot layer and the hottest particle in the cold layer, and taking an average value for multiple exchanges until a stable temperature gradient is formed in the epoxy resin model;

[0008] S4, obtaining the temperature gradient and energy flux values ​​of the epoxy resin model, and calculating the thermal conductivity of the epoxy resin model according to Fourier's heat transfer law.

[0009] Optionally, the epoxy resin is at least one of bisphenol A epoxy resin, bisphenol F epoxy resin, alicyclic epoxy resin, aliphatic epoxy resin, phenolic epoxy resin, brominated epoxy resin, and hydrogenated bisphenol A epoxy resin.

[0010] Optionally, the epoxy resin is bisphenol A epoxy resin.

[0011] Optionally, the curing agent is at least one of polyamine, acid anhydride, polythiol and imidazole derivatives.

[0012] Optionally, the curing agent is at least one of DDM, 33DDS, PACM, and MDEA.

[0013] Optionally, the ratio of the number of epoxy resin molecules to the number of curing agent molecules in the epoxy resin model is 2:1.

[0014] Optionally, after step S1, the following steps are also included:

[0015] A temperature reduction simulation is performed on the epoxy resin model to obtain the density of the epoxy resin at each set temperature.

[0016] Optionally, the step of performing a cooling simulation on the epoxy resin model to obtain the density of the epoxy resin at each set temperature includes:

[0017] The cooling range was set to 250-600K and the cooling rate was set to 50K / 250ps.

[0018] Optionally, the step of performing a cooling simulation on the epoxy resin model to obtain the density of the epoxy resin at each set temperature further includes:

[0019] At each set temperature, the epoxy resin model was subjected to a molecular dynamics simulation for 100 ps in the NVT ensemble and then to a molecular dynamics simulation for 150 ps in the NPT ensemble.

[0020] Optionally, in step S2, the preset number of layers is 40.

[0021] Optionally, the ambient simulation temperature is not lower than 250K and not higher than 600K.

[0022] The present invention also provides a device for predicting the thermodynamic properties of epoxy resin, comprising:

[0023] Model building module, used to build molecular models;

[0024] A cross-linking script input module is used to set the cross-linking script;

[0025] A cross-linking execution module, used for causing the molecules in the molecular model to undergo a cross-linking reaction according to an input cross-linking script;

[0026] A model optimization module, used for performing geometric optimization and dynamic optimization on the molecular model according to molecular dynamics;

[0027] A division module, used for dividing the molecular model into a preset number of layers, and setting the outermost layer of the molecular model as a hot layer and the innermost layer as a cold layer;

[0028] A momentum exchange module, used for exchanging momentum between the coldest particle in the hot layer and the hottest particle in the cold layer;

[0029] The calculation module is used to calculate the density and thermal conductivity of the molecular model.

[0030] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned method for predicting the thermodynamic properties of epoxy resin when executing the computer program.

[0031] The present invention has the following beneficial effects:

[0032] The method for predicting the thermodynamic properties of epoxy resin proposed in the present invention analyzes the epoxy resin at the microscopic level from the perspective of molecular dynamics, and divides the epoxy resin model into a hot layer and a cold layer by constructing an epoxy resin model that is close to a real cross-linking and curing reaction. The momentum between the coldest particle in the hot layer and the hottest particle in the cold layer is exchanged, which is equivalent to taking away a part of the heat from the hot layer and adding an equal amount of heat to the cold layer at the same time. The heat flow from the hot layer to the cold layer is constructed, and then the thermal conductivity of the epoxy resin model can be calculated by Fourier's heat transfer law, thereby realizing the theoretical prediction of the thermodynamic properties of the epoxy resin model. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0034] Figure 1 It is a flowchart of a method for predicting the thermodynamic properties of epoxy resin in some embodiments of the present invention;

[0035] Figure 2 Schematic diagram of the molecular structure model of bisphenol A epoxy resin in an embodiment of the present invention;

[0036] Figure 3 Schematic diagram of the molecular structure model of four curing agents PACM, DDM, MDEA and 33DDS in the embodiment of the present invention;

[0037] Figure 4 Schematic diagram of the blending model of four curing agents, PACM, DDM, MDEA and 33DDS, respectively with bisphenol A epoxy resin in an embodiment of the present invention;

[0038] Figure 5The thermal conductivity bar graph of the epoxy resin model corresponding to the four curing agents PACM, DDM, MDEA and 33DDS in the embodiment of the present invention at different temperatures;

[0039] Figure 6 It is a density-temperature curve diagram of the epoxy resin model corresponding to the four curing agents PACM, DDM, MDEA, and 33DDS in the embodiment of the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0041] The English abbreviations of the professional terms involved in the present invention are explained as follows: DGEBA: bisphenol A type epoxy resin; DDM: diaminodiphenylmethane; 33DDS: 3,3'-diaminodiphenyl sulfone; MDEA: 4,4'-methylenebis(2,6-diethylaniline); PACM: 4,4'-diaminodicyclohexylmethane.

[0042] In an embodiment of the present invention, the epoxy resin refers to at least one of various epoxy resins such as bisphenol A epoxy resin, bisphenol F epoxy resin, alicyclic epoxy resin, aliphatic epoxy resin, phenolic epoxy resin, brominated epoxy resin, hydrogenated bisphenol A epoxy resin, etc.; the epoxy resin curing agent is at least one of addition polymerization type, catalytic polymerization type, latent type and flame retardant type, among which the addition polymerization curing agent includes polyamines, acid anhydrides, polythiols and imidazole derivatives, etc., and the polyamine curing agent includes aliphatic amines, alicyclic amines, aromatic amines, heterocyclic amines, modified polyamines, low molecular weight polyphthalamides, etc.

[0043] The method for predicting the thermodynamic properties of epoxy resin provided in the embodiment of the present invention comprises the following steps:

[0044] S1, constructing an epoxy resin model: using a cross-linking script to cross-link epoxy resin molecules and curing agent molecules to obtain an epoxy resin model, and then performing geometric optimization and dynamic optimization on the epoxy resin model.

[0045] Specifically, the epoxy resin matrix and the curing agent were modeled according to a monomer molecule number ratio of 2:1. The model was first subjected to 5000 steps of geometric optimization to achieve model minimization, and then 200 ps of kinetic optimization was performed under the NPT ensemble at a temperature of 600 K. Then, 200 ps of kinetic optimization was performed under the NVT ensemble at a temperature of 300 K. The final model was used for subsequent cross-linking.

[0046] The cross-linking script is used to make the epoxy resin matrix and the curing agent undergo a cross-linking reaction. The model after cross-linking needs to be used for subsequent parameter calculations and also needs to be optimized structurally and dynamically to eliminate the internal stress of the system.

[0047] First, the model is geometrically optimized, and then five cycles of annealing with a total time of 140 ps are carried out in the NVT ensemble within the temperature range of 300~650K. After the cyclic annealing, the model with the lowest potential energy is selected to carry out 100 ps NVT and 200 ps NPT kinetic simulations at 600K, and then a 200 ps NVT simulation is carried out at 300K. The final model can be used for the subsequent calculation of the thermodynamic properties of epoxy resin.

[0048] See also Figure 1 The first aspect of the embodiment of the present invention calculates the thermal conductivity of the epoxy resin model, comprising the following steps:

[0049] S2, evenly dividing the epoxy resin model into a preset number of layers along a preset direction, and setting the outermost layer as a hot layer and the innermost layer as a cold layer.

[0050] Among them, when modeling, the c-axis is set to about three times the a-axis and b-axis to solve the thermal conductivity of the epoxy resin.

[0051] S3, setting the ambient simulation temperature, which is not lower than 250K and not higher than 600K; exchanging the momentum between the coldest particle in the hot layer and the hottest particle in the cold layer, and taking the average value after multiple exchanges until a stable temperature gradient is formed in the epoxy resin model.

[0052] S4, obtaining the temperature gradient and energy flux values ​​of the epoxy resin model, and calculating the thermal conductivity of the epoxy resin model according to Fourier's heat transfer law.

[0053] Among them, Fourier's heat transfer law is:

[0054]

[0055] Where λ is the thermal conductivity; J is the energy flux in the z direction; dT / dz is the temperature gradient, and the negative sign indicates that the direction of the energy flux is opposite to the gradient.

[0056] By Exchange energy between 2 fixed layers in the system to apply the flux, namely:

[0057]

[0058] Where A is the area perpendicular to the flux direction and the factor 2 is due to the periodic boundary conditions.

[0059] The method for predicting the thermodynamic properties of epoxy resin proposed in the embodiment of the present invention analyzes the epoxy resin at the microscopic level from the perspective of molecular dynamics. By constructing an epoxy resin model close to the actual cross-linking and curing reaction, the epoxy resin model is divided into a hot layer and a cold layer, and the momentum between the coldest particle in the hot layer and the hottest particle in the cold layer is exchanged, which is equivalent to taking away a part of the heat from the hot layer and adding an equal amount of heat to the cold layer at the same time. The heat flow from the hot layer to the cold layer is constructed, and then the thermal conductivity of the epoxy resin model can be calculated by Fourier's heat transfer law, thereby realizing the theoretical prediction of the thermodynamic properties of the epoxy resin model.

[0060] A second aspect of the embodiment of the present invention calculates the glass transition temperature of the epoxy resin model.

[0061] In the free volume theory, the volume of liquid and solid is divided into free volume V f and occupied volume V o There are two parts. The volume of the molecule itself is the actual occupied volume, and the free volume is the gaps not occupied by the molecules. These gaps are evenly dispersed in the entire system in the form of "holes". It is precisely because of the existence of these gaps that provide space for movement that the molecular chain can adjust its conformation. With the change of temperature, not only the molecular volume will change, but the free volume will also expand and contract. When the temperature rises, the free volume increases, the space for the movement of the polymer chain segments increases, and the movement between molecules is freer, presenting a macroscopic state of liquid. When the temperature drops, the free volume decreases accordingly. When the free volume is reduced to a certain extent, the segments are bound and difficult to move, and the macroscopic state of the substance becomes solid. The freezing of the segment movement leads to the glass transition.

[0062] Therefore, the free volume theory holds that at a certain temperature, when the free volume is reduced to a point where it cannot provide enough space to accommodate the movement of the chain segments, the movement of the molecular chain is frozen, and the value corresponding to this critical temperature is the glass transition temperature T. g .

[0063] T g It is an important thermodynamic performance parameter of polymers, representing the upper limit of the use temperature of thermoplastic materials. The higher the use temperature of thermoplastic polymers, the higher the corresponding glass transition temperature. gMany properties of polymers will change suddenly near T, such as thermal expansion coefficient, elastic modulus, refractive index, etc. g The two ends show completely different physical properties, so to a large extent, the application environment of polymer materials is determined by T g Although many experimental methods can be used to determine the T g However, there is still no unified theory to explain the microscopic mechanism of T g , use simulation to predict T g , which can better study the microscopic changes of polymers as temperature changes.

[0064] Specifically, in the molecular dynamics calculation, the relationship between material density and temperature is obtained by cooling the model, and then the glass transition temperature of the epoxy resin is obtained.

[0065] In some embodiments, a cooling simulation is performed on the epoxy resin model to obtain the density of the epoxy resin at each set temperature, wherein the cooling range can be set to 250-600K, the cooling rate can be set to 50K / 250ps, and 100ps and 150ps molecular dynamics simulations are performed under the NVT ensemble and the NPT ensemble at each temperature, respectively, and several sets of density-temperature relationships are recorded. There is an obvious inflection point in the density-temperature point. With the inflection point as the dividing point, the density-temperature data before and after are linearly fitted, and the intersection of the two fitting curves is the glass transition temperature T g .

[0066] Example 1

[0067] Based on the above embodiments, in this embodiment, the epoxy resin is selected from bisphenol A epoxy resin, and its molecular model is as follows: Figure 2 As shown; the curing agents are DDM, 33DDS, PACM, and MDEA, and the molecular models of the four curing agents are as follows Figure 3 As shown; the monomer molecular model of the epoxy resin and the monomer molecular model of the curing agent are mixed in a quantity ratio of 2:1 to establish a blending model, as shown in Figure 4 As shown, four epoxy resin system control groups were obtained.

[0068] First, the model was minimized by 5000 steps of geometric optimization, and then 200 ps of kinetic optimization was performed under the NPT ensemble at 600 K. Then, 200 ps of kinetic optimization was performed under the NVT ensemble at 300 K. The final model was used for subsequent cross-linking.

[0069] The cross-linking script is used to make the epoxy resin matrix and the curing agent undergo a cross-linking reaction. The model after cross-linking needs to be used for subsequent parameter calculations and also needs to be optimized structurally and dynamically to eliminate the internal stress of the system.

[0070] Specifically, the model was geometrically optimized and then subjected to five cycles of annealing for a total of 140 ps in the NVT ensemble within the temperature range of 300-650 K. After the cyclic annealing, the model with the lowest potential energy was selected to perform 100 ps NVT and 200 ps NPT kinetic simulations at 600 K, followed by 200 ps NVT simulations at 300 K. The final model can be used for subsequent calculations of the thermodynamic properties of epoxy resins.

[0071] The epoxy resin model is evenly divided into 40 layers along the preset direction, and the two outermost layers are set as hot layers and the two innermost layers are set as cold layers.

[0072] Among them, when modeling, the c-axis is set to about three times the a-axis and b-axis to solve the thermal conductivity of the epoxy resin.

[0073] The ambient simulation temperature is set to be not less than 250K and not more than 600K; the momentum between the coldest particle in the hot layer and the hottest particle in the cold layer is exchanged, and the average value is taken after multiple exchanges until a stable temperature gradient is formed in the epoxy resin model.

[0074] The temperature gradient and energy flux values ​​of the epoxy resin model are obtained, and the thermal conductivity of the epoxy resin model is calculated according to Fourier's heat transfer law.

[0075] See also Figure 5 ,Analysis of the calculated results shows that as the temperature of epoxy resin increases, the thermal conductivity also increases, and it is stable after 500K with slight fluctuations; the selection of different curing agents has little effect on the thermal conductivity of epoxy resin; the thermal conductivity under DGEBA / MDEA system is slightly higher than that of the other three curing agent systems.

[0076] In addition, a cooling simulation was performed on the epoxy resin model to obtain the density of the epoxy resin at each set temperature. The cooling range was set to 250~600K, the cooling rate was set to 50K / 250ps, and 100ps and 150ps molecular dynamics simulations were performed under the NVT ensemble and NPT ensemble at each temperature, respectively. A total of 15 sets of density-temperature relationships were recorded; see Figure 6 There is an obvious inflection point in the density-temperature point. Taking this inflection point as the dividing point, the density-temperature data before and after it are linearly fitted. The intersection of the two fitting curves is Tg; the glass transition temperature of pure epoxy resin is between 400~425K. Figure 6 It can be seen that the curing agent can affect the glass transition temperature of epoxy resin. The glass transition temperatures of the four curing agents are MDEA from small to large. <PACM<33DDS<DDM。

[0077] The embodiment of the present invention further provides a device for predicting the thermodynamic properties of epoxy resin, comprising:

[0078] Model building module for building molecular models.

[0079] The cross-link script input module is used to set the cross-link script.

[0080] The cross-linking execution module is used to make the molecules in the molecular model undergo cross-linking reactions according to the input cross-linking script.

[0081] The model optimization module is used to perform geometric optimization and dynamic optimization of molecular models based on molecular dynamics.

[0082] The division module is used to divide the molecular model into a preset number of layers, and set the outermost layer of the molecular model as a hot layer and the innermost layer as a cold layer.

[0083] Momentum exchange module, used to exchange momentum between the coldest particle in the hot layer and the hottest particle in the cold layer.

[0084] Computational module for calculating density and thermal conductivity of molecular models.

[0085] The above modules can be obtained through Material Studio software and run in the application environment of the software. The specific parameters involved are set with reference to the embodiment of the aforementioned method for predicting thermodynamic properties of epoxy resin, which will not be described in detail here.

[0086] An embodiment of the present invention further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method for predicting the thermodynamic properties of epoxy resins provided in the above embodiment is implemented.

[0087] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for predicting the thermodynamic properties of epoxy resin, characterized in that: The following steps are involved: S1, constructing an epoxy resin model: using a cross-linking script to perform a cross-linking reaction on epoxy resin molecules and curing agent molecules to obtain an epoxy resin model, and performing geometric optimization and dynamic optimization on the epoxy resin model; S2, evenly dividing the epoxy resin model into a preset number of layers along a preset direction, and setting the outermost layer as a hot layer and the innermost layer as a cold layer; S3, setting the environment simulation temperature, exchanging the momentum between the coldest particle in the hot layer and the hottest particle in the cold layer, and taking an average value for multiple exchanges until a stable temperature gradient is formed in the epoxy resin model; S4, obtaining the temperature gradient and energy flux values ​​of the epoxy resin model, and calculating the thermal conductivity of the epoxy resin model according to Fourier's heat transfer law.

2. The method for predicting the thermodynamic properties of epoxy resin according to claim 1, characterized in that: The epoxy resin is at least one of bisphenol A epoxy resin, bisphenol F epoxy resin, alicyclic epoxy resin, aliphatic epoxy resin, phenolic epoxy resin, brominated epoxy resin, and hydrogenated bisphenol A epoxy resin; The curing agent is at least one of polyamine, acid anhydride, polythiol and imidazole derivatives.

3. The method for predicting the thermodynamic properties of epoxy resin according to claim 2, characterized in that: The ratio of the number of epoxy resin molecules to the number of curing agent molecules in the epoxy resin model is 2:

1.

4. The method for predicting the thermodynamic properties of epoxy resin according to claim 1, characterized in that: After step S1, the method further includes the following steps: A temperature reduction simulation is performed on the epoxy resin model to obtain the density of the epoxy resin at each set temperature.

5. The method for predicting the thermodynamic properties of epoxy resin according to claim 4, characterized in that: The step of performing a cooling simulation on the epoxy resin model to obtain the density of the epoxy resin at each set temperature includes: The cooling range was set to 250-600K and the cooling rate was set to 50K / 250ps.

6. The method for predicting the thermodynamic properties of epoxy resin according to claim 5, characterized in that: The step of performing a cooling simulation on the epoxy resin model to obtain the density of the epoxy resin at each set temperature also includes: At each set temperature, the epoxy resin model was subjected to a molecular dynamics simulation for 100 ps in the NVT ensemble and then to a molecular dynamics simulation for 150 ps in the NPT ensemble.

7. The method for predicting the thermodynamic properties of epoxy resin according to claim 1, characterized in that: In step S2, the preset number of layers is 40.

8. The method for predicting the thermodynamic properties of epoxy resin according to claim 1, characterized in that: The environmental simulation temperature is not lower than 250K and not higher than 600K.

9. An epoxy resin thermodynamic property prediction device, characterized in that: include: Model building module, used to build molecular models; A cross-linking script input module is used to set the cross-linking script; A cross-linking execution module, used for causing the molecules in the molecular model to undergo a cross-linking reaction according to an input cross-linking script; A model optimization module, used for performing geometric optimization and dynamic optimization on the molecular model according to molecular dynamics; A division module, used for dividing the molecular model into a preset number of layers, and setting the outermost layer of the molecular model as a hot layer and the innermost layer as a cold layer; A momentum exchange module, used for exchanging momentum between the coldest particle in the hot layer and the hottest particle in the cold layer; The calculation module is used to calculate the density and thermal conductivity of the molecular model.

10. A computer device comprising a memory and a processor, characterized in that: The memory stores a computer program, and when the processor executes the computer program, the method for predicting the thermodynamic properties of epoxy resin according to any one of claims 1 to 8 is implemented.

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