A method for predicting hydrophobicity based on reaxff reaction force field simulation

Abstract

The present application belongs to the technical field of performance detection of power equipment outer insulation material, and relates to a hydrophobicity prediction method based on ReaxFF reaction force field simulation, aiming to solve the problem that the hydrophobicity performance of high-voltage bushing sheath silicone rubber only depends on static contact angle, FTIR and other macroscopic tests, and can only be detected afterwards and cannot be predicted in advance. The present application comprises: obtaining silicone rubber core data; constructing a silicone rubber model according to the molecular dynamics method and optimizing it; using the ReaxFF program embedded in LAMMPS to perform reaction molecular dynamics simulation on the cracking process of the silicone rubber; accurately calculating four micro-performance parameters, i.e. mean square displacement, diffusion coefficient, cohesive energy density and free volume fraction; establishing the relationship between microstructure evolution and macroscopic hydrophobicity change, deriving the hydrophobicity prediction formula, and finally realizing accurate prediction of the hydrophobicity of in-service silicone rubber. The present application effectively realizes the quantitative correspondence between the microstructure of silicone rubber and the macroscopic hydrophobicity performance, significantly improves the accuracy and reliability of hydrophobicity prediction, can realize early warning, and is suitable for hydrophobicity prediction of various high-voltage equipment outer insulation materials.

Description

Technical Field

[0001] This invention belongs to the field of performance testing technology for external insulation materials of power equipment, and relates to a method for predicting hydrophobicity based on ReaxFF reactive force field simulation. Background Technology

[0002] Silicone rubber is an insulating material that combines excellent hydrophobicity, high insulation performance, and stable mechanical properties. It performs well in various high-voltage insulation scenarios and is widely used in insulation protection fields such as power grids, power systems, and the electronics industry.

[0003] However, under the complex stress coupling effects of humidity, atmospheric weathering, ultraviolet radiation, and high temperatures, high-temperature vulcanized silicone rubber outer insulation materials are prone to aging phenomena such as surface cracking and hardening embrittlement, leading to critical issues such as reduced hydrophobicity. Reduced hydrophobicity allows moisture to spread and form a film on the insulator surface, creating conductive channels, increasing leakage current, and triggering partial discharge. Continued discharge can cause carbonization and corrosion on the insulator surface, ultimately leading to insulation breakdown and flashover accidents, resulting in significant economic losses to the power system. Therefore, a simulateable and quantifiable method for predicting hydrophobicity is needed to provide a basis for predicting the hydrophobicity of silicone rubber surfaces on high-voltage bushing sheaths.

[0004] Currently, the hydrophobicity testing of silicone rubber for high-voltage bushing sheaths still largely relies on traditional macroscopic offline detection methods such as static contact angle and Fourier transform infrared spectroscopy (FTIR). These methods are all post-hoc detections and cannot predict the trend of hydrophobicity changes in the early stages of aging. While ReaxFF reaction molecular dynamics simulations have been used to study the degradation mechanism of silicone rubber, existing simulations have not yet established a quantitative mapping model between microscopic characteristic parameters such as mean square displacement, diffusion coefficient, cohesive energy density, and free volume fraction and macroscopic hydrophobicity indices. Based on the above requirements and considerations, this invention proposes a hydrophobicity prediction method based on ReaxFF reactive force field simulation. Gradient heating is introduced into the ReaxFF simulation to simulate a high-temperature, electric field coupling environment, realistically reproducing the actual service conditions of the bushing sheath and improving the accuracy of the prediction. The simulation results at the picosecond to nanosecond level are extrapolated using the established hydrophobicity prediction formula, achieving a quantitative mapping from short-timescale simulation data to long-timescale hydrophobicity aging and degradation patterns. This enables accurate prediction of the hydrophobic properties of silicone rubber in high-voltage bushing sheaths, providing early warning of insulation failure and ensuring the long-term reliable operation of transmission equipment. Furthermore, the method is universal and can be extended to predict the hydrophobicity of external insulation materials for other high-voltage equipment. Summary of the Invention

[0005] The purpose of this invention is to provide a hydrophobicity prediction method based on ReaxFF reactive force field simulation, which can realize the direct output of hydrophobicity trend from molecular simulation input, and achieve accurate prediction of hydrophobicity change trend and remaining lifetime.

[0006] To achieve the above objectives, the technical solution provided by the present invention is as follows:

[0007] S1. Obtain structural and material properties of silicone rubber for high-voltage bushing sheaths: Conduct basic performance tests on the target silicone rubber for high-voltage bushing sheaths, obtain core parameters such as density, glass transition temperature, and relative permittivity, and clarify the matrix composition, crosslinking structure, and filler information.

[0008] S2. Constructing a molecular dynamics model of silicone rubber based on molecular dynamics methods: Based on molecular dynamics methods, a molecular model is constructed according to the information of silicone rubber matrix components and cross-linking structure. Geometric optimization, cyclic annealing and molecular dynamics relaxation calculations under NPT ensemble conditions are carried out in sequence to obtain a structurally stable silicone rubber simulation system.

[0009] S3. Verify the effectiveness of the established molecular model: Calculate key parameters such as density, glass transition temperature, and relative permittivity of the optimized silicone rubber molecular model. Compare the simulation results with experimental test data. If the deviation is within a reasonable range, the model is deemed effective; otherwise, return to S2 to re-optimize and reconstruct the model.

[0010] S4. Molecular dynamics simulation of silicone rubber aging and cracking based on ReaxFF reaction force field: Using the ReaxFF program embedded in LAMMPS, the molecular dynamics simulation of the aging and cracking process of silicone rubber is performed. The preset ensemble, unit, atom type, boundary conditions, reaction force field, heating range, simulation duration and reaction step size are set, and the cracking reaction state of the system is output in real time.

[0011] S5. Calculate microscopic performance parameters using the effective molecular model after pyrolysis simulation: Based on molecular dynamics methods, calculate four microscopic performance parameters for the aged and pyrolyzed model: mean square displacement, diffusion coefficient, cohesive energy density, and free volume fraction.

[0012] S6. Establish quantitative formulas for predicting microscopic and macroscopic hydrophobic properties: Fit the microscopic performance parameters obtained in S5 with preset reference values ​​and silicone rubber service parameters to establish quantitative formulas for macroscopic hydrophobic properties and microscopic parameters. The hydrophobicity of silicone rubber can be predicted through this model.

[0013] Preferably, in step S2, the construction of the molecular model involves first constructing a single-chain structure of methyl vinyl silicone rubber; then establishing an amorphous model of two siloxane chains, using the side chain vinyl as the active crosslinking site, and achieving vulcanization crosslinking through CC bonding; and selecting the conformation with the lowest system energy as the initial structure for simulation.

[0014] Preferably, in step S2, the optimization of the molecular model involves allocating force field and charge parameters to the established silicone rubber model and performing geometric optimization calculations. The cyclic annealing simulation uses the NVT ensemble, with a simulation duration of 2000 ps and a temperature control range of 300 K to 600 K. The molecular dynamics relaxation calculation uses 298 K and the NPT ensemble condition, with the simulation step count set to 10,000 steps.

[0015] Preferably, in step S4, the molecular dynamics simulation of the ReaxFF reaction involves converting the established silicone rubber model into a data file required for reaction dynamics, using an NVT ensemble, setting the reaction system unit to real, the atom type to charge, the boundary conditions to periodicity, the reaction force field to CHONSSi.ff, the heating range to 300K-5000K, the simulation duration to 200ps, the reaction step size to 0.1fs, and outputting the current cracking reaction state of the system every 100 steps.

[0016] Preferably, the calculation of each microscopic performance parameter in step S5 is carried out as follows:

[0017] S51. Mean square displacement: The trajectory data of the last 30 ps of the total simulation time after the model system has fully relaxed and reached thermodynamic stability at 300 K is used for calculation and fitting, and the average value is taken as the mean square displacement value.

[0018] S52. Diffusion coefficient: obtained by linear fitting of the mean square displacement curve;

[0019] S53. Cohesive energy density: Calculated using molecular dynamics at 300K.

[0020] S54. Free volume fraction: The free volume distribution is calculated with fine precision using a probe with a diameter of 1.0 Å.

[0021] Preferably, the formula for calculating the quantified hydrophobicity in step S6 is:

[0022]

[0023] In the formula, Y=0% corresponds to the initial intact state of the hydrophobic properties of the unaged silicone rubber, and Y=100% corresponds to the state where the hydrophobic properties of the silicone rubber have degraded to the critical failure level; MSD represents the mean square displacement, in Å. 2MSD0 represents the mean square displacement reference value, in Å. 2 D represents the diffusion coefficient, in Å. 2 / ps; D0 represents the reference value for the diffusion coefficient, in Å. 2 / ps; FFV represents free volume fraction, in %; FFV0 represents the reference value for free volume fraction, in %; t represents service time, in h; t0 represents the reference value for service time, in h; CED represents cohesive energy density, in J / cm³. 3 CED0 represents the cohesive energy density reference value, in J / cm³. 3 T represents the service temperature in K; T0 represents the reference service temperature in K.

[0024] Preferably, the high-voltage bushing sheath silicone rubber is methyl vinyl silicone rubber, with a main chain of polydimethylsiloxane and a small amount of vinyl in the side chain. The crosslinking method is C-C covalent crosslinking, and aluminum hydroxide is used as the main filler in the system.

[0025] The hydrophobicity prediction method based on ReaxFF reactive force field simulation of the present invention has the following advantages:

[0026] This invention constructs a precise molecular model of silicone rubber based on molecular dynamics methods and conducts reaction molecular dynamics simulations of the silicone rubber thermal decomposition aging process using a ReaxFF reactive force field. This enables a direct, integrated output of the material's hydrophobicity evolution law from molecular simulation parameter input. The invention establishes a quantitative correlation mapping model between the microscopic characteristic parameters of molecular simulation and macroscopic hydrophobicity evaluation indicators, establishing a quantitative link between microscopic structural evolution and macroscopic service performance degradation. This allows for accurate prediction of the hydrophobicity change trend of silicone rubber, quantitative testing of the degree of material aging damage, and effective prediction of the remaining service life of insulating materials. Compared to traditional accelerated aging testing methods, this invention eliminates the need for large-scale sample preparation and long-term accelerated aging tests. The detection and analysis process is efficient, rapid, and non-destructive throughout, forming a system for advanced prediction of silicone rubber hydrophobicity. This effectively solves the industry pain point that traditional testing methods can only achieve post-aging detection and cannot provide early warning of performance degradation, providing efficient technical support for the life management and safe operation and maintenance of high-voltage insulating silicone rubber materials. Attached Figure Description

[0027] Figure 1 This is a schematic diagram of the overall process of the present invention.

[0028] Figure 2 This is a schematic diagram of the molecular dynamics modeling analysis in this invention.

[0029] Figure 3 This is a molecular model diagram of the silicone rubber for the casing sheath.

[0030] Figure 4 This is a diagram illustrating the pyrolysis process of the silicone rubber sheathing in the casing. Detailed Implementation

[0031] The technical solutions of the present invention will now be described clearly and in detail with reference to the accompanying drawings. In the description of the embodiments of the present invention, unless otherwise stated, " / " indicates "or," for example, A / B can mean A or B. "And / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, in the description of the embodiments of the present invention, "multiple" refers to two or more. The terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature.

[0032] like Figure 1 , Figure 2 As shown, this invention provides a method for predicting hydrophobicity based on ReaxFF reactive force field molecular simulation, the method comprising the following steps:

[0033] S1. Obtain the structural and material properties of the silicone rubber sheath for high-voltage bushings.

[0034] Basic performance tests were conducted on the target high-voltage bushing sheath silicone rubber to obtain core parameters such as density, glass transition temperature, and relative permittivity, and to clarify the matrix composition, crosslinking structure, and filler information.

[0035] S2. Construct a molecular dynamics model of silicone rubber based on molecular dynamics methods.

[0036] Based on molecular dynamics methods, a molecular model was built according to the composition and cross-linking structure information of the silicone rubber matrix. Geometric optimization, cyclic annealing and molecular dynamics relaxation calculations under NPT ensemble conditions were carried out in sequence to obtain a structurally stable silicone rubber simulation system.

[0037] First, a single-chain structure of methyl vinyl silicone rubber was constructed; then, an amorphous model of two siloxane chains was established, and vulcanization crosslinking was achieved through CC bonding with the side chain vinyl as the active crosslinking site; the conformation with the lowest energy of the system was selected as the initial structure for simulation.

[0038] The process involved allocating force field and charge parameters to the established silicone rubber model and performing geometric optimization calculations. The cyclic annealing simulation used the NVT ensemble with a simulation duration of 2000 ps and a temperature control range of 300 K to 600 K. The molecular dynamics relaxation calculation used 298 K and the NPT ensemble, with a simulation step count of 10,000.

[0039] S3. Verify the effectiveness of the established molecular model.

[0040] The optimized silicone rubber molecular model is used to calculate key parameters such as density, glass transition temperature, and relative permittivity. The simulation results are compared with experimental test data. If the deviation is within a reasonable range, the model is deemed valid; otherwise, it is returned to S2 for re-optimization and reconstruction.

[0041] S4. Molecular dynamics simulation of silicone rubber aging and cracking based on ReaxFF reactive force field

[0042] Using the ReaxFF program embedded in LAMMPS, the aging and cracking process of silicone rubber is simulated by reaction molecular dynamics. Preset ensemble, unit, atom type, boundary conditions, reaction force field, heating range, simulation duration and reaction step size are set, and the cracking reaction state of the system is output in real time.

[0043] The process involves converting the established silicone rubber model into a data file required for reaction kinetics. The NVT ensemble is used, with the reaction system unit set to real, the atom type set to charge, the boundary conditions set to periodicity, the reaction force field selected as CHONSSi.ff, the heating range set to 300K-5000K, the simulation duration set to 200ps, the reaction step size set to 0.1fs, and the current cracking reaction state of the system output every 100 steps.

[0044] S5. Calculate microscopic performance parameters using the effective molecular model derived from the fragmentation simulation.

[0045] S51. Mean square displacement: The trajectory data of the last 30 ps of the total simulation time after the model system has fully relaxed and reached thermodynamic stability at 300 K is used for calculation and fitting, and the average value is taken as the mean square displacement value.

[0046] S52. Diffusion coefficient: obtained by linear fitting of the mean square displacement curve;

[0047] S53. Cohesive energy density: Calculated using molecular dynamics at 300K.

[0048] S54. Free volume fraction: The free volume distribution is calculated with fine precision using a probe with a diameter of 1.0 Å.

[0049] S6. Establish quantitative prediction formulas for hydrophobic properties at both micro and macro levels.

[0050] The microscopic performance parameters obtained from S5 are fitted with preset reference values ​​and silicone rubber service parameters to establish a quantitative prediction formula for macroscopic hydrophobic properties and microscopic parameters. This model is used to predict the hydrophobicity of silicone rubber.

[0051] Example 1

[0052] A 1000kV GIS high-temperature vulcanized silicone rubber composite bushing in a certain region was selected, and the hydrophobicity prediction method proposed in this invention was applied, as follows:

[0053] 1) Obtain the structural parameters and material properties of the silicone rubber for high-voltage bushing sheaths, as shown in Table 1 below.

[0054] Table 1 shows the core parameter values ​​for silicone rubber.

[0055]

[0056] 2) Construct a molecular dynamics model of silicone rubber based on the core parameters of silicone rubber.

[0057] like Figure 3 As shown, a molecular dynamics model of silicone rubber was constructed based on molecular dynamics methods, according to the obtained structural parameters, material property parameters, and crosslinking methods. The initial model was then subjected to geometric optimization, cyclic annealing, and ensemble dynamic relaxation to obtain a stable and usable simulation system.

[0058] 3) Model validity verification

[0059] Based on molecular dynamics methods, the density, glass transition temperature, and relative permittivity of the established model were calculated and compared with actual test values ​​to verify the model's effectiveness. Specific comparison data are shown in Table 2 below. The error range between the parameters of the constructed silicone rubber model and the field test results is small, and the deviations are all within a reasonable range, verifying the model's effectiveness and its applicability for subsequent hydrophobic aging simulation.

[0060] Table 2 shows a comparison of the parameters of the silicone rubber model.

[0061]

[0062] 4) Molecular dynamics aging and pyrolysis simulation based on ReaxFF reactive force field

[0063] The ReaxFF program embedded in LAMMPS was used to simulate the reaction molecular dynamics of silicone rubber pyrolysis. The established silicone rubber model was converted into a data file required for reaction dynamics. During the calculation, the NVT ensemble was used, the reaction system unit was set to real, the atom type to charge, the boundary conditions to periodicity, the reaction force field was selected as CHONSSi.ff, and the temperature range was set to 300K-5000K. During the pyrolysis process, simulations were performed on the model for different durations from ps to ns, with a simulation duration of 200 ps and a reaction step size of 0.1 fs. The current pyrolysis reaction state of the system was output every 100 steps, accurately capturing the microscopic changes during the aging and pyrolysis process of silicone rubber. The silicone rubber pyrolysis process is as follows: Figure 4 As shown.

[0064] 5) Calculate microscopic performance parameters using the effective molecular model obtained from the pyrolysis simulation.

[0065] Based on molecular dynamics methods, four microscopic performance parameters were calculated for the model after aging and pyrolysis. The specific results are as follows:

[0066] ① Mean square displacement: The trajectory data of the last 30 ps of the total simulation time after the model system has fully relaxed and reached thermodynamic stability at 300 K were used for calculation and fitting, and the average value was taken, which yielded a value of 48.97 Å. 2 ② Diffusion coefficient: Linear fitting of the mean square displacement curve yielded a diffusion coefficient D of 0.0081 Ų / ps; ③ Cohesive energy density: The cohesive energy density of the silicone rubber system was calculated to be 146.89 J / cm³ at 300 K; ④ Free volume fraction: Using a probe with a diameter of 1.0 Å, the free volume distribution was calculated with fine precision, yielding a free volume fraction of 25.84%.

[0067] 6) Establish a quantitative correlation formula for lifetime prediction between micro and macro perspectives.

[0068] The above microscopic performance parameters, silicone rubber service parameters, and preset reference values ​​are summarized in Table 3 below.

[0069] Table 3 shows the statistical data.

[0070]

[0071] Substituting the above data into the quantitative hydrophobicity prediction formula of this invention, the aging degradation degree of the hydrophobicity of the silicone rubber was calculated to be Y≈78.84%. This result is in high agreement with the hydrophobicity level of the measured sample, verifying the rationality and reliability of the evaluation method of this invention.

[0072] It is understood that this invention has been described through some embodiments, and those skilled in the art will recognize that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of this invention. Furthermore, under the teachings of this invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of this invention. Therefore, this invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this invention are within the protection scope of this invention.

Claims

1. A method for predicting hydrophobicity based on ReaxFF reactive force field molecular simulation, characterized in that, The method includes the following steps: S1. Obtain structural and material properties of silicone rubber for high-voltage bushing sheaths: Conduct basic performance tests on the target silicone rubber for high-voltage bushing sheaths, obtain core parameters such as density, glass transition temperature, and relative permittivity, and clarify the matrix composition, crosslinking structure, and filler information. S2. Constructing a molecular dynamics model of silicone rubber based on molecular dynamics methods: Based on molecular dynamics methods, a molecular model is constructed according to the information of silicone rubber matrix components and cross-linking structure. Geometric optimization, cyclic annealing and molecular dynamics relaxation calculations under NPT ensemble conditions are carried out in sequence to obtain a structurally stable silicone rubber simulation system. S3. Verify the effectiveness of the established molecular model: Calculate key parameters such as density, glass transition temperature, and relative permittivity of the optimized silicone rubber molecular model. Compare the simulation results with experimental test data. If the deviation is within a reasonable range, the model is deemed effective; otherwise, return to S2 to re-optimize and reconstruct the model. S4. Molecular dynamics simulation of silicone rubber aging and cracking based on ReaxFF reaction force field: Using the ReaxFF program embedded in LAMMPS, the molecular dynamics simulation of the aging and cracking process of silicone rubber is performed. The preset ensemble, unit, atom type, boundary conditions, reaction force field, heating range, simulation duration and reaction step size are set, and the cracking reaction state of the system is output in real time. S5. Calculate microscopic performance parameters using the effective molecular model after pyrolysis simulation: Based on molecular dynamics methods, calculate four microscopic performance parameters for the aged and pyrolyzed model: mean square displacement, diffusion coefficient, cohesive energy density, and free volume fraction. S6. Establish quantitative formulas for predicting microscopic and macroscopic hydrophobic properties: Fit the microscopic performance parameters obtained in S5 with preset reference values ​​and silicone rubber service parameters to establish quantitative formulas for macroscopic hydrophobic properties and microscopic parameters. The hydrophobicity of silicone rubber can be predicted through this model.

2. The hydrophobicity prediction method based on ReaxFF reactive force field molecular simulation according to claim 1, wherein the construction of the molecular model in step S2 is characterized in that, First, a single-chain structure of methyl vinyl silicone rubber was constructed; then, an amorphous model of two siloxane chains was established, and vulcanization crosslinking was achieved through CC bonding with the side chain vinyl as the active crosslinking site; the conformation with the lowest energy of the system was selected as the initial structure for simulation.

3. The hydrophobicity prediction method based on ReaxFF reactive force field molecular simulation according to claim 1, wherein the optimization processing of the molecular model in step S2 is characterized in that, Force field and charge parameters were assigned to the established silicone rubber model, and geometric optimization calculations were performed. Cyclic annealing simulation was performed using the NVT ensemble with a simulation duration of 2000 ps and a temperature control range of 300 K to 600 K. Molecular dynamics relaxation calculation was performed using the 298 K NPT ensemble condition with a simulation step count of 10,000.

4. The hydrophobicity prediction method based on ReaxFF reactive force field molecular simulation according to claim 1, wherein the ReaxFF reactive molecular dynamics simulation in step S4 is characterized in that, The established silicone rubber model was converted into a data file required for reaction kinetics. The NVT ensemble was used, the reaction system unit was set to real, the atom type was set to charge, the boundary conditions were set to periodicity, the reaction force field was selected as CHONSSi.ff, the heating range was set to 300K-5000K, the simulation duration was 200ps, the reaction step size was set to 0.1fs, and the current cracking reaction state of the system was output every 100 steps.

5. The hydrophobicity prediction method based on ReaxFF reactive force field molecular simulation according to claim 1, characterized in that, The calculation of each microscopic performance parameter in step S5 includes the following steps: S51. Mean square displacement: The trajectory data of the last 30 ps of the total simulation time after the model system has fully relaxed and reached thermodynamic stability at 300 K is used for calculation and fitting, and the average value is taken as the mean square displacement value. S52. Diffusion coefficient: obtained by linear fitting of the mean square displacement curve; S53. Cohesive energy density: Calculated using molecular dynamics at 300K. S54. Free volume fraction: The free volume distribution is calculated with fine precision using a probe with a diameter of 1.0 Å.

6. The hydrophobicity prediction method based on ReaxFF reactive force field molecular simulation according to claim 1, characterized in that, The formula for predicting the hydrophobic properties in step S6 is as follows: In the formula, Y=0% corresponds to the initial intact state of the hydrophobic properties of the unaged silicone rubber, and Y=100% corresponds to the state where the hydrophobic properties of the silicone rubber have degraded to the critical failure level; MSD represents the mean square displacement, in Å. 2 ; MSD0 represents the mean square displacement reference value, in Å. 2 D represents the diffusion coefficient, in Å. 2 / ps; D0 represents the reference value for the diffusion coefficient, in Å. 2 / ps; FFV represents free volume fraction, in %; FFV0 represents the reference value for free volume fraction, in %; t represents service time, in h; t0 represents the reference value for service time, in h; CED represents cohesive energy density, in J / cm³. 3 CED0 represents the cohesive energy density reference value, in J / cm³. 3 T represents the service temperature in K; T0 represents the reference service temperature in K.

7. The hydrophobicity prediction method based on ReaxFF reactive force field molecular simulation according to claim 1, characterized in that, The high-voltage bushing sheath silicone rubber is methyl vinyl silicone rubber, with a main chain of polydimethylsiloxane and a small amount of vinyl in the side chain. The crosslinking method is C-C covalent crosslinking, and aluminum hydroxide is used as the main filler in the system.

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