Method for analyzing PMVE / CO2 gas conformation evolution and phase change correlation mechanism based on fusion algorithm

Abstract

The invention discloses a PMVE / CO2 gas conformation evolution and phase change correlation mechanism analysis method based on a fusion algorithm, and the method comprises the following steps: 1, building a PMVE / CO2 mixed gas system model, and carrying out the energy minimization and geometric optimization of the system based on a COMPASS II force field, and obtaining an optimized model; 2, carrying out linear cooling molecular dynamics simulation on the optimized model based on an NPT ensemble, and taking 10K as a temperature gradient; step 3, analyzing conformational change and diffusivity change of the PMVE / CO2 system at different temperatures based on a dynamic simulation result, and searching a theoretical phase transition temperature; and 4, constructing a phase change temperature difference coefficient of the dynamic simulation software based on the actual phase change temperatures of PMVE and CO2, and obtaining a reliable phase change prediction model. The invention mainly aims to establish a reliable phase change prediction model based on molecular dynamics, and provides a theoretical basis for researching phase change temperature prediction after PMVE and CO2 are mixed.

Description

Technical Field

[0001] The present invention relates to the field of PMVE / CO2 insulating gas, and more specifically, to an analysis method for the conformational evolution and phase transition correlation mechanism of PMVE / CO2 gas based on a fusion algorithm. Background Art

[0002] PMVE (perfluoromethyl vinyl ether), as a new type of environmentally friendly insulating gas, has received wide attention. This gas has weak toxicity, low GWP and ODP, but has the problem of a relatively high liquefaction temperature. To overcome this shortcoming, it is often necessary to mix it with buffer gases such as CO2 and N2.

[0003] The PMVE / CO2 mixed gas makes up for the defect of low liquefaction temperature of pure PMVE and also provides certain flame retardancy and improves the insulation performance. However, PMVE / CO2 will face various harsh working conditions in engineering applications, and the substance phase transition behavior will inevitably occur. Molecular dynamics, as a widely used molecular simulation technology, can save time and material resources, and explore the phase transition behavior of the molecular system from the perspective of simulation, avoiding the long experimental process.

[0004] However, the simulation results of molecular dynamics have randomness and contingency, and are affected by artificial parameter settings. Therefore, there are inevitably errors in the prediction accuracy. Sometimes the predicted value of the phase transition temperature is very different from the actual value, and the prediction process is difficult. Based on this, there is an urgent need for a phase transition prediction model with higher accuracy and feasibility to correct the phase transition prediction temperature of molecular dynamics simulation and improve the prediction accuracy. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides an analysis method for the conformational evolution and phase transition correlation mechanism of PMVE / CO2 gas based on a fusion algorithm, and proposes a phase transition prediction model based on molecular dynamics simulation and the fusion algorithm, which improves the prediction accuracy.

[0006] To achieve the above object, an analysis method for the conformational evolution and phase transition correlation mechanism of PMVE / CO2 gas based on a fusion algorithm includes the following steps:

[0007] Step 1, establish a system model of the PMVE / CO2 mixed gas, and perform energy minimization and geometric optimization on the system based on the COMPASSⅡ force field to obtain an optimized model;

[0008] Step 2, perform molecular dynamics simulation with linear temperature reduction on the optimized kinetic model based on the NPT ensemble, with a temperature gradient of 10K;

[0009] Step 3: Analyze the conformational changes and the changes in the mean square displacement of the PMVE / CO2 system at different temperatures based on the kinetic simulation results, and find the theoretical phase transition temperature.

[0010] Step 4: Based on the actual phase transition temperature of PMVE and CO2 and the data fusion algorithm, construct the phase transition temperature difference coefficient and a reliable phase transition prediction model for the kinetic simulation software.

[0011] Preferably, the said Step 1 includes: respectively establishing the molecular models of the environmentally friendly insulating gas and the buffer gas in the Materials Studio simulation software and performing geometric optimization; mixing the optimized environmentally friendly insulating gas and buffer gas molecules in proportion to establish a gas mixture box model; performing relaxation and equilibrium optimization on the box model to obtain the optimized kinetic model.

[0012] Preferably, in the said Step 2, the molecular dynamics simulation is carried out in the temperature range of 303K to 113K, with a temperature gradient of 10K, and a 1000ps NPT ensemble cooling simulation is performed on the optimized kinetic model. The simulation step size is 1fs, and a total of 200 frames are collected for analysis.

[0013] Preferably, in the said Step 3, it includes: finding the conformation in which the morphology of the PMVE / CO2 mixed gas changes in the NPT simulation results, and recording the simulation temperature corresponding to this conformation as T c ; performing Mean Square Displacement calculation on the NPT simulation results to obtain the mean square displacement of the mixed gas at a determined temperature, summing and averaging the obtained mean square displacements to get the average mean square displacement Average MSD at this temperature; finding the numerical mutation point of Average MSD in the temperature range of 303K to 113K, and the temperature corresponding to this mutation point is the theoretical temperature at which the phase transition occurs, denoted as T M .

[0014] Preferably, the said Step 4 includes:

[0015] The fusion algorithm is the weighted average method.

[0016] The weighted average method subtracts the actual phase transition temperature T t from the conformational change temperature T c and the theoretical temperature T M of the phase transition respectively, to obtain (T t -T c ), (T t –T M ), assigns different weights according to the accuracy and reliability, adds the weighted data to the conformational change temperature T c to obtain the initial phase transition prediction model, and the calculation formula is as follows:

[0017] T f = K i (T t - T c ) + K j (T t - T M ) + T c

[0018] Among them, T f is the phase transition temperature predicted by the initial model, and K i is the weight of the difference between the conformational change temperature and the actual phase transition temperature, and K j is the weight of the difference between the theoretical phase transition temperature and the actual phase transition temperature.

[0019] The said K i , K j are set as variable parameters. By comparing the difference between the actual phase transition temperature and the predicted temperature T f of the mixed gas under different mixing ratios, the curve fitting method is used to dynamically optimize K i , K j so that the phase transition temperature of the prediction model and the actual phase transition temperature reach the best match, thereby constructing the final phase transition prediction model:

[0020] T e = K e1 (T t - T c ) + K e2 (T t - T M )

[0021] Among them, K e1 , K e2 are the phase transition temperature difference coefficients of the difference between the conformational change temperature and the actual phase transition temperature and the phase transition temperature difference coefficient of the difference between the theoretical phase transition temperature and the actual phase transition temperature respectively, and T e is the phase transition temperature predicted by the model.

[0022] Compared with the prior art, the present invention has the following beneficial effects:

[0023] The present invention provides an analysis method for the conformational evolution and phase transition correlation mechanism of PMVE / CO2 gas based on a fusion algorithm, which studies the conformational evolution and phase transition process of PMVE / CO2 gas under different conditions from molecular simulation. It is not restricted by the instrument precision and the influence of human factors, ensuring the repeatability and accuracy of the simulation. Based on the actual phase transition temperature, the temperature corresponding to the conformational transition, and the temperature corresponding to the sudden change in the Average MSD value, an initial phase transition prediction model is constructed, and a reasonable phase transition prediction model is obtained through optimization by the fusion algorithm. This model makes up for the shortcoming of inaccurate prediction of the phase transition temperature by molecular dynamics and is of great significance for predicting the phase transition temperature of PMVE / CO2 gas with different mixing ratios. The present invention can provide theoretical guidance for analyzing the optimal mixing ratio of PMVE gas and CO2 gas; at the same time, it also lays a foundation for the subsequent exploration of new environmentally friendly insulating gases. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 is the kinetic model of PMVE / CO2 mixed gas. (a) is the box model obtained by mixing PMVE and CO2 with a molar ratio of 4:6, (b) is the box model obtained by mixing PMVE and CO2 with a molar ratio of 5:5, and (c) is the box model obtained by mixing PMVE and CO2 with a molar ratio of 6:4.

[0025] Figure 2 is the conformational change of the gas during the NPT process after mixing PMVE / CO2 with a molar ratio of 5:5. (a) is the conformation at 303K, (b) is the conformation at 253K, (c) is the conformation at 203K, (d) is the conformation at 153K, and (e) is the conformation at 113K.

[0026] Figure 3 is the flow chart for building the phase transition prediction model. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] The following clearly and completely describes the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0028] The present invention proposes an analysis method for the conformational evolution and phase transition correlation mechanism of PMVE / CO2 gas based on a fusion algorithm, including the following steps:

[0029] Step 1, establish a system model of PMVE / CO2 mixed gas, and perform energy minimization and geometric optimization on the system based on the COMPASSⅡ force field to obtain an optimized model;

[0030] Step 2: Perform molecular dynamics simulations with linear cooling on the optimized kinetic model based on the NPT ensemble, with a temperature gradient of 10 K.

[0031] Step 3: Analyze the conformational changes and mean square displacement changes of the PMVE / CO2 system at different temperatures based on the kinetic simulation results to find the theoretical phase transition temperature.

[0032] Step 4: Based on the actual phase transition temperatures of PMVE and CO2 and the data fusion algorithm, construct the phase transition temperature difference coefficient and a reliable phase transition prediction model for the kinetic simulation software.

[0033] Example 1

[0034] Establish a system model for the PMVE / CO2 mixed gas, perform energy minimization and geometric optimization on the system based on the COMPASS II force field to obtain an optimized model.

[0035] Specifically, when implementing, draw the molecular models of PMVE and CO2 in the Materials Studio molecular simulation software, optimize the configurations of PMVE and CO2 based on the DMol 3 module, use the Amorphous Cell module to mix the optimized PMVE molecules and CO2 molecules to obtain a box model of the mixed gas, and perform structural optimization on the box model to obtain a stable molecular dynamics model, as Figure 1 shown.

[0036] More specifically, when optimizing PMVE and CO2 molecules, set coretreatment = All Electron in the Electron window of the DMol3 module for all-electron treatment and DllS size = 6 to construct subspace iterative calculations to accelerate the convergence rate. The models of the mixed gas include three gas mixture models obtained by mixing the optimized PMVE molecules and CO2 molecules in a molar ratio of 4:6, 5:5, and 6:4 using the Amorphous Cell module, as Figure 1 shown.

[0037] Example 2

[0038] Perform molecular dynamics simulations with linear cooling on the optimized kinetic model based on the NPT ensemble, with a temperature gradient of 10 K.

[0039] In specific implementation, under the Forcite module, select the Dynamics task, select Fine for the task quality, and select COMPASSⅡ for the force field. First, perform NPT calculations on the kinetic model at 303K for 1000 ps with a calculation step of 1 fs, and output 200 frames for subsequent analysis. The Velocity Rescale method is used for temperature control, and the Berendsen method is used for pressure control. The electrostatic interaction is calculated using the Particle Mesh Ewald method, and the van der Waals interaction is calculated using the truncation method. After the calculation is completed, copy the last frame of the obtained xtd file as an xsd file. In the Dynamics module, lower the temperature by 10K, change the initial velocity to current, and keep other parameters the same as those in the initial temperature calculation, and perform NPT simulations at 293K. Calculate by lowering the temperature in turn to obtain the NPT calculation results in the temperature range of 303K to 103K.

[0040] Example 3

[0041] Based on the kinetic simulation results, analyze the conformational changes and mean square displacement changes of the PMVE / CO2 system at different temperatures to find the theoretical phase transition temperature.

[0042] In specific implementation, based on the xtd file obtained from the NPT simulation, observe the conformation of the PMVE / CO2 gas during the molecular dynamics process (as Figure 2 shown), and record the simulation temperature when the conformation begins to change as T c . Use the Analysis function in the Forcite module of the Materials Studio software to analyze the mean square displacement (Mean Square Displacement, MSD) of the environmentally friendly mixed insulating gas. Sum and average the obtained mean square displacements to get the average mean square displacement Average MSD at this temperature; find the numerical mutation point of the Average MSD in the temperature range of 303K to 113K. The temperature corresponding to this mutation point is the theoretical temperature at which the phase transition occurs, denoted as T M . As shown in Table 1, it can be seen from the figure that it is 183K, deviating from the original change trend, that is, 193K is the theoretical phase transition temperature.

[0043] Table 1. Change diagram of Average MSD of PMVE / CO2 mixed gas

[0044]

[0045] Example 4

[0046] Based on the actual phase transition temperature of PMVE and CO2 and the data fusion algorithm, construct the phase transition temperature difference coefficient of the kinetic simulation software and a reliable phase transition prediction model.

[0047] During specific implementation, use the weighted average method to calculate the difference between the actual phase transition temperature T t and the conformational change temperature T c and the theoretical temperature T M of phase transition occurrence respectively, obtaining (T t -T c ), (T t –T M ). Assign different weights according to accuracy and reliability, and add the weighted data to the conformational change temperature T c to obtain the initial phase transition prediction model. The calculation formula is as follows:

[0048] T f =K i (T t -T c )+K j (T t -T M )+T c

[0049] where, T f is the phase transition temperature predicted by the initial model, K i is the weight of the difference between the conformational change temperature and the actual phase transition temperature, and K j is the weight of the difference between the theoretical phase transition temperature and the actual phase transition temperature.

[0050] The above K i and K j are set as variable parameters. Compare the above initial model with the true phase transition temperatures of three PMVE / CO2 mixed gases with ratios of 4:6, 5:5, and 6:4 respectively. By comparing the differences between the actual phase transition temperatures and the predicted temperature T f of the mixed gases under different mixing ratios, use the curve fitting method to dynamically optimize K i and K j so that the phase transition temperatures of the prediction model under all mixing ratios are in the best match with the actual phase transition temperatures, thereby constructing the final phase transition prediction model:

[0051] T e =K e1 (T t -T c )+K e2 (T t -T M )

[0052] where, K e1 and K e2They are the phase change temperature difference coefficients of the difference between the conformational change temperature and the actual phase change temperature and the difference between the theoretical phase change temperature and the actual phase change temperature, respectively. T e is the phase change temperature predicted by the model.

[0053] The above content is only an explanation of the structure of the present invention. Those skilled in the art of the present technology can make various modifications or supplements to the described specific structure. As long as they do not deviate from the structure of the invention or exceed the scope defined by this claim book, they should fall within the protection scope of the present invention.

Claims

1. An analytical method for PMVE / CO2 gas conformation evolution and phase change correlation mechanism based on a fusion algorithm, characterized in that: The following steps are involved: Step 1: Establish a system model of PMVE / CO2 mixed gas, minimize the energy and optimize the geometry of the system based on COMPASSⅡ force field to obtain the optimized model; Step 2, based on the NPT ensemble, a molecular dynamics simulation of linear cooling was performed on the optimized dynamic model, with a temperature gradient of 10 K; Step 3, based on the kinetic simulation results, analyze the conformational changes and average mean square displacement changes of the PMVE / CO2 system at different temperatures to find the theoretical phase transition temperature; Step 4, based on the actual phase change temperatures of PMVE and CO2 and the data fusion algorithm, construct the phase change temperature difference coefficient and a reliable phase change prediction model for the kinetic simulation software.

2. The method for analyzing the PMVE / CO2 gas conformation evolution and phase change correlation mechanism based on a fusion algorithm according to claim 1, characterized in that: The step 1 specifically includes: In the Materials Studio simulation software, molecular models of environmentally friendly insulating gas and buffer gas were established and geometric optimization was performed; The optimized environmentally friendly insulating gas and buffer gas molecules are mixed in proportion to establish a gas mixture box model; The box model was relaxed and optimized to obtain the optimized kinetic model.

3. The method for analyzing the PMVE / CO2 gas conformation evolution and phase change correlation mechanism based on a fusion algorithm as described in claim 1, characterized in that: The molecular dynamics simulation in step 2 is performed in the temperature range of 303K to 113K, with a temperature gradient of 10K, and a 1000ps NPT ensemble cooling simulation is performed on the optimized dynamics model.

4. The method for analyzing the PMVE / CO2 gas conformation evolution and phase change correlation mechanism based on a fusion algorithm as described in claim 1, characterized in that: The step 3 specifically includes: Find the conformation in which the PMVE / CO2 mixed gas morphology changes in the NPT simulation results, and record the simulation temperature corresponding to this conformation as T c ; The mean square displacement of the mixed gas at a certain temperature is calculated by the mean square displacement calculation of the NPT simulation results. The mean square displacements are summed and averaged to obtain the average mean square displacement Average MSD at the temperature. Find the numerical mutation point of Average MSD in the temperature range of 303K to 103K. The temperature corresponding to this mutation point is the theoretical temperature at which the phase transition occurs, denoted as T M .

5. The method for analyzing the PMVE / CO2 gas conformation evolution and phase change correlation mechanism based on a fusion algorithm according to claim 4, characterized in that: The step 4 specifically includes: The fusion algorithm is a weighted average method; The weighted average method converts the actual phase transition temperature T t and the conformational change temperature T c and the theoretical temperature T at which the phase transition occurs M Subtract and we get (T t -T c )、(T t –T M ), different weights are assigned according to accuracy and reliability, and the weighted data are compared with the conformational change temperature T c Add together to get the initial phase change prediction model, the calculation formula is as follows: T f =K i (T t -T c )+K j (T t -T M )+T c Among them, T f is the phase transition temperature predicted by the initial model, K i is the weight of the difference between the conformational change temperature and the actual phase transition temperature, K j is the weight of the difference between the theoretical phase transition temperature and the actual phase transition temperature. The K i , K j It is set as a variable parameter, and the actual phase transition temperature of the mixed gas at different mixing ratios is compared with the predicted temperature T f The difference between K i , K j Dynamic optimization is performed to achieve the best match between the phase change temperature of the prediction model and the actual phase change temperature, thereby constructing the final phase change prediction model: T e =K e1 (T t -T c )+K e2 (T t -T M ) Among them, K e1 , K e2 are the phase transition temperature difference coefficient of the difference between the conformational change temperature and the actual phase transition temperature and the phase transition temperature difference coefficient of the difference between the theoretical phase transition temperature and the actual phase transition temperature, T e is the phase transition temperature predicted by the model.

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