JP2014110000A - Device for preparing filler-filled polymer model, method thereof and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To produce, from image data of a filler-filled polymer, a model with excellent reproducibility of a radial distribution function of the filler.SOLUTION: A method includes: obtaining image data of a filler-filled polymer (S1); obtaining maximum diameter distribution of the filler from the image data (S2); obtaining a size of the filler having the maximum diameter as a diameter thereof, and calculating a density of the filler in the filler-filled polymer (S3); preparing and producing a system including particles having the maximum diameter distribution referring to the density (S5); performing molecular dynamics calculation to and balancing the produced system to obtain position coordinates of the particles at the balanced state (S6); defining the position coordinates of the particles at the balanced state as position coordinates of the filler (S7); arranging a filler model at the position coordinates of the filler and arranging a polymer model in a void to produce a filler-filled polymer model (S8).

Description

  The present invention relates to a creation apparatus for creating a filler-filled polymer model used when analyzing the characteristics of a polymer filled with a filler by molecular dynamics simulation, a method thereof, and a program thereof.

  Polymers such as rubber are generally blended with fillers such as carbon black and silica, and are widely used for various rubber products such as tires. In order to evaluate various properties such as physical properties and deformation behavior of such filler-filled polymers, numerical analysis by simulation using a computer has been performed.

  Conventionally, as numerical analysis of a filler-filled polymer, for example, as disclosed in Patent Document 1, analysis by a finite element method in which a filler and a polymer are divided into a plurality of finite elements is generally used.

  On the other hand, there is molecular dynamics simulation as a technique for analyzing the structure and characteristics of materials at the atomic and molecular level. Molecular dynamics simulation is a method of statistically examining the behavior of particles moving according to Newton's equation of motion by calculating the force acting on each particle constituting the material. For example, Patent Document 2 proposes using molecular dynamics simulation in order to analyze the structure and performance of a molecule or molecular assembly with high accuracy. However, analysis at the atomic and molecular level is difficult to apply to large systems such as filler-filled polymers. Therefore, it is conceivable to use a coarse-grained molecular dynamics method represented by the Beesspring model. In Patent Document 2, if the atom is paraphrased as coarse-grained particles (beads) and the physical property value given as the initial condition is set like the radial distribution function, the interparticle potential that reproduces the given radial distribution function However, there is no concept of individual particle size. On the other hand, since the filler filled in the polymer has a particle size distribution and its arrangement is reflected in the radial distribution function, it is necessary to create a model in which both are considered and reproduced simultaneously.

JP 2007-271369 A JP 2002-189978 A

  When analyzing the filler-filled polymer by molecular dynamics simulation, in order to increase the analysis accuracy, it is required to reproduce the distribution state of the filler filled in the polymer as faithfully as possible. That is, it is difficult to obtain high analytical accuracy by simply creating a filler-filled polymer model based on the particle size of the filler to be blended and the blending amount without considering the state after mixing. On the other hand, if a model that reproduces the dispersion state of the filler, in particular, the radial distribution function, can be obtained from the image data of the filler-filled polymer, more accurate molecular dynamics simulation is possible.

  In view of the above points, the present invention is a filler-filled polymer model creation apparatus, method, and method capable of creating a model excellent in reproducibility of the radial distribution function of the filler from image data of the filler-filled polymer. The purpose is to provide the program.

  The filler-filled polymer model creation device according to the present invention is a creation device that creates a filler-filled polymer model for analyzing the characteristics of a polymer filled with a filler by molecular dynamics simulation; An input unit to obtain; a distribution calculation unit for obtaining a distribution of the maximum diameter of the filler from the image data; and a density calculation for calculating a filler density in the filler-filled polymer by calculating a filler size using the maximum diameter as a diameter. A system creation unit that creates a system in which particles having a distribution of the maximum diameter are included at a ratio calculated based on the density; and by equilibrating the created system by molecular dynamics calculation, A molecular dynamics calculator that calculates the position coordinates of particles in equilibrium; and the position coordinates of particles in equilibrium A filler position acquisition unit that acquires as coordinates; a model creation unit that places a filler model at the filler position coordinates and creates a filler-filled polymer model by placing a polymer model around the filler model; It is.

  According to the present invention, the position coordinate of the filler is obtained by molecular dynamics calculation using the system containing the filler according to the distribution of the maximum diameter, so that the radial distribution function of the filler in the filler-filled polymer is reproduced. Can increase the sex.

It is a block diagram of the creation apparatus concerning a 1st embodiment. It is a flowchart of the production apparatus which concerns on 1st Embodiment. It is a figure of the image data of a filler filling polymer. It is a figure for demonstrating the maximum diameter of a filler. It is a figure which shows the distribution data of the largest diameter of the filler by a distribution calculation part. It is a figure which shows the position coordinate of the particle | grains obtained by the molecular dynamics calculation part. It is a figure in the state before the polymer arrangement | positioning of the filler filling polymer model created by the model creation part. It is a figure which shows an example of the filler filling polymer model created by the model creation part. It is a block diagram of the production apparatus concerning a 2nd embodiment. It is a flowchart of the production apparatus which concerns on 2nd Embodiment. It is a figure which shows the gravity center coordinate data of the filler by a gravity center coordinate calculation part. It is a graph which shows the fitting result with respect to distribution of the maximum diameter. It is a figure which shows the distribution data of the largest diameter produced according to the distribution function. It is a graph which shows the radial distribution function obtained in the Example. 6 is a graph showing a radial distribution function obtained in Comparative Example 1. 6 is a graph showing a radial distribution function obtained in Comparative Example 2. 10 is a graph showing a radial distribution function obtained in Comparative Example 3.

  Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[First Embodiment]
As shown in FIG. 1, the filler-filled polymer model creation device 10 according to the embodiment includes an input unit 12, a distribution calculation unit 14, a density calculation unit 16, a diameter ratio calculation unit 18, a system creation unit 20, and a molecular dynamics calculation unit. 22, a filler position acquisition unit 24, a model creation unit 26, and an output unit 28.

  Note that the creation device 10 can also be realized by using, for example, a general-purpose computer having a mouse and a keyboard as basic hardware. That is, the input unit 12, the distribution calculation unit 14, the density calculation unit 16, the diameter ratio calculation unit 18, the system creation unit 20, the molecular dynamics calculation unit 22, the filler position acquisition unit 24, the model creation unit 26, and the output unit 28 It can be realized by causing a processor mounted on the computer to execute a program. At this time, the creation apparatus 10 may be realized by installing the above program in a computer in advance, or may be stored in a storage medium such as a CD-ROM, or distributed through the network. This program may be realized by appropriately installing it on a computer.

  Hereinafter, the configuration and functions of the above-described units will be described in order.

(1) Input unit 12
The input unit 12 acquires image data of a target filler-filled polymer. The image data may be two-dimensional data or three-dimensional data. Examples of the two-dimensional data include an image obtained with a scanning electron microscope (SEM). Examples of the three-dimensional data include an image obtained with a three-dimensional transmission electron microscope (three-dimensional TEM), and a two-dimensional data. One example is image data obtained by stacking images at a predetermined interval to make them three-dimensional. Such image data may be binarized after input in order to distinguish between filler and polymer, or alternatively, a binarized image binarized in advance may be input.

  Examples of the filler include various fillers blended in polymers such as rubber and resin. For example, in addition to inorganic fillers such as carbon black, silica, talc, and clay, organic fillers may be used. Also, the polymer is not particularly limited. For example, in addition to diene rubbers such as natural rubber, butadiene rubber, styrene butadiene rubber, and chloroprene rubber, various rubbers such as silicone rubber, fluorine rubber, and urethane rubber, polyethylene And various resins including plastics such as polypropylene, polyester and polyamide. Among these, it is preferable to target rubber reinforced with a reinforcing filler such as carbon black or silica, that is, filler-filled rubber. In addition to the filler, the filler-filled polymer may contain various additives such as a softener such as oil, a plasticizer, an anti-aging agent, a vulcanizing agent, and a vulcanization accelerator.

  FIG. 3 shows a binarized image of a rubber composition filled with carbon black as an example of input image data, and the black portion shows carbon black as a filler. This image is two-dimensional image data having an area of (x * y). “X * y” represents the product of x and y (hereinafter, the same applies to cases other than x and y).

(2) Distribution calculation unit 14
The distribution calculation unit 14 obtains the distribution of the maximum diameter of the filler from the two-dimensional or three-dimensional image data obtained above. Specifically, the primary particles of the filler or the aggregates thereof are regarded as one constituent, that is, particles by statistical processing of the image, the maximum diameter of all the particles included in the image is calculated, and the distribution is obtained. Such image processing can be performed using general image processing software.

  The maximum diameter of a particle means the distance between the two points at which the distance between the two points is maximum for any two points in the particle P, as indicated by a symbol R in FIG.

FIG. 5 is a table showing an example of the distribution of the maximum diameter obtained by the distribution calculation unit 14 from the two-dimensional image data shown in FIG. 3, and each particle (index: i = 1) included in the image. ˜N) includes the maximum diameter (R _i ), its radius (r _i ), the area of the circle with radius r _i (S2 _i ), and the area (S1 _i ) in the image. The radius r _i and the area S2 _i of the circle are calculated from the maximum diameter R _i . The area S1 _i in the image is a projected area of each particle of the filler, and can be directly obtained from the image data by image processing.

  The distribution of the maximum diameter obtained in this way may be temporarily stored in a storage device (not shown) such as a hard disk.

(3) Density calculation unit 16
The density calculation unit 16 calculates the size of the filler using the maximum diameter R _i as a diameter, and calculates the density of the filler in the filler-filled polymer. That is, when the image data is a two-dimensional image, the density calculation unit 16 obtains an area density occupied by a circle whose diameter is the maximum diameter _Ri with respect to the entire image area, and when the image data is a three-dimensional image. Finds the volume density occupied by a sphere having the maximum diameter Ri relative to the entire image volume. The density thus obtained may be temporarily stored in a storage device such as a hard disk.

More specifically, for example, when the image data is two-dimensional, (total area occupied by a circle of diameter R _i (radius r _i )) / (area of the entire image) = filler density ρ, that is, density ρ Is represented by the following formula (1).

(4) Diameter ratio calculation unit 18
Diameter ratio calculation unit 18 calculates the average value A of the ratio of equivalent diameter to the maximum diameter R _i of filler from the image data, i.e., the average value A (corresponding diameter) / (maximum diameter). The obtained average value A may be temporarily stored in a storage device such as a hard disk.

  The equivalent diameter is a diameter of a circle or a sphere corresponding to the area, volume, and circumference of the filler obtained from the image data, and is obtained by image analysis. Specifically, in the case of two-dimensional data, the diameter of a circle having the same area as the filler area of the image (area equivalent circle diameter, also referred to as equal area equivalent circle diameter) and the same circumference as the filler circumference of the image. Examples include the diameter of a circle (equivalent diameter of a circumferential circle). In the case of three-dimensional data, the diameter of a sphere having the same volume as the filler volume of the image (volume sphere equivalent diameter) is mentioned.

As an example, when the equivalent diameter is an area circle equivalent diameter, the average value A is represented by the following formula (2). In the formula, r ′ is a radius of an equivalent diameter.

(5) System creation unit 20
The system creation unit 20 creates a system (system) used for molecular dynamics calculation, and creates a system in which particles having the maximum diameter distribution are included at a ratio calculated based on the density. To do. The system to be created may be two-dimensional or three-dimensional. The system to be created is an NVT ensemble with a fixed number of particles, volume and temperature.

  More specifically, the system creation unit 20 defines fillers with circular (two-dimensional) or spherical (three-dimensional) particles that have a maximum diameter, and the particles have the maximum diameter shown in FIG. Generate in the system according to the distribution. At that time, the density is adjusted so that the density of the particles in the system matches the density of the filler in the filler-filled polymer, and then the particles are randomly arranged in the system.

For example, in the case of creating a three-dimensional system, the density may be adjusted by calculating the volume from the particle radius ri and determining the volume V of the system from the filler density ρ based on the following equation (3). . Note that the density ρ of the filler may be used as it is even when the area density is calculated from the two-dimensional image data as in the above formula (1).

(6) Molecular dynamics calculation unit 22
The molecular dynamics calculation unit 22 obtains the position coordinates of the particles in the equilibrium state by equilibrating the system created above by molecular dynamics calculation (hereinafter also referred to as MD calculation). Equilibrium calculation by MD calculation is well known, and several potential functions are set between particles, and the particle position is gradually shifted according to Newton's equation of motion. By simulating the situation where the statistical equilibrium state is reached, the position coordinate group of the gravity center of the particle in the equilibrium state can be obtained. Such molecular dynamics simulation can be performed using, for example, the public program LAMMPS [Large-Scale Atomic / Molecular Massively Parallel Simulator: Sandia National Laboratory, USA].

  Since the system equilibrates with time development, after randomly generating particles as described above, MD calculation is performed in predetermined time increments, and it can be determined that equilibration occurs after a certain period of time has passed. . Alternatively, in the NVT ensemble system, it is also possible to determine whether or not an equilibrium state is established based on whether or not the pressure statistically approaches a constant value.

  FIG. 6 is a diagram showing the position of the center of gravity of the particle P in an equilibrium state obtained by MD calculation for a three-dimensional system. The figure shows a state in which spherical particles P having the same size are arranged at the center of gravity of each particle in consideration of visibility, but in actual MD calculation, each particle has a different radius according to the distribution of the maximum diameter. have.

(7) Filler position acquisition unit 24
The filler position acquisition unit 24 acquires the position coordinates of the particles in an equilibrium state as the position coordinates of the filler. That is, the filler position acquisition unit 24 defines the position coordinates of the center of gravity of the particles obtained by the molecular dynamics calculation unit 22 as the position coordinates of the center of gravity of the filler when creating the filler-filled polymer model.

(8) Model creation unit 26
The model creation unit 26 places a filler model at the filler position coordinates obtained above, and creates a filler-filled polymer model by placing a polymer model around the filler model.

Specifically, the model creation unit 26 arranges the filler model around the position coordinates acquired by the filler position acquisition unit 24. In that case, since the volume fraction is large in the particles created with the maximum diameter, a filler model having a volume corresponding to each position coordinate is arranged. Specifically, the model creation unit 26 calculates the equivalent diameter of each filler from the maximum diameter using the average value A of the ratio calculated by the diameter ratio calculation unit 18, and creates a filler model with the equivalent diameter as the diameter. It arrange | positions to the position coordinate of this filler. For example, a spherical filler model having a radius r ′ _j calculated by the following equation (4) is arranged around the coordinates obtained above. Thereby, as shown in FIG. 7, filler models F having different radii corresponding to the equivalent diameter are arranged at predetermined positions. The volume of the filler model may be quoted as it is, instead of calculating the volume by multiplying the average value of the ratio of the equivalent diameter and the maximum diameter by the maximum diameter.

  In addition, the model creating unit 26 arranges the polymer model G in the voids of the filler model F arranged as described above. This provides a three-dimensional filler-filled polymer model that can be used for molecular dynamics simulations as shown in FIG. FIG. 8 shows a state in which a part of the filler-filled polymer model is cut out by a cube (the same applies to FIG. 7).

(9) Output unit 28
The output unit 28 outputs the filler-filled polymer model obtained as described above. The filler-filled polymer model can be output by being displayed on a display or printed by a printer.

  Next, the operation state of the creation apparatus 10 according to the present embodiment will be described based on the flowchart of FIG.

  In step S1, the input unit 12 acquires image data of the target filler-filled polymer as shown in FIG.

Then, in step S2, the distribution calculation unit 14, the image data obtained in the above, determine the distribution of the maximum diameter R _i of filler, creating a distribution data of maximum diameter R _i as shown in FIG. Then, the process proceeds to step S3.

In step S3, the density calculation unit 16 calculates the size of the filler using the maximum diameter R _i as the diameter, and calculates the density ρ of the filler in the filler-filled polymer. Then, the process proceeds to step S4.

In step S4, the diameter ratio calculation unit 18 calculates the average value A of the ratio of equivalent diameter to the maximum diameter R _i of filler from the image data. Then, the process proceeds to step S5.

In step S <b> 5, the system creation unit 20 creates a system that includes particles having a distribution with the maximum diameter R _i at a ratio calculated based on the density ρ. Then, the process proceeds to step S6.

  In step S6, the molecular dynamics calculation unit 22 obtains the position coordinates of the particles P in an equilibrium state as shown in FIG. 6 by equilibrating the system created above by MD calculation. Then, the process proceeds to step S7.

  In step S7, the filler position acquisition unit 24 acquires the position coordinates of the particles in the equilibrium state as the filler position coordinates. Then, the process proceeds to step S8.

  In step S8, the model creation unit 26 places the filler model F in the position coordinates of the filler obtained above (see FIG. 7), and places the polymer model G in the gap to create a filler-filled polymer model. . Then, the process proceeds to step S9.

  In step S9, the output unit 28 outputs the filler-filled polymer model obtained as described above.

  According to the present embodiment as described above, since the filler position coordinates are obtained by MD calculation using the system containing the filler according to the distribution of the maximum diameter, the radial distribution function of the filler in the filler-filled polymer. Reproducibility can be improved. That is, the filler position coordinates that reproduce the radial distribution function of the filler can be obtained from the image data of the filler-filled polymer actually blended. Moreover, according to this embodiment, the particle size distribution of the filler in the actually mixed state is also considered. The obtained filler-filled polymer model is used to analyze the properties (for example, physical properties and deformation behavior) of the filler-filled polymer by molecular dynamics simulation. In this case, the particle size distribution is considered as described above. Since the radial distribution function is also reproduced, the analysis accuracy can be improved.

  In the present embodiment, in step S8, the model creation unit 26 converts the maximum diameter to the equivalent diameter and arranges the filler model having the equivalent diameter as the diameter. A simulation model according to the density can be created, and also from this point, the analysis accuracy of the molecular dynamics simulation using the model can be improved.

[Second Embodiment]
As shown in FIG. 9, the filler-filled polymer model creation device 10A according to the second embodiment includes a barycentric coordinate calculation unit 30, a first radial distribution function calculation unit 32, a fitting processing unit 34, and a distribution creation unit 36. And the second radial distribution function calculation unit 38 and the determination unit 40 are different from the first embodiment.

  The creation apparatus 10A can also be realized by using a general-purpose computer as basic hardware. In addition to the components of the first embodiment, the center-of-gravity coordinate calculation unit 30, the first radial distribution function calculation unit 32, and the fitting The processing unit 34, the distribution creation unit 36, the second radial distribution function calculation unit 38, and the determination unit 40 can be realized by causing a processor mounted on a computer to execute a program.

  Hereinafter, parts different from the first embodiment will be described. First, the configuration and function of each unit will be described in order.

(10) Centroid coordinate calculation unit 30
The barycentric coordinate calculation unit 30 calculates the barycentric position of each filler from the image data (see FIG. 3) input by the input unit 12. Specifically, the center of gravity of all particles (N particles) included in the image is obtained using image processing software, and the position coordinates are calculated. FIG. 11 shows an example of the center-of-gravity position coordinate data thus obtained. For a two-dimensional binarized image having a size of (x * y), each particle included in the image (index : i = 1 to N), the barycentric position coordinate is specified by the x coordinate and the y coordinate.

(11) First radial distribution function calculation unit 32
The 1st radial distribution function calculation part 32 calculates the radial distribution function of the filler in a filler filling polymer from the gravity center position coordinate acquired above. The radial distribution function indicates the probability of how many other particles are present at a certain distance from a certain particle, and can be calculated by a known calculation method. The radial distribution function of the filler based on the obtained gravity center position may be temporarily stored in a storage device (not shown) such as a hard disk.

(12) Fitting processing unit 34
The fitting processing unit 34 performs a fitting process on the maximum diameter distribution obtained by the distribution calculating unit 14 to obtain a distribution function that approximates the maximum diameter distribution. That is, the fitting processing unit 34 performs function fitting on the maximum diameter distribution to obtain a distribution function as an approximate curve.

  More specifically, as shown in FIG. 12, a histogram is created by plotting the relationship between the maximum diameter obtained from image data and its frequency, and using an optimization algorithm based on the plotted discrete data, FIG. An approximate curve as shown in Fig. 2 is obtained. Examples of the distribution function as the approximate curve include a normal distribution function and a lognormal distribution function, and it is particularly preferable to use a lognormal distribution function. This is because the particle size distribution of a general filler usually follows a log normal distribution. The optimization algorithm for calculating the approximate curve includes a least square method, a non-linear least square method using the Levenberg-Marquardt algorithm, a non-linear least square method using a genetic algorithm, and the like. The least square method is preferable.

The lognormal distribution function is represented by a probability density function f (x) represented by the following formula (5) where x is the maximum diameter. In the equation, μ and σ are distribution parameters (where σ> 0), and a is a coefficient. By obtaining a, μ and σ by fitting, a distribution function approximating the distribution of the maximum diameter can be obtained.

(13) Distribution creation unit 36
The distribution creating unit 36 creates a maximum diameter distribution according to the distribution function from the distribution function obtained by the fitting processing unit 34 with a predetermined number of particles. Specifically, the maximum diameter distribution according to the distribution function is generated according to random number generation or a cumulative function. FIG. 13 shows an example of the created maximum diameter distribution data, and the maximum diameter distribution can be created with an arbitrary number of particles.

  The number of particles to be generated can be set appropriately according to the calculation man-hours, the size of the filler-filled polymer model to be created, etc., and by generating the number of particles according to the simulation size, various sizes can be set. A filler-filled polymer model corresponding to the simulation can be obtained.

  The number of particles may be input in advance using the input unit 12. In addition, when the reproducibility of the radial distribution function calculated from the position coordinates of the filler obtained in step S7 is insufficient, it is updated so that the number of particles is increased by a predetermined number to obtain the updated number of particles. The maximum diameter distribution according to the distribution function may be recreated.

(14) System creation unit 20
The system creation unit 20 is basically the same as that of the first embodiment described above, but in this embodiment, the density calculation unit 16 obtains particles having the maximum diameter distribution created by the distribution creation unit 36. It is generated in the system at a ratio calculated based on the obtained density, thereby creating a system including particles having a maximum diameter distribution.

  Specifically, the volume of the particles is calculated from the particles having the maximum diameter distribution created by the distribution creating unit 36, the system volume V is determined based on the above equation (3) from the filler density ρ, and the volume V Each particle having an excluded volume calculated from the maximum diameter is generated in a system having, and the particles are randomly arranged in the system.

(15) Second radial distribution function calculation unit 38
The second radial distribution function calculation unit 38 calculates a filler radial distribution function from the position coordinates acquired by the filler position acquisition unit 24. The calculation method of the radial distribution function itself can be configured in the same manner as the first radial distribution function calculation unit 32.

(16) Determination unit 40
The determination unit 40 determines that the radial distribution function obtained by the second radial distribution function calculation unit 38 is within a predetermined allowable range with respect to the radial distribution function obtained by the first radial distribution function calculation unit 32. Check if they match. That is, comparing the two, if the difference is within a predetermined range, it is determined that the reproducibility of the radial distribution function is within the allowable range, and if the difference exceeds the predetermined range, the reproducibility of the radial distribution function is It is determined that it is outside the allowable range. If it is outside the allowable range, the number of particles generated in the distribution creation unit 36 is increased to recreate the maximum diameter distribution, and the system creation unit 20, molecular dynamics calculation unit 22, filler position acquisition unit 24, and second Control is performed so that the processing of the radial distribution function calculation unit 38 is repeated. On the other hand, if it is within the allowable range, control is performed so as to proceed to the process of the model creation unit 26.

The determination as to whether or not the value is within the allowable range is not particularly limited, and can be performed using, for example, the square root of the mean square error (RMSE) expressed by the following formula (6), and the calculated RMSE is a predetermined value. Judgment can be made based on whether it is within the range.

Wherein, in g (r) is the radial distribution function, n represents the number of data, r _k is the value of the distance of each data point, g _exp (r) were calculated by the first radial distribution function calculation unit 38 G _calc (r) is calculated by the second radial distribution function calculator 38.

  Next, the operation state of the creation apparatus 10A according to the second embodiment will be described based on the flowchart of FIG.

  In step S <b> 1, the input unit 12 acquires image data of the target filler-filled polymer.

  Next, in step S10, the barycentric coordinate calculation unit 30 calculates the barycentric position of each filler from the image data obtained above, and creates barycentric position coordinate data as shown in FIG. Then, the process proceeds to step S11.

  In step S11, the first radial distribution function calculation unit 32 calculates the radial distribution function of the filler in the filler-filled polymer from the center-of-gravity position coordinates obtained above, and is represented by “experiment” in FIG. A radial distribution function like this is obtained. And it progresses to step S2 and performs the process of step S3 and step S4 sequentially. The processes in steps S2 to S4 are the same as those in the first embodiment.

  Next, in step S12, the fitting processing unit 34 performs a fitting process on the maximum diameter distribution obtained in step S2 to obtain a distribution function that approximates the maximum diameter distribution. Then, the process proceeds to step S13.

  In step S13, the distribution creating unit 36 creates a maximum diameter distribution according to the distribution function with a predetermined number of particles as shown in FIG. 13 by random number generation or the like from the distribution function obtained in step S12. Then, the process proceeds to step S5.

  In step S5, the system creation unit 20 generates particles having the maximum diameter distribution created in step S13 in the system at a ratio calculated based on the density obtained in step S3. Create a system containing particles with a size distribution. And it progresses to step S6 and performs step S7 further. The processes in steps S6 and S7 are the same as those in the first embodiment.

  Next, in step S14, the second radial distribution function calculation unit 38 calculates a filler radial distribution function from the position coordinates acquired in step S7. Then, the process proceeds to step S15.

  In step S <b> 15, the determination unit 40 determines that the radial distribution function obtained by the second radial distribution function calculation unit 38 is a predetermined value relative to the radial distribution function obtained by the first radial distribution function calculation unit 32. It is determined whether they are within the allowable range, and if they are out of the allowable range, the distribution creation unit 36 increases the number of particles generated in step S13 and then recreates the maximum diameter distribution, and steps S5, S6, Control is performed so as to repeat the processes of S7 and S14. If it is within the allowable range, the process proceeds to step S8, and step S9 is further performed. The processes in steps S8 and S9 are the same as those in the first embodiment.

  According to the second embodiment as described above, in addition to the operational effects of the first embodiment, there are further subparts for allowing the size of the system to be freely changed, such as steps S12 and S13. A filler-filled polymer model can be created corresponding to a large simulation size. That is, when the maximum diameter distribution acquired from the image data is used as it is, the number of particles is limited to the number of fillers included in the image data. If the number of particles is arbitrarily extracted from the maximum diameter distribution, the maximum diameter distribution may be different from that of actual image data. According to the second embodiment, the distribution function that approximates the distribution of the maximum diameter obtained from the image is obtained, and the distribution of the maximum diameter is reconstructed from the distribution function. The number of particles can be arbitrarily set while maintaining.

  Further, according to the second embodiment, it is confirmed whether or not the radial distribution function of the filler arrangement obtained by MD calculation accurately reproduces the radial distribution function calculated based on the center of gravity position from the image data. Therefore, it can be assured that the obtained filler-filled polymer model accurately reproduces the distribution state of the filler in the image data.

  In the second embodiment, the center-of-gravity coordinate calculation unit 30, the first radial distribution function calculation unit 32, and the second radial distribution are used to confirm whether the radial distribution function calculated based on the center-of-gravity position is reproduced. Although the function calculation unit 38 and the determination unit 40 are provided, they may be configured without providing them. Further, in the first embodiment, the respective parts 30, 32, 38 and 40 for confirmation can be provided.

  From the two-dimensional binarized image having the area (x * y) shown in FIG. 3, the gravity center position of each filler aggregate was calculated, and the radial distribution function was calculated. The results are shown as “experiment” in FIGS.

  In the example, from the two-dimensional binarized image having the area of (x * y) shown in FIG. 3, according to the second embodiment, based on the distribution of the maximum diameter of each filler aggregate, the MD calculation is performed. The position coordinates were obtained, and the radial distribution function was calculated from the obtained position coordinates. The result is shown as “Example” in FIG.

  In the example, in step S12, fitting was performed using the lognormal distribution function represented by the above formula (5), and a, μ, and σ were obtained by the least square method. a = 2367, μ = 2.17, and σ = 0.58. In step S13, the distribution of the maximum diameter shown in FIG. 13 according to the distribution function was created by generating a random number from the distribution function obtained in step S12, with the number of particles being N = 100.

For the MD calculation in step S6, the published program LAMMPS [Large-Scale Atomic / Molecular Massively Parallel Simulator: Sandia National Laboratory, USA] was used. calculated by 3), the temperature T = 1.0, LJ / expand ε = 1.0, σ = 1.0, the displacement volume radius delta = r _i -0.56, and the cutoff = 1.12.

  In Comparative Example 1, the number N of filler aggregates is obtained from a two-dimensional binarized image having an area of (x * y) shown in FIG. 3, and the number of filler aggregates with respect to the total area of the image is expressed by density (number (Density) ρ, particles having a particle shape as a mass point (radius = 0) are generated in a volume V = N / ρ system, and MD calculation is performed in the same manner as in Example 1 by using the system to determine the filler. The position coordinates were obtained, and the radial distribution function was calculated from the obtained position coordinates. The result is shown as “Comparative Example 1” in FIG.

  In Comparative Example 2, the particle diameter of the particles arranged in the system in Comparative Example 1 was set to a value obtained by an equal division of the filler volume. That is, the particle diameter was calculated from the total number of pixels of filler / number of filler aggregates, and the resulting spherical particles having the same diameter were generated in the system. Otherwise, the radial distribution function was calculated in the same manner as in Comparative Example 1. The result is shown as “Comparative Example 2” in FIG.

  In Comparative Example 3, the radial distribution function was calculated in the same manner as in the Example except that the area circle equivalent diameter distribution was used instead of using the maximum diameter distribution. Therefore, in Comparative Example 3, the density ρ in step S3 was calculated by ρ = (occupied area of each filler aggregate) / (area of the entire image). The results are shown as “Comparative Example 3” in FIG.

About the radial distribution function obtained by the said Example and Comparative Examples 1-3, the precision of reproducibility was confirmed by the square root (RMSE) of the mean multiplication error by the said Formula (6). In Expression (6), g _exp (r) is a radial distribution function indicated by “experiment”, and g _calc (r) is a radial distribution function obtained in Examples and Comparative Examples 1 to 3. . The results are shown in Table 1.

  As shown in FIGS. 15 and 16, in Comparative Examples 1 and 2 in which the particle size distribution is not considered, the difference from the “experimental” radial distribution function calculated as it is from the image data is large. Could not be reproduced at all. In Comparative Example 3 shown in FIG. 17, the reproducibility can be improved by generating according to the distribution of the equivalent diameter as compared with Comparative Examples 1 and 2, but the radial distribution function of “Experiment” can be accurately reproduced. I couldn't say that. In contrast, in the example shown in FIG. 16, the radial distribution function of the filler arrangement obtained by MD calculation is in good agreement with the radial distribution function of “experiment”, and the radial distribution of “experiment” The function was reproduced. This point was also clear from the results of RMSE shown in Table 1.

  Although one embodiment of the present invention has been described above, this embodiment is presented as an example and is not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the spirit of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, and are included in the invention described in the claims and the equivalents thereof.

DESCRIPTION OF SYMBOLS 10 ... Creation apparatus 12 ... Input part 14 ... Distribution calculation part 16 ... Density calculation part 18 ... Diameter ratio calculation part 20 ... System creation part 22 ... Molecular dynamics calculation part 24 ... Filler position acquisition part 26 ... Model creation part 34 ... Fitting Processing unit 36 ... Distribution creation unit

Claims (6)

A creation device for creating a filler-filled polymer model for analyzing the characteristics of a polymer filled with a filler by molecular dynamics simulation,
An input unit for obtaining image data of the filler-filled polymer;
A distribution calculation unit for obtaining a distribution of the maximum diameter of the filler from the image data;
Calculating the size of the filler with the maximum diameter as the diameter, and a density calculating unit for calculating the density of the filler in the filler-filled polymer;
A system creation unit for creating a system in which particles having a distribution of the maximum diameter are included in a ratio calculated based on the density;
By equilibrating the created system by molecular dynamics calculation, a molecular dynamics calculation unit that obtains the position coordinates of the particles in the equilibrium state,
A filler position acquisition unit that acquires the position coordinates of the particles in an equilibrium state as the position coordinates of the filler;
A model creation unit that places a filler model in the position coordinates of the filler and creates a filler-filled polymer model by placing a polymer model around the filler model;
A filler-filled polymer model creation device characterized by comprising: A diameter ratio calculation unit for calculating an average value of the ratio of the equivalent diameter to the maximum diameter of the filler from the image data,
The model creation unit calculates the equivalent diameter of each filler from the maximum diameter using the average value of the ratio calculated by the diameter ratio calculation unit, and places a filler model having the equivalent diameter as a diameter at the position coordinates of the filler The filler-filled polymer model creation device according to claim 1, wherein: A fitting processing unit that performs a fitting process on the distribution of the maximum diameter obtained by the distribution calculating unit and obtains a distribution function that approximates the distribution of the maximum diameter;
A distribution creating unit that creates a maximum diameter distribution according to the distribution function at a predetermined number of particles from the distribution function;
The system creation unit generates particles having the maximum diameter distribution created by the distribution creation unit in the system at a ratio calculated based on the density, thereby including a system including particles having the maximum diameter distribution. The filler-filled polymer model creation device according to claim 1 or 2, wherein the device is created.   4. The filler-filled polymer model creation device according to claim 3, wherein the distribution function is a lognormal distribution function. A creation method for creating a filler-filled polymer model for analyzing characteristics of a polymer filled with a filler by molecular dynamics simulation,
An input step for obtaining image data of the filler-filled polymer;
A distribution calculating step for obtaining a maximum diameter distribution of the filler from the image data;
Calculating the size of the filler with the maximum diameter as the diameter, and a density calculating step of calculating the density of the filler in the filler-filled polymer;
A system creation step of creating a system in which particles having a distribution of the maximum diameter are included in a proportion calculated based on the density;
A molecular dynamics calculation step for obtaining the position coordinates of particles in an equilibrium state by equilibrating the created system by molecular dynamics calculation,
A filler position acquisition step of acquiring the position coordinates of the particles in an equilibrium state as the position coordinates of the filler;
A model creation step of placing a filler model at the filler position coordinates and creating a filler-filled polymer model by placing a polymer model around the filler model;
A method for creating a filler-filled polymer model, comprising: A program for creating a filler-filled polymer model for analyzing the characteristics of a polymer filled with a filler by molecular dynamics simulation,
On the computer,
An input function to acquire image data of filler-filled polymer;
A distribution calculating function for obtaining the distribution of the maximum diameter of the filler from the image data;
A density calculating function for calculating the filler size in the filler-filled polymer by calculating the size of the filler using the maximum diameter as a diameter; and
A system creation function for creating a system in which particles having a distribution of the maximum diameter are included in a ratio calculated based on the density;
A molecular dynamics calculation function that obtains the position coordinates of particles in an equilibrium state by equilibrating the created system by molecular dynamics calculation,
Filler position acquisition function that acquires the position coordinates of the particles in an equilibrium state as the position coordinates of the filler,
A model creation function for placing a filler model in the position coordinates of the filler and creating a filler-filled polymer model by placing a polymer model around the filler model;
A program to create a filler-filled polymer model to achieve this.