Program and method for simulating particle behavior

Abstract

To provide a program and a method for simulating particles from which at least part of predetermined restrictions is removed, and a method for manufacturing data.SOLUTION: A program for simulating the behavior of particles in fluid causes a processor of an information processing apparatus to execute the steps of: reading data of the fluid; reading data of first particles; performing coarse graining of the particles based at least on the data of the first particles to generate data of second particles; and calculating the behavior of the particles based at least on the data of the fluid and the data of the second particles. The step of generating the data of the second particles includes, in correspondence with performing coarse graining of the particles, correcting one or more of the mass and density of the first particles and a force applied to the particles so that the acceleration of the second particles is made equal to the acceleration of the first particles. The step of calculating the behavior of the particles includes calculating the behavior based at least on the drag coefficient of the fluid.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present disclosure relates to a program and method for simulating particle behavior, and more particularly to a program for simulating the behavior of coarse-grained particles, a method using the program, and a method for producing data using the program. [Background technology]

[0002] When dealing with powders, particle behavior is often simulated in advance using a computer. Popular methods include the Distinct Element Method (DEM) or Discrete Element Method (DEM).

[0003] Patent Document 1 discloses a method for determining the electrostatic force acting between particles based on the degree of particle mixing when the particles are mixed and stirred, and the amount of static electricity generated between the particles, in a DEM simulation.

[0004] Furthermore, Non-Patent Document 1 discloses a method using a simpler coarse-grain model (SCG model) for the purpose of reducing the computational load in DEM. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2000-214134 [Non-patent literature]

[0006] [Non-Patent Document 1] Processes 2021, 9(7), 1098 Summary of the Invention [Problem to be solved by the invention]

[0007] As mentioned above, various particle simulation methods have been developed up to now. However, these methods have had various limitations. For example, the method used in the above-mentioned Non-Patent Document 1 could only be adopted under limited conditions.

[0008] Therefore, an object of the present disclosure is to provide a means for simulating particles that overcomes at least some of the limitations mentioned above. [Means for solving the problem]

[0009] In order to achieve the above object, the present disclosure provides, in one aspect, the following invention. (Invention 1) A program for simulating the behavior of particles in a fluid, comprising: A program capable of instructing a processor of an information processing device to execute the following steps: Reading fluid data; reading data of a first particle; coarse-graining the particles based at least on the data of the first particles to generate data of a second particle; calculating particle behavior based at least on the fluid data and the second particle data; wherein the step of generating data of the second particle includes correcting one or more of a mass, a density, and a force applied to the first particle in response to coarse-graining of the particle so that an acceleration of the second particle is equal to an acceleration of the first particle; The step of calculating the particle behavior includes calculating based at least on a drag coefficient of the fluid; The drag coefficient is expressed by the following formula:

number

number

number

number

[0010] In one aspect, the invention calculates particle behavior based at least on a drag coefficient expressed by the following formula:

[0011]

number

[0012] (where Re is the Reynolds number of the fluid, and a, b, and c are constants determined by the sphericity of the first particles.)

[0013] This allows simulation of particles other than spherical ones, and calculations can be performed over a range of Reynolds numbers. [Brief explanation of the drawings]

[0014] [Figure 1] 1 illustrates a configuration of an information processing device according to an embodiment of the present disclosure. [Figure 2] 1 illustrates a configuration of a system according to an embodiment of the present disclosure. [Figure 3] 1 illustrates a method of the present disclosure in one embodiment. [Figure 4] 1 shows the results of simulating particle behavior in an example. [Figure 5] 1 shows the results of simulating the behavior of particles in Comparative Example 1. [Figure 6] 10 shows the results of simulating the behavior of particles in Comparative Example 2. DETAILED DESCRIPTION OF THE INVENTION

[0015] Specific embodiments for carrying out the present invention will be described below. The following description is intended to facilitate understanding of the invention and is not intended to limit the scope of the present invention. 1. Overview In one embodiment, the present disclosure relates to a program for simulating the behavior of particles in a fluid, a method using the program, and a method for producing data using the program.

[0016] The technical field of application is not particularly limited, and the method can be applied to any technical field that simulates the behavior of particles in a fluid. Examples of the technical field include, but are not limited to, particles in a cyclone separator, particles in a stirring tank, dust in a room, particles in a jet mill crusher, particles in flotation, particles in sandblasting, particles in a slurry, etc.

[0017] The size of the particles is not particularly limited, and the method and program of the present disclosure can be applied to particles of any size. For example, the size of the particles may be on the order of millimeters, microns, or nanometers. Particles also include powders. The type of particles is also not limited, and the method and program of the present disclosure may simulate the behavior of one type of particle or two or more types of particles. The shape of the particles and the number of particles are also not particularly limited.

[0018] Preferably, the program etc. of the present disclosure is suitable for simulating the behavior of particles in a dilute fluid, in other words, the program etc. of the present disclosure is suitable for simulating the behavior of particles in a situation where the concentration of particles in the fluid is low.

[0019] Although not limited thereto, the program etc. of the present disclosure is suitable for simulating the behavior of particles under conditions represented by a Stokes number of less than 100, for example.

[0020] Furthermore, the terms "first particle" and "second particle" are used herein. Here, "second particle" is a term related to the coarse-graining of "first particle." However, when there are multiple types of particles to be simulated, there are "first particles" corresponding to the number of types, and there are "second particles" corresponding to each of these. Therefore, as stated again, it should be noted that the scope of applicability of the present disclosure is not limited to cases where one type of particle is handled.

[0021] 2. Program execution environment The environment for executing the program and method is not particularly limited, and a typical information processing device can be used. The information processing device 100 can typically include a processor 110, a memory 120, a non-transitory storage medium 130, and a communication module 140, as shown in FIG.

[0022] Examples of the information processing device (100) include, but are not limited to, a server, a personal computer, a tablet terminal, a smartphone, a smart watch, smart glasses, etc.

[0023] The program is stored in a non-transitory storage medium (130, e.g., HDD, SSD, etc.), loaded into a memory (120, e.g., RAM, etc.) as needed, and executed by a processor (110, e.g., CPU, etc.). If necessary, the program can connect to a network through a communication module (140) to send and receive information.

[0024] In one embodiment, the program may be installed as application software on one information processing device (100) and executed by the information processing device (100).

[0025] In another embodiment, the number of information processing devices 100 is not limited to one, and multiple information processing devices 100 may be used as needed. In this case, the functions of the program may be distributed among the multiple information processing devices 100.

[0026] Alternatively, as shown in Fig. 2, a system (200) may be configured in which a server (210) and a terminal (220) are interconnected via a network. In the system (200), the terminal (220) may receive input from a user and transmit at least a portion of the received input to the server (210). The server (210) may receive input information transmitted from the terminal (220), process the information, and transmit a portion of the output to the terminal (220). The terminal (220) may then receive output information transmitted from the server (210) and display it on the terminal (220).

[0027] Therefore, in another aspect, the present disclosure also relates to an information processing device including the program of the present disclosure, and a system including the information processing device. In yet another aspect, the present disclosure relates to a terminal and / or a server constituting the system of the present disclosure. The internal configuration of the terminal and the server may be the same as that of the information processing device shown in FIG. 1. In yet another aspect, the present disclosure relates to a storage medium (e.g., HDD, SSD, flash memory, optical disk, etc.) that stores a program.

[0028] 3. Each step the program takes In one embodiment, the program of the present disclosure includes at least the following steps, as shown in FIG.

[0029] A step of reading fluid data (S10); A step of reading data of a first particle (S20); coarse-graining the particles based at least on the data of the first particles to generate data of the second particles (S30); Calculating particle behavior based at least on the fluid data and the second particle data (S40).

[0030] Here, the step of generating data for the second particle includes correcting one or more of the mass, density, and force applied to the first particle so that the acceleration of the second particle is equal to the acceleration of the first particle, corresponding to the coarse-graining of the particle.

[0031] Also, the step of calculating the behavior of the particles includes calculating based at least on a drag coefficient of the fluid.

[0032] The drag coefficient is then expressed by the following formula:

number

[0033] Each step is described in detail below.

[0034] 3-1. Steps to read fluid data As described above, in one embodiment, the present disclosure relates to a program for simulating the behavior of particles in a fluid. Therefore, in order to perform the simulation, it is necessary to load data of the fluid.

[0035] The fluid data may be loaded by the user inputting given information through an interface (e.g., an input form displayed on a display), or by loading a given file (e.g., when duplicating data from past simulation data).

[0036] The fluid data may include, but is not limited to, any one or more of the following: fluid density, fluid viscosity, fluid velocity, etc. Preferably, the fluid data may further include any one or more of the following: fluid temperature, spatial coordinates (e.g., X, Y, Z), and X, Y, Z components of fluid velocity, etc.

[0037] 3-2. Steps to read data for the first particle In order to perform the simulation, it is necessary to load not only fluid data but also particle data. The execution of the steps of loading both fluid data and particle data can be either first, either second, or simultaneously.

[0038] In this specification, the term "first particle" refers to the original particle that is the subject of the simulation.

[0039] The data of the first particle may include, but is not limited to, any one or more of the following: particle size (e.g., diameter in the case of spherical particles), number of particles, shape (e.g., sphericity), mass. Preferably, the data of the first particle may include any one or more of the following: particle size (e.g., diameter in the case of spherical particles), shape (e.g., sphericity), Poisson's ratio, Young's modulus, coefficient of friction, coefficient of restitution, contact angle, name of substance, mass per particle, volume, number of particles, mass, etc.

[0040] 3-3. Step of generating data for the second particle The data of the second particles is generated based at least on the data of the first particles described above. Preferably, the data of the second particles may be generated further based on the data of the fluid described above. A large number of particles increases the calculation load during simulation. This results in a long time required to obtain simulated results.

[0041] In order to reduce the computational load, the number of particles to be handled can be reduced and particle behavior can be analyzed using particles with a size larger than the size of the actual particles.

[0042] For the purpose of facilitating understanding of the invention, this section first describes the technique shown in Non-Patent Document 1, and then describes the step of generating data for the second particle in a program according to one embodiment of the present disclosure.

[0043] First, the acceleration of a particle can be expressed by the following relational expression. a=F D / m (Formula 7)

[0044] where: a: acceleration F D : Force acting on a particle from a fluid m: mass

[0045] And the force F that the particle receives from the fluid D can be expressed by the following relational expression:

number

[0046] where: C D :Drag coefficient (or resistance coefficient) A: Particle projected area on a plane perpendicular to the fluid flow direction ρ f : fluid density u r : Relative velocity of the particle to the fluid When the particle is non-spherical, the projected area A may be calculated by an appropriate method depending on the situation. For example, since the particle is arranged so that the surface with the smallest projected area faces the fluid flow, the projected area in this orientation may be used. Alternatively, the cross-sectional area of ​​a sphere with the same volume as the non-spherical particle (i.e., the area of ​​the cross section passing through the center of the sphere) may be used.

[0047] And the drag coefficient C D can be expressed by the following relational expression:

number

[0048] where: Re: Reynolds number (However, the above equation assumes that the Reynolds number is less than 2 and the particles are spherical.)

[0049] The Reynolds number Re can be expressed by the following equation:

number

[0050] where: d: particle diameter μ f : Fluid viscosity

[0051] Therefore, the acceleration in coarse-grained particles (CG) can be expressed by the following relational expression:

[0052]

number

[0053]

number

[0054] where ρ p : particle density

[0055] Based on these relational expressions, the calculation formula is corrected so that the coarse-grained particles (CG, Coarse-Grain) and the original particles (O, original) have the same acceleration.

[0056] To do this, the following equation should be solved:

[0057] a CG =a O (Formula 13)

[0058] where: a CG : Acceleration of coarse-grained particles a O :Acceleration of the original particle

[0059] Applying this to the above equation, the following relational expression is derived:

[0060]

number

[0061] By rearranging this equation, the following relational expression is derived:

[0062]

number

[0063] Therefore, by correcting the density of the original particles using the square ratio of the particle diameter ratio between the original particles and the coarse-grained particles, the acceleration of the original particles and the acceleration of the coarse-grained particles can be made equal.

[0064] For the effect of the above correction, please refer to the experimental results shown in Non-Patent Document 1 (for example, Fig. 1, etc.).

[0065] In one embodiment, the program may be configured to allow the user to input, for example, the original particle size together with a magnification (coarse-graining ratio) for the particle size through an interface, rather than directly receiving a correction value input by the user.

[0066] For example, if you want to double the size of the original particles, you can enter the number "2" as the coarse-graining ratio, or you can enter "200" (%) as a percentage.

[0067] The program may also adjust the number of particles according to the particle coarse-graining ratio. Preferably, the program may adjust the number according to the volume change resulting from the coarse-graining process. For example, if the particle coarse-graining ratio is set to double, the particle volume will increase eightfold. Accordingly, the number of particles may be adjusted to 1 / 8.

[0068] In the above method, the following values ​​were used as the drag coefficients:

number

[0069] However, the above drag coefficients are based on Stokes' law and assume that the Reynolds number is less than 2 and that the particles are spherical.

[0070] Therefore, it cannot be applied in situations other than the above prerequisites.

[0071] Therefore, in one embodiment, the method of the present disclosure employs the following drag coefficient instead of the above drag coefficient:

[0072]

number

[0073] The above relational expression was proposed by HNYOW et al. (Advanced Powder Technol., Vol. 16, No. 4, pp. 363-372 (2005)).

[0074] HNYOW et al. state that the above relationship is applicable to a wide range of sphericity, a wide range of Reynolds numbers, and particles of various shapes.

[0075] Therefore, in the program of one embodiment, the applicable sphericity is not particularly limited, and may be preferably 0.006 to 1, and more preferably 0.034 or more. In addition, in the program of one embodiment, the applicable Reynolds number is not particularly limited, and preferably 10 -2 ~10 5 and more preferably, it may be 2 or more. Furthermore, in one embodiment of the program, the shape of applicable particles is not particularly limited, and examples thereof include spheres, cubes, cuboctahedrons, regular octahedrons, regular tetrahedrons, disks, cylinders, and rectangular parallelepipeds.

[0076] When the drag coefficient by HNYOW et al. is adopted, the acceleration is calculated as follows:

number

[0077]

number

[0078] Here, the three terms in the above equation are respectively a 1,CG , a 2,CG , a 3,CG These correspond to terms containing constants a, b, and c, respectively.

[0079] a CG =a O where we need to solve the equation 1,O +a 2,O +a 3,O =a 1,CG +a 2,CG +a 3,CG It can be understood that the relationship between the two is sufficient. Then, by solving this equation, ρ O and ρ CGIf the relationship between the above and the drag coefficient is derived, the same correction as above becomes possible (in the above, the density is corrected by the ratio of the square of the particle diameter). However, when trying to solve this equation, it was found to be extremely complicated and could not be solved. Therefore, those skilled in the art did not even think of replacing the drag coefficient in the method shown in Non-Patent Document 1 with the drag coefficient proposed by HNYOW et al. In this regard, the present inventor CG =a O In solving the equation, i.e., a 1,O +a 2,O +a 3,O =a 1,CG +a 2,CG +a 3,CG In solving this relationship, we discovered that for this equation to hold, the terms containing the constants a, b, and c must be equal.

[0080] Therefore, three types of equations are set as follows:

[0081]

number

[0082] Solving the three equations gives the following:

[0083]

number

[0084] Therefore, when calculating the acceleration of coarse-grained particles, all that is required is to make a correction by multiplying the original particle density by the particle size ratio (square ratio, 3 / 2 power ratio, first power ratio) in each of the three terms. The method of solving the above equations is not particularly complicated, and has the advantage of being intuitively easy to handle as equations.

[0085] In the above method, density is corrected. The object of direct value correction is not limited to density, and other variables may be corrected. For example, when correcting mass, "m CG =km O" and "a CG =a O " (F D,CG / m CG =F D,O / m O After calculating the constant k such that the following equation holds, the acceleration in the coarse-grained particle is calculated as "F D,CG / (km O )" Therefore, correcting the acceleration of the second particle to be equal to the acceleration of the first particle includes correcting one or more of the mass, density, and force applied to the first particle.

[0086] The values ​​of a, b, and c in the following formula may be appropriately set according to the sphericity, as proposed by HNYOW et al.

[0087]

number

[0088] For example, as shown in Fig. 4 and Table 1 of HNYOW et al., a curve of sphericity, Reynolds number, and drag coefficient may be created by experiment or the like, and the values ​​of a, b, and c that fit this curve may be derived. Preferably, the values ​​of a, b, and c may be determined as follows according to the sphericity ψ:

[0089]

number

[0090]

number

[0091]

number

[0092] However, ψ is the sphericity of the particle. Here, the sphericity is the sphericity defined by Wadell. That is, the sphericity is expressed by the following formula: (Sphericity) = (Surface area of a sphere with the same volume as the first particle) / (Surface area of the first particle)

[0093] By the method shown above, the parameters related to the particles are corrected to generate the data of the second particle.

[0094] When simulating two or more types of particles, the step of generating the data of the second particle described above can be repeated according to the number of types.

[0095] By adopting the above method, it becomes possible to apply the simulation by coarsening to particles other than spherical particles, which could not be applied by the method of Non-Patent Document 1. Also, by adopting the above method, in the region where the Reynolds number is 2 or more (for example, the Allen region (2 < Re < 500), the Newton region (500 < Re < 10 5 )) where the conventional SCG model could not be applied, it becomes possible to apply the simulation by coarsening.

[0096] Also, when dealing with particles other than spherical particles, the size of the particles may be set by any method. For example, the diameter of a sphere having the same volume as the volume of the particle may be adopted as the size of the particle. Alternatively, twice the distance from the central part to the apex part of the particle may be adopted as the size of the particle (for example, in the case of a cube, rectangular parallelepiped, regular tetrahedron, regular octahedron, etc.). Alternatively, in the case of a cylinder or disk, twice the distance from the central part of the particle to an arbitrary point on the circumference may be adopted as the size of the particle. Alternatively, in any shape, the projected area is derived based on its volume, and the diameter of a sphere equal to the projected area may be adopted as the size of the particle.

[0097] 3-4. Steps for calculating particle behavior After generating the data of the second particles, the behavior of the particles is calculated. Although there is no particular limitation on the method for calculating the behavior of the particles, a typical example is DEM.

[0098] In this step, calculation is performed based on at least the fluid data and the second particle data, and in some cases, calculation may be performed further based on gravity data.

[0099] When calculating the behavior of particles in the internal space of the container, the calculation may be performed further based on data on the internal space of the container. In this case, the program can execute a step of reading the data on the internal space of the container in advance.

[0100] By performing this calculation, it is possible to generate data that records the position of each particle over time. This data can then be read into a specific program to display the particle behavior on a screen. [Example]

[0101] Below we show the results of simulating the behavior of particles settling in a liquid. The conditions were set as follows: Particle shape: spherical Original particle size: 1mm Coarse-grained particle size (or coarse-graining ratio): 1mm (1x), 2mm (2x), 4mm (4x) Fluid density: 1000kg / m 3 Fluid viscosity: 8.9×10 -4 Pa·s Particle density: 1200kg / m 3 Force acting on particle: Gravity g (9.8 m / s 2 )

[0102] Drag coefficient C D The following formulas were used to calculate the constants a, b, and c used to calculate

[0103]

number

[0104] [Number] (Equation 27)

[0105] [Number] (Equation 28)

[0106] Here, since the shape of the particle is spherical, the sphericity ψ = 1.

[0107] First, the theoretical value of the terminal velocity under the above set conditions was calculated.

[0108] At that time, since the Reynolds number Re was unknown, it was once assumed that the Reynolds number Re was in the Allen region (2 < Re < 500), and the terminal velocity was calculated by applying it to the following equation.

[0109] [Number] (Equation 29)

[0110] According to the above set conditions, each variable is as follows. ρ p = 1200 (kg / m 3 ) ρ f = 1000 (kg / m 3 ) g = 9.8 (m / s 2 ) μ = 8.9×10 -4 (Pa·s) d = 10 -3 (m)

[0111] Also, as described above, the Reynolds number Re can be expressed by the following relational expression. [Number] (Formula 30)

[0112] Here, d: Diameter of the particle μ f : Viscosity of the fluid

[0113] Therefore, when the above theoretical value is applied to the above formula to calculate the Reynolds number, it becomes as follows.

Number

[0114] From this, the Reynolds number is a value in the Allen region (2 < Re < 500), and it was confirmed that there is no contradiction with the above assumption. Therefore, it was confirmed that the terminal velocity (0.0425 m / s) calculated on the premise that it is the Allen region is correct.

[0115] Next, based on the above setting conditions, the behavior of particles settling in the liquid was simulated. Regarding the correction of acceleration performed when coarsening the particles, three types of corrections were set. The first is the correction corresponding to the example, and the acceleration was corrected based on the above (Formula 21). The second is the correction corresponding to the comparative example, and the acceleration was corrected based on the above (Formula 15) (Comparative Example 1). The third corresponds to the comparative example and no acceleration correction was performed (Comparative Example 2).

[0116] The results of the simulation are shown in FIGS. 4 to 6. In the example (FIG. 4), all the particles were settling at the same speed. Also, in Comparative Example 1 (FIG. 5), all the particles were settling at the same speed. On the other hand, in Comparative Example 2 (FIG. 6), the largest coarsened particles were settling the fastest.

[0117] Thus, it was shown that in the example and Comparative Example 1, even after coarsening, they behave in the same way as the original particles.

[0118] [[ID=四十二]]Here, when simulating, the terminal velocity of each particle in the example and Comparative Example 1 was calculated. The results were as follows. Example: 0.0422 m / s Comparative example 1: 0.1223m / s

[0119] Therefore, the terminal velocity of each particle in the example (0.0422 m / s) is almost identical to the theoretically calculated terminal velocity (0.0425 m / s). On the other hand, the terminal velocity of each particle in Comparative Example 1, which corresponds to the conventional SCG model (0.1223 m / s), is significantly different from the theoretically calculated terminal velocity. In other words, the example demonstrates that consistent simulation results can be obtained when the Reynolds number is 2 or greater (Re = 48).

[0120] Specific embodiments of the invention have been described above. The above embodiments are merely illustrative examples, and the present invention is not limited to these embodiments. For example, technical features disclosed in one of the above embodiments may be applied to other embodiments. Furthermore, unless otherwise specified, for a particular method, the order of some steps may be interchanged, and additional steps may be added between two specific steps. The scope of the present invention is defined by the claims. [Explanation of symbols]

[0121] 100 Information processing device 110 processors 120 memory 130 Non-transitory storage medium 140 Communication Module 200 systems 210 Server 220 terminals 310 Original Particles 320 2mm particles 330 particles of size 4 mm

Claims

1. 1. A method for simulating particle behavior in a fluid, comprising: A method in which a processor of an information processing device executes steps including: - reading fluid data; - reading data of a first particle; - coarse-graining the particles based at least on the data of the first particles to generate data of a second particle; - calculating particle behavior based at least on the fluid data and the second particle data; wherein the step of generating data of the second particle includes correcting one or more of a mass, a density, and a force applied to the first particle in response to coarse-graining of the particles so that an acceleration of the second particle is equal to an acceleration of the first particle; The step of calculating the particle behavior includes calculating based at least on a drag coefficient of the fluid; The drag coefficient is expressed by the following formula: [Equation 1] (Formula 1) (where Re is the Reynolds number of the fluid, and a, b, and c are constants determined by the sphericity of the first particles)

2. 2. The method of claim 1, wherein the correcting step includes correcting one or more of the mass, density, and force applied to the first particle so that, when the acceleration of the first particle and the acceleration of the second particle are each expressed using the drag coefficient, terms including constants a, b, and c are equal.

3. The method according to claim 1, wherein a, b, and c are determined by the following formulas 2 to 4. [Equation 2] (Formula 2) [Equation 3] (Formula 3) [Equation 4] (Formula 4) (where ψ is the sphericity of the particle)

4. 2. The method according to claim 1, wherein the step of calculating the constants a, b, and c in advance is executed, The method, wherein the step of calculating the constants a, b, and c in advance includes fitting from experimental data including one or more of sphericity, drag coefficient, and Reynolds number.

5. 10. The method of claim 1, further comprising the step of reading a coarse-graining ratio; The method, wherein the step of generating data for the second particles includes setting a size of the second particles based at least on the data for the first particles and the coarse-graining ratio.

6. The method of claim 1 , wherein the first particle data includes at least one of particle size, number, sphericity, and mass.

7. A program for causing an information processing device to execute each step according to any one of claims 1 to 6.

8. 1. A method for producing data defining particle behavior, comprising: A method in which a processor of an information processing device executes steps including: - reading fluid data; - reading data of a first particle; - coarse-graining the particles based at least on the data of the first particles to generate data of a second particle; - calculating particle behavior based at least on the fluid data and the second particle data; - creating data defining the behavior of the particles; wherein the step of generating data of the second particle includes correcting one or more of a mass, a density, and a force applied to the first particle in response to coarse-graining of the particles so that an acceleration of the second particle is equal to an acceleration of the first particle; The step of calculating the particle behavior includes calculating based at least on a drag coefficient of the fluid; The drag coefficient is expressed by the following formula: [Equation 5] (Formula 1) (where Re is the Reynolds number of the fluid, and a, b, and c are constants determined by the sphericity of the first particles) The data defining the particle behavior includes information about the location of each particle at each time.

9. 1. A system for simulating particle behavior in a fluid, comprising: The system includes a server and a terminal; The system is configured to perform steps including: - reading fluid data; - reading data of a first particle; - coarse-graining the particles based at least on the data of the first particles to generate data of a second particle; - calculating particle behavior based at least on the fluid data and the second particle data; wherein the step of generating data of the second particle includes correcting one or more of a mass, a density, and a force applied to the first particle in response to coarse-graining of the particles so that an acceleration of the second particle is equal to an acceleration of the first particle; The step of calculating the particle behavior includes calculating based at least on a drag coefficient of the fluid; The drag coefficient is expressed by the following formula: [Equation 6] (Formula 1) (where Re is the Reynolds number of the fluid, and a, b, and c are constants determined by the sphericity of the first particles)

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