CN111581875A - Computer simulation method of self-adaptive particle fluid - Google Patents
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Abstract
The invention discloses a computer simulation method of a self-adaptive particle fluid, which comprises the following steps: calculating the distance of each fluid particle from the surface of the fluid in the whole fluid simulation domain, and calculating the optimal radius of each particle according to the distance; according to the optimal radius, carrying out particle splitting operation or particle merging operation on all particles in the fluid domain to obtain updated particles; based on the updated particles, vortex amount constraint is added, and the virtual particles of the range boundary are constructed by using a boundary virtual particle self-adaptive method to control the boundary of the fluid so as to improve detail simulation; the latest position and velocity of all particles in the entire fluid simulation domain are constructed. The invention can automatically adjust the radius of the fluid particles to adapt to the required resolution simulation, thereby accelerating the efficiency of the fluid simulation and still providing abundant fluid details.
Description
Technical Field
The invention relates to the field of particle fluid, in particular to a computer simulation method of self-adaptive particle fluid.
Background
Fluids exist in every aspect of daily life, and fluids in the narrow sense refer to flowing liquids, such as: a water drop, a fountain, a spray in the sea, a waterfall, etc. In a broad sense, the fluid is not limited to liquid, and all objects flowing according to a certain physical law are called fluid, such as: smoke, debris flow, storm, quicksand, and the like. The existence of fluid is very common, the motion phenomenon seems to be simple, but the physical laws of the internal domains of the fluid are very complex when the fluid moves. The whole movement process of the fluid comprises the following steps: the convection, diffusion, turbulence and surface tension of the fluid. In the field of physics, the discipline of fluid mechanics is mainly to study the mechanical law of fluid motion, which belongs to a branch of mechanics, and mainly studies the interaction and flow law of the static state or relative motion state of fluid. The computational fluid mechanics is mainly expanded by methods of partial differential equation calculation, numerical analysis and the like, and physical phenomena and laws in the traditional fluid mechanics are expressed by using a numerical method. Meanwhile, the fluid motion can be predicted by using numerical analysis and calculation in computational fluid dynamics, so that a plurality of practical problems can be solved, and a large number of applications exist in a plurality of current engineering fields.
Although computational fluid dynamics also solves the problem of fluid simulation in the aspect of researching fluid numerical laws, in some application fields, such as movie animation works or game animations, visual effects in science fiction films, interactive games, virtual reality technologies, even media arts, and computational fluid dynamics methods, some problems cannot be perfectly solved, and in the application fields, the generation display effect of fluid and the artistry of fluid simulation are emphasized. In the field of computer animation, the subject of fluid simulation is also receiving increasing attention from researchers. Computer animation is a technology for generating a series of animations mainly by assistance of a computer. The animation generated by the method not only accords with the physical law in real life, but also has certain artistic creativity. The fluid simulation method combines some numerical methods in computational fluid mechanics, and generates a series of realistic fluid animations by using the computational speed of a high fluid numerical equation and a parallel or other acceleration strategy under the assistance of a computer so as to meet the corresponding engineering application requirements.
Fluid animation is a complex problem, solving fluid dynamics is considered one of the most challenging problems in mathematics, but at the same time, the aesthetic appeal of its complexity has affected many developers and researchers in various fields, including in computer graphics, where physics-based animation is a simulation of natural phenomena such as fire, smoke, rain, or wind. It can simulate various types of materials such as solids, water, gas, and even fabrics or hair. With the development of computer technology, the requirements of related application fields on fluid animation are higher and higher, the correctness of the fluid motion trend and the diversity of fluid interaction phenomena are ensured, and meanwhile, a higher simulation rate is required to ensure the instantaneity, which also puts higher demands on researchers.
Disclosure of Invention
The invention provides a computer simulation method of self-adaptive particle fluid, which can automatically adjust the radius of fluid particles to adapt to the required resolution simulation, quickens the efficiency of fluid simulation and simultaneously still provides abundant fluid details, and is described in detail as follows:
a method of computer simulation of an adaptive particle fluid, the method comprising:
calculating the distance of each fluid particle from the surface of the fluid in the whole fluid simulation domain, and calculating the optimal radius of each particle according to the distance;
according to the optimal radius, carrying out particle splitting operation or particle merging operation on all particles in the fluid domain to obtain updated particles;
based on the updated particles, vortex amount constraint is added, and the virtual particles of the range boundary are constructed by using a boundary virtual particle self-adaptive method to control the boundary of the fluid so as to improve detail simulation;
the latest position and velocity of all particles in the entire fluid simulation domain are constructed.
Performing a particle splitting operation or a particle merging operation on all particles in the fluid domain to obtain updated particles specifically includes:
analyzing the relative relation, and if the relative relation of the particles is more than or equal to 1.5, determining that the particles are large particles; if the particle size is less than or equal to 0.5, the particles are small; between 0.5 and 1.5, then suitable particles;
and (3) carrying out particle splitting operation on the large particles and carrying out particle merging operation on the small particles.
Further, the particle splitting operation performed on the large particles specifically comprises:
N＝[Ci]
Anew＝Ai
wherein N is the number of particle divisions, mnewMass of particles generated for new fragmentation, AnewOther respective physical quantities of the new particles, mrelIs the mass of the primary particle, AiOther physical quantities of the original particle.
The particle merging operation on the small particles specifically comprises the following steps:
wherein the content of the first and second substances,
and AnRespectively representing the mass of the small particles before combination and other respective physical quantities, mnewIs the mass of the new particle after combination, AnewAre other individual physical quantities of the new particles after combination.
The method for controlling the boundary of the fluid by using the virtual particles of the boundary virtual particle adaptive method to construct the virtual particles of the range boundary specifically comprises the following steps:
wherein the content of the first and second substances,
representing the distance of the escaping fluid particles from the set range of the boundary, k is a self-defined coefficient, Fi boundaryIs the boundary resistance experienced by the escaping fluid particles i.
The technical scheme provided by the invention has the beneficial effects that:
1. the method has higher efficiency in the searching process of the neighborhood particles during fluid calculation, and also improves the calculation speed in the calculation process of an SPH (smooth particle fluid dynamics) method;
2. from the final comparison of simulation experiments, it is found that the simulation of the fluid particles is not adversely affected by the present invention. Therefore, the invention obviously improves the simulation efficiency of the fluid particle method on the premise of ensuring that the simulation effect is not obviously influenced.
Drawings
FIG. 1 is a flow chart of a method of computer simulation of an adaptive particle fluid;
FIG. 2 is a time-consuming comparison of a particle search algorithm;
FIG. 3 is a comparison graph of the time consumption of the adaptive particle algorithm.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in further detail below.
In order to achieve the above object, the present invention provides a computer simulation method of an adaptive particle fluid, referring to fig. 1, the method comprising the steps of:
101: calculating the distance of each fluid particle from the surface of the fluid in the whole fluid simulation domain, and calculating the optimal radius of each particle according to the distance;
102: according to the optimal radius, carrying out particle splitting operation or particle merging operation on all particles in the fluid domain to obtain updated particles;
103: based on the updated particles, vortex amount constraint is added, and the virtual particles of the range boundary are constructed by using a boundary virtual particle self-adaptive method to control the boundary of the fluid so as to improve detail simulation;
104: the latest position and velocity of all particles in the entire fluid simulation domain are constructed.
Example 2
The scheme in example 1 is further described below with reference to specific examples and calculation formulas, which are described in detail below:
step S0101: the distance from the fluid surface is estimated for each fluid particle i using a particle level set method, as shown in equation (1), equation (2), equation (3), and equation (4).
Wherein x isjAnd rjRespectively the position and the radius of the particle j,
is the average position of the fluid particles in the neighborhood of particle i,
is the average radius in the neighborhood of the point,
is to calculate an initial value of a distance level set function, h represents the range of a neighborhood h, WijFor coefficients, W is the kernel function in the SPH method.
Step S0102: calculating an initial estimate of the distance of a particle near the surface from the surface of the fluid by a step size
As shown in equation (5).
Wherein the content of the first and second substances,
is a minimum value of the calculated distance, which, when less than the minimum value, is defined as the minimum value, and, as such,
and (4) defining the maximum value when the calculation result is larger than the maximum value, wherein r is the radius of the particle.
Step S0103: a minor modification was made using equation (6) to calculate the adaptive particle size.
Wherein, dist (x)i-xj) Denotes the particle xiAnd xjThe distance between the two or more of the two or more,
the distance of a neighboring particle, which is particle i, from the surface of the fluid.
Step S0104: the optimal radius of each particle is calculated by the distance of each particle from the surface according to the fluid adaptive consensus.
Step S0201: the optimal mass of each particle i is calculated as shown in equation (7).
Wherein m isbaseIs the mass of the smallest particle. diIs the radius of the smallest particle. And n is a user-defined constant and represents the corresponding relation between the maximum particles and the minimum particles in the fluid domain, and the maximum fluid particles used in the adaptive fluid simulation domain can be determined through n.
Step S0202: due to the movement of the particles, after the calculation of each frame is finished, each particle needs to be classified according to the current position of each particle, and the relative relationship is calculated by the formula (8).
Wherein the content of the first and second substances,
representing the actual mass of the current particle i,
represents the optimal mass of the current particle i at the position, which is calculated in equation (7).
Step S0203: by a pair relative relationship CiThe classification rule defining the following particles is shown in formula (9).
If the relative relationship of the particles is more than or equal to 1.5, defining the particles as large particles; if the relative relationship of the particles is less than or equal to 0.5, defining the particles as small particles; a particle is defined as a suitable particle when the relative relationship of the particles is between 0.5 and 1.5.
Step S0204: the large particles are subjected to a particle splitting operation according to the above particle classification rule, as shown in equations (10), (11) and (12). The small particles are simultaneously subjected to a particle combination operation as shown in the formulas (13), (14) and (15).
N＝[Ci](10)
Anew＝Ai(12)
Wherein N is the number of particle divisions, mnewMass of particles generated for new fragmentation, AnewOther respective physical quantities (kept the same as before the splitting, i.e. pressure, density, acceleration, velocity, viscosity coefficient, surface tension coefficient) for the new particles.
Wherein the content of the first and second substances,
and AnRespectively representing the mass of the small particles before combination and other physical quantities (i.e. pressure, density, acceleration, velocity, viscosity coefficient, surface tension coefficient), mnewIs the mass of the new particle after merging. A. thenewAre other individual physical quantities (i.e. pressure, density, acceleration, velocity, viscosity coefficient, surface tension coefficient) of the new particles after merging. In a fluid simulation frame, new particle masses and properties are calculated.
Wherein x isnAnd mnRespectively representing the original position and the original mass of the small particles before combination, mnewAnd xnewRespectively representing the mass and position coordinates of the new particle after merging.
Step S0205: when the attributes of the particles are calculated, the attribute of each fluid particle is calculated by interpolation of the particles in the neighborhood, and the resource consumption of searching is reduced by using a tree-shaped particle searching algorithm.
Step S0301: the vorticity of each phase fluid particle in the mixed model of the multiphase fluid is calculated as shown in equation (16).
Wherein u represents the velocity of the fluid particles, mjMass of a particle in the neighborhood of particle i, αkIs a coefficient of riIs the radius of the particle i, rjThe radius of the neighborhood particle of particle i.
Step S0302: setting a unit vector of a vorticity field center to be N, wherein
The direction of the vector points from the portion where the vorticity field is low to the portion where the vorticity field is high. The calculation of the eddy force is shown in equation (17).
Wherein the content of the first and second substances,krepresenting the vorticity coefficient of each phase fluid particle, the total vorticity coefficient of the mixed particles in the fluid mixing model isi＝∑kαk kIn fluid simulation, Fi vortivityCan be used to calculate the vorticity inside discrete particles, and the fluid simulation details can be added in the calculation of the SPH method by fusing the vorticity.
Step S0303: different boundary particle sizes are used to set boundary virtual particles for the fluid simulation domain. If no fluid particles are present at the fluid boundary, no boundary dummy particles are set. In order to simplify the calculation process, the boundary virtual particles with fixed positions are still used although the sizes of the fluid particles are continuously changed in the flowing process of the fluid.
Step S0304: the fluid particles may penetrate the physical boundary with a certain possibility, and after penetrating the virtual particle boundary, the fluid particles may be forced away from the fluid domain, so that the fluid particles may escape and may not enter the fluid. The situation of fluid particle penetration boundary was calculated using the spring resistance model, as shown in equation (18).
Wherein the content of the first and second substances,
the distance representing the distance between the escaping fluid particles and the boundary setting range is a vector perpendicular to the boundary range, k is a self-defined coefficient, Fi boundaryIs the boundary resistance experienced by the escaping fluid particles i.
Step S0401: the position and velocity of the fluid particles are updated using a time step integration method as shown in equations (19), (20), (21), (22).
Where Δ t represents the time interval, fαRepresents an external force, csα are each physical quantity of the calculated resultant force in the SPH method, and the method can ensure the stability of the calculated value in the time integration calculation process.
In the method of time integration, a frog leap method is used. The core idea is to assume that the motion of the fluid particles is a uniform acceleration motion within a very short time interval. Assuming that the current time interval is Δ t and the current time is t, the acceleration at the next time t +1/2 Δ t is obtained as the average velocity of the fluid particles in the next time period according to the velocity at the time t-1/2 Δ t and the acceleration a at the current time. Thereby, the displacement of the fluid particles is calculated, and the positions of the fluid particles are updated.
Wherein the content of the first and second substances,
and
respectively the velocity and position of the particle i at time t.
The present invention proposes an improved fluid particle adaptation method. The method is based on the basic method of the SPH, the fixed size of the fluid particles in the basic method of the SPH is changed into a method of simulating by using the variable size of the fluid particles, namely, the radius of the fluid simulation particles is automatically changed in the simulation process. Since the radius of the particle is determined by other physical quantities, mainly the distance of the particle from the liquid level, in each particle simulation frame, the distance of the particle from the fluid surface particle is calculated by using an improved particle level set method. Meanwhile, in order to process the neighborhood problem of calculating the fluid particles with variable radius, a tree neighborhood search algorithm is used for increasing the neighborhood calculation rate. Compared with the basic SPH method, the method has faster simulation efficiency in a fluid simulation domain simulating the same surface resolution by realizing the fluid particle radius self-adaptive algorithm.
Example 3
The following experiments were performed to verify the feasibility of the protocols of examples 1 and 2, as described in detail below:
as can be seen in fig. 2 and table 1, as the number of particles increases, the search time of the tree search algorithm is significantly better than that of the global matching search algorithm. In terms of memory consumption, the global matching search algorithm is used to save more memory, and the tree search algorithm occupies slightly more memory.
Table 1 search algorithm memory usage table
In summary, if a fluid domain simulation with a small fluid particle number is to be simulated, the efficiency of the particle search algorithm using global matching may be better, but if the simulated particle number is large, most of the time, the invention is to simulate at least ten thousand particle numbers, so the tree search algorithm is a more suitable method.
As can be seen from table 2, when the number of particles is small, the time consumed by the adaptive particles is slightly longer than the time consumed by the SPH, and the efficiency of the adaptive particles is higher as the number of particles increases. As can be seen from the data in fig. 3, the CPU consumption for the simulation is higher when the simulation incorporates the method.
TABLE 2 adaptive particle Algorithm memory usage List
Those skilled in the art will appreciate that the drawings are only schematic illustrations of preferred embodiments, and the above-described embodiments of the present invention are merely provided for description and do not represent the merits of the embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (5)
1. A method for computer simulation of an adaptive particle fluid, the method comprising:
calculating the distance of each fluid particle from the surface of the fluid in the whole fluid simulation domain, and calculating the optimal radius of each particle according to the distance;
according to the optimal radius, carrying out particle splitting operation or particle merging operation on all particles in the fluid domain to obtain updated particles;
based on the updated particles, vortex amount constraint is added, and the virtual particles of the range boundary are constructed by using a boundary virtual particle self-adaptive method to control the boundary of the fluid so as to improve detail simulation;
the latest position and velocity of all particles in the entire fluid simulation domain are constructed.
2. The method according to claim 1, wherein the performing a particle splitting operation or a particle merging operation on all particles in the fluid domain to obtain updated particles specifically comprises:
analyzing the relative relation, and if the relative relation of the particles is more than or equal to 1.5, determining that the particles are large particles; if the particle size is less than or equal to 0.5, the particles are small; between 0.5 and 1.5, then suitable particles;
and (3) carrying out particle splitting operation on the large particles and carrying out particle merging operation on the small particles.
3. The method for computer simulation of an adaptive particle fluid according to claim 2, wherein the particle splitting operation performed on large particles specifically comprises:
N＝[Ci]
Anew＝Ai
wherein N is the number of particle divisions, mnewMass of particles generated for new fragmentation, AnewOther respective physical quantities of the new particles, mrelAs mass of primary particles, AiAs primary particlesOther respective physical quantities.
4. The method for computer simulation of an adaptive particle fluid according to claim 2, wherein the particle merging operation on the small particles is specifically:
wherein the content of the first and second substances,
and AnRespectively representing the mass of the small particles before combination and other respective physical quantities, mnewIs the mass of the new particle after combination, AnewAre other individual physical quantities of the new particles after combination.
5. The method for computer simulation of an adaptive particle fluid according to claim 1, wherein the boundary control of the fluid by using the boundary virtual particle adaptive method to construct virtual particles at the boundary of the range is specifically:
wherein the content of the first and second substances,
representing the distance of the escaping fluid particles from the set range of the boundary, k is a self-defined coefficient, Fi boundaryIs the boundary resistance experienced by the escaping fluid particles i.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102819650A (en) * | 2012-08-16 | 2012-12-12 | 同济大学 | Computational simulation method of flow slide catastrophe of rock and soil material |
| KR101549983B1 (en) * | 2014-02-28 | 2015-09-03 | 김종원 | The self-adaptive method for inserting/detecting a forensic mark using particle swarm optimization in the multiple transform domain and the apparatus thereof |
| CN109117508A (en) * | 2018-07-13 | 2019-01-01 | 北京大学 | It is a kind of for simulating the mesh free numerical computation method of metal cold spraying |
| CN109344450A (en) * | 2018-09-07 | 2019-02-15 | 中山大学 | Fluid sets analogy method and system based on PBF |
| CN110929450A (en) * | 2019-11-22 | 2020-03-27 | 北京航空航天大学 | Realistic turbulence simulation method based on SPH |
-
2020
- 2020-04-16 CN CN202010300684.2A patent/CN111581875A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102819650A (en) * | 2012-08-16 | 2012-12-12 | 同济大学 | Computational simulation method of flow slide catastrophe of rock and soil material |
| KR101549983B1 (en) * | 2014-02-28 | 2015-09-03 | 김종원 | The self-adaptive method for inserting/detecting a forensic mark using particle swarm optimization in the multiple transform domain and the apparatus thereof |
| CN109117508A (en) * | 2018-07-13 | 2019-01-01 | 北京大学 | It is a kind of for simulating the mesh free numerical computation method of metal cold spraying |
| CN109344450A (en) * | 2018-09-07 | 2019-02-15 | 中山大学 | Fluid sets analogy method and system based on PBF |
| CN110929450A (en) * | 2019-11-22 | 2020-03-27 | 北京航空航天大学 | Realistic turbulence simulation method based on SPH |
Non-Patent Citations (6)
| Title |
|---|
| SHAO XUQIANG 等: ""Synthesizing Solid-induced Turbulence for Particle-based Fluids"", 《2013 INTERNATIONAL CONFERENCE ON COMPUTER-AIDED DESIGN AND COMPUTER GRAPHICS》 * |
| WINCHENBACH, R 等: ""Infinite Continuous Adaptivity for Incompressible SPH"", 《ACM TRANSACTIONS ON GRAPHICS》 * |
| XIANG,YING 等: ""Vorticity constraints in multiphase flow mixture models"", 《10TH INTERNATIONAL CONFERENCE ON GRAPHICS AND IMAGE PROCESSING (ICGIP)》 * |
| 朱娟: ""实时光滑粒子流体模拟及渲染关键技术研究"", 《中国优秀硕士学位论文全文数据库（基础科学辑）》 * |
| 朱晓临 等: ""SPH 流固耦合模拟自适应采样固壁虚粒子边界处理方法"", 《图学学报》 * |
| 朱鉴 等: ""一种基于自适应光滑长度的SPH液体模拟方法"", 《HTTPS://DOI.ORG/10.19734/J.ISSN.1001 -3695.2019.06.0172》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112580770A (en) * | 2020-12-21 | 2021-03-30 | 西安交通大学 | Particle method-based variable particle size splitting method |
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Application publication date: 20200825 |