WO2019010859A1 - 一种高致密度离散颗粒多相体系的建模方法 - Google Patents

一种高致密度离散颗粒多相体系的建模方法 Download PDF

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WO2019010859A1
WO2019010859A1 PCT/CN2017/106883 CN2017106883W WO2019010859A1 WO 2019010859 A1 WO2019010859 A1 WO 2019010859A1 CN 2017106883 W CN2017106883 W CN 2017106883W WO 2019010859 A1 WO2019010859 A1 WO 2019010859A1
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particle
model
particles
modeling
volume
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季忠
刘韧
张纪芝
生培瑶
邹方坤
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山东大学
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Priority to US16/314,502 priority Critical patent/US11170144B2/en
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    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00Computer-aided design [CAD]
    • G06F30/20Design optimisation, verification or simulation
    • G06F30/23Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F2111/00Details relating to CAD techniques
    • G06F2111/10Numerical modelling

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  • the invention belongs to the field of computational materials science, and particularly relates to a mesoscale modeling method for a mesostructure of a high-density discrete particle multi-phase system, in particular to a mesoscale structure of a particle reinforced composite material, a soft material granular material, a particle accumulation material, and the like.
  • the modeling method can also be extended to short fiber reinforced composites.
  • granular materials can be regarded as random stacked structures composed of particles of different scales.
  • the interstices between the particles are typically filled with a medium such as a matrix material, interface, pores, or fluid.
  • a medium such as a matrix material, interface, pores, or fluid.
  • the random placement method also known as the Monte Carlo method, assigns the position of the particles one by one in a given time and space in a certain order. Once the allocation is successful, the placement is successful and the particles are fixed at the assigned position.
  • the Monte Carlo method is highly efficient, but the bulk density of the particles is very low. The later the release, the smaller the selection space of random points, the more difficult it is to put, so it is difficult to put all the particles into the limited model space, and there are often some materials in the engineering practice. However, it is not possible to generate a mesoscale structural model of the material.
  • Voronoi method can easily generate densely distributed random particles, it cannot control the gradation of the particles, that is, it is impossible to control the volume ratio or weight ratio of particles of different particle sizes.
  • the Voronoi method is mainly suitable for convex particles, and is not suitable for the case where the concave shape, or the convex shape and the concave shape are simultaneously distributed.
  • Chinese patent document 201510345395.3 discloses a modeling method for the mesostructure of discrete phase reinforced composite materials, which firstly generates randomly distributed particles in a large space, then divides the surface of the particles into shell elements and simulates the particles. Free fall motion, allowing it to fall into a smaller space, resulting in a highly dense particle packing structure. Finally, based on the shell element of the particle surface, the solid element of the particle is generated, and finally the finite element model of the particle is obtained.
  • the method can solve the problem that the bulk density of the particle phase is insufficient in the random injection method, and the Voronoi method cannot be used for the concave particle and the particle gradation cannot be controlled, but the upper surface of the deposit body may not be flat after the drop, and the calculation of the falling process is also It is time consuming and requires two meshing of shell and solid elements, which is costly to calculate. In addition, this method works very well when all particles are inside a particular space, but special handling is required when the particles cross the model boundary.
  • the object of the present invention is to provide a modeling method for a high-density discrete particle multiphase system.
  • the method can model the high-density microstructure of the particle-reinforced composite material, the soft material-based granular material, the particulate-deposited material, the short-fiber reinforced composite material, etc. by the principle of shrinkage, which is described in Chinese Patent Document 201510345395.3
  • the advantages of the invention are characterized by simple method, wide adaptability and high calculation efficiency.
  • a modeling method for a high-density discrete particle multiphase system comprising the following steps:
  • the volume of the particles in each size range is prepared, that is, the volume of the particle phase in the range of i to j is set to be V (ij) ;
  • step (3) repeating step (3) to generate a compact model of the particles in all size ranges;
  • the compact model of each particle in the step (4) is volume-expanded, that is, the size of the compact model of each particle is enlarged by 1/ ⁇ times, and the volume is also followed. Amplification 1/ ⁇ times, thereby expanding the compact model of each particle to a normal size model conforming to the predetermined gradation in step (2), and the obtained particle distribution model is the desired high-density particle structure model.
  • the amount of expansion of the compact model can obtain the particle bulk density consistent with the real material, and solve the problem that the high-density model cannot be obtained by random placement and the like.
  • FIG. 1 is a schematic diagram of a compact model for randomly generating particles in a modeling space
  • FIG. 2 is a finite element mesh diagram of a high density model obtained by expanding and redistributing each particle in a compact model
  • Figure 3 is a schematic diagram comparing the compact model with the expanded model.
  • the 1-compact particle model, the 2-model boundary, the finite element mesh of the normal-sized particles obtained after 3-expansion, and the 4-expansion normal-size particle model are included in the 1-compact particle model, the 2-model boundary, the finite element mesh of the normal-sized particles obtained after 3-expansion, and the 4-expansion normal-size particle model.
  • the present invention proposes a modeling method for a high-density discrete particle multi-phase system. , including the following steps:
  • the volume of the particles in each size range is prepared, that is, the volume of the particle phase in the range of i to j is set to be V (ij) ;
  • step (3) repeating step (3) to generate a compact model of the particles in all size ranges;
  • the compact model of each particle in the step (4) is volume-expanded, that is, the size of the compact model of each particle is enlarged by 1/ ⁇ times, and the volume is also followed. Amplification 1/ ⁇ times, thereby expanding the compact model of each particle to a normal size model conforming to the predetermined gradation in step (2), and the obtained particle distribution model is the desired high-density particle structure model.
  • a void between the particles serves as a second phase, and thereby a multiphase model is produced.
  • step (5) the expansion method of each particle compaction model comprises the following steps:
  • step (1) dividing the boundary of the modeling space in step (1) (referred to as the model boundary) into a finite element mesh, and assigning material properties;
  • step (4) dividing the particles described in step (4) into a finite element mesh and assigning material properties
  • the model boundary is a temperature independent rigid material.
  • the particles are thermoelastic materials or thermoelastic materials, and further preferably, the particles are thermoelastic materials.
  • each particle model is divided into physical units, and the model boundary is an entity element or a shell unit.
  • the contact mode is a face-to-face contact, or a point-to-face contact, or a point-to-point contact.
  • the high-density discrete particle multi-phase system is a particle-reinforced composite material, a soft material-based particulate material (such as a soft material-based particulate material composed of concrete or a debris flow), a particulate-deposited material, or a short-fiber reinforced composite material.
  • the method for obtaining a plurality of particle models in the range of i ⁇ j ⁇ includes the following steps:
  • step b judging whether the ball generated in step a interferes with the particles that have been generated around, if interference occurs, delete the generated ball, and repeat step a;
  • the shape of the generated model is a spherical or ellipsoidal shape or other spherical shape.
  • the particle model is directly generated by the mathematical equation, and the step c is omitted.
  • the expansion method of each particle compaction model can also be solved by a numerical method such as discrete element.
  • the application of the above modeling method in the modeling of fiber reinforced composite materials is characterized in that the ball generated by random points in the above modeling method is replaced by a column, and a fiber model is generated from the column.
  • a modeling method for a high-density two-dimensional discrete particle multiphase system comprising the following steps:
  • the finite element method is used to apply a temperature or a heat flow load to the compact model of each particle in the step (4) to cause an area expansion, that is, a compact model of each particle.
  • the size of the particles i ⁇ ⁇ ⁇ j ⁇ ⁇ is a comparison of the model of the compaction model and the model after expansion, 1 is the particle compaction model, 4 It is a normal size particle model after expansion.
  • the area sum of each particle also expands by 1 ⁇ ⁇ times, from S (ij) ⁇ ⁇ to S (ij) , thereby expanding the contraction model of each particle to the normal size model conforming to the predetermined gradation in step (2). .
  • the particle distribution model obtained at this time is the high density particle structure model obtained.
  • a void between the particles serves as a second phase, and thereby a multiphase model is produced.
  • the method for obtaining a plurality of particle models in the range of i ⁇ j ⁇ includes the following steps:
  • step b judging whether the circle generated in step a interferes with the particles that have been generated around, if interference occurs, delete the generated circle, and repeat step a;
  • a modeling method for a three-dimensional mesostructure of a high-density particle-reinforced composite material comprising the following steps:
  • the volume of the particles in each size range is prepared, that is, the volume of the particle phase in the range of i to j is set to be V (ij) ;
  • a method for obtaining a plurality of particle models in the range of i ⁇ j ⁇ includes the following steps:
  • step b judging whether the ball generated in step a interferes with the particles that have been generated around, if interference occurs, delete the generated ball, and repeat step a;
  • step (3) repeating step (3) to generate a compact model of the particles in all size ranges;
  • the compact model of each particle in the step (4) is volume-expanded, that is, the size of the compact model of each particle is enlarged by 1/ ⁇ times, and the volume is also followed. Amplification 1/ ⁇ times, thereby expanding the compact model of each particle to a normal size model conforming to the predetermined gradation in step (2), and the obtained particle distribution model is the desired high-density particle structure model.
  • the expansion method of each particle compact model specifically includes the following steps:
  • step (1) Divide the boundary of the modeling space (called the model boundary) in step (1) into a finite element mesh, and give it no temperature. Closed body material properties;
  • step (4) dividing the particles described in step (4) into a finite element mesh and imparting properties to the thermoelastic material
  • the particle distribution model obtained at this time is the high density particle structure model obtained.
  • a void between the particles serves as a second phase, and thereby a multiphase model is produced.

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Abstract

一种高致密度离散颗粒多相体系的建模方法,包括以下步骤:确定模型边界;拟定所述模型中各尺寸范围内的颗粒相的体积和;生成所有尺寸范围内的颗粒模型的紧缩模型;将紧缩模型中各颗粒进行膨胀;得到高致密度的颗粒堆积模型; 该方法可用于颗粒增强复合材料、软物质系颗粒材料、颗粒堆积材料等细观结构的建模,也可扩展应用至短纤维增强复合材料等; 方法解决了颗粒与模型边界存在交叉时的建模问题,能够应用于边缘经过机加工的复合材料试样的建模与分析。

Description

一种高致密度离散颗粒多相体系的建模方法 技术领域
本发明属于计算材料学领域,具体涉及一种高致密度离散颗粒多相体系细观结构的中尺度建模方法,尤其涉及颗粒增强复合材料、软物质系颗粒材料、颗粒堆积材料等细观结构的建模方法,也可扩展应用至短纤维增强复合材料。
背景技术
在颗粒增强复合材料以及颗粒组成的软物质体系如混凝土和泥石流中,材料的性能如力学性能和传输性能,严重依赖于材料的细观结构。因此,针对这类材料的结构进行多尺度建模,对于材料的性能分析和材料结构的优化设计,均具有非常重要的意义。
颗粒材料作为复杂的多尺度离散介质,可以视为由不同尺度的颗粒组成的随机堆积结构。颗粒间的空隙通常充满着基体材料、界面、孔隙、或流体等介质。正确描述离散颗粒多相体系的性能,应首先正确描述该体系的多尺度结构,尤其是颗粒的堆积结构。
对于随机堆积的颗粒,目前主要采用的建模方法有随机投放法和Voronoi法等。随机投放法亦称蒙特卡洛法,它按照一定的次序在给定的时间和空间内逐一分配颗粒的位置,一旦分配成功,即为投放成功,该颗粒即被固定在所分配的位置上。蒙特卡洛法的执行效率很高,但颗粒的堆积密度却很低。越是投放的后期,随机点的选取空间就越小,投放的难度也越大,从而难以将所有的颗粒都投放到有限的模型空间中,经常发生工程实际中存在某种配比的材料,却无法生成该材料的细观尺度结构模型的情况。而Voronoi法虽然可以很容易地生成致密分布的随机颗粒,却无法控制颗粒的级配,即无法控制不同粒径的颗粒的体积比或重量比。另外,Voronoi法主要适合于凸形颗粒,而不适用于凹形、或凸形和凹形同时分布的情况。
中国专利文件201510345395.3公布了一种离散相增强复合材料细观结构的建模方法,该方法首先在一较大的空间中生成随机分布的颗粒,然后把颗粒表面划分为壳单元,并模拟颗粒的自由落体运动,使之下落到一个较小的空间中,从而生成高致密度的颗粒堆积结构。最后,根据颗粒表面的壳单元,生成颗粒的实体单元,最终得到颗粒的有限元模型。该方法能够解决随机投放等方法颗粒相的堆积密度不足,以及Voronoi法不能用于凹形颗粒和不能控制颗粒级配等问题,但下落之后堆积体的上表面可能不平整,下落过程的计算也较为耗时,并且需要壳单元和实体单元两次网格划分,计算成本高。另外,当所有颗粒均在某个特定空间内部时,该方法非常有效,但是,当颗粒与模型边界存在交叉时,则需要进行特殊处理。
发明内容
针对现有技术的不足,本发明的目的是提供一种高致密度离散颗粒多相体系的建模方法, 该方法通过缩胀原理,可以对颗粒增强复合材料、软物质系颗粒材料、颗粒堆积材料、短纤维增强复合材料等,进行高致密度细观结构建模,该方法具备中国专利文件201510345395.3所述发明的优势,并具有方法简单、适应性广、计算效率高等特点。
为实现上述目的,本发明的技术方案如下:
一种高致密度离散颗粒多相体系的建模方法,包括以下步骤:
(1)拟定模型的轮廓形状和尺寸,即确定建模空间及其边界;
(2)根据预定的级配,拟定各尺寸范围内的颗粒的体积和,即设定i~j尺寸范围内的颗粒相的体积和为V(i-j)
(3)在所述建模空间内,随机获得在i×α~j×α尺寸范围内的若干个颗粒模型,其中,α<1,使各个颗粒模型的体积和为V(i-j)×β,各个颗粒均与周围的颗粒之间不发生干涉,由此获了i~j尺寸范围内的各颗粒的紧缩模型;
(4)重复步骤(3),生成所有尺寸范围内的颗粒的紧缩模型;
(5)在步骤(1)中所述的建模空间中,将步骤(4)中各颗粒的紧缩模型进行体积膨胀,即将各颗粒的紧缩模型的尺寸放大1/α倍,体积也随之放大1/β倍,从而将各颗粒的紧缩模型膨胀至符合步骤(2)中预定级配的正常尺寸模型,此时得到的颗粒分布模型即为所求的高致密度颗粒结构模型。
与现有技术相比,本发明的技术方案的有益技术效果为:
(1)由于各颗粒都是在随机点生成的,因此能够保证颗粒空间分布的均匀性。
(2)由于首先生成的是各颗粒的紧缩模型,因此,容易产生符合条件的随机点,解决了现有随机投放法长时间找不到或根本找不到符合条件的随机点的问题。
(3)能够按预定的级配生成各种形状颗粒的紧缩模型,解决了Voronoi法不能用于凹形颗粒和不能控制颗粒级配等问题。
(4)解决了颗粒与模型边界存在交叉时的建模问题,能够应用于边缘经过机加工的复合材料试样的建模与分析。
(5)将紧缩模型膨胀量,可以得到与真实材料一致的颗粒堆积密度,解决了随机投放等方法不能获得高密度模型的问题。
附图说明
构成本发明的一部分的说明书附图用来提供对本发明的进一步理解,本发明的示意性实施例及其说明用于解释本发明,并不构成对本发明的不当限定。
图1是在建模空间内随机生成各颗粒的紧缩模型的示意图;
图2是紧缩模型中的各颗粒膨胀并重新分布后得到的高致密度模型的有限元网格图;
图3是紧缩模型与膨胀后模型的比较示意图。
其中,1-紧缩颗粒模型,2-模型边界,3-膨胀后得到的正常尺寸颗粒的有限元网格,4-膨胀后的正常尺寸颗粒模型。
具体实施方式
应该指出,以下详细说明都是示例性的,旨在对本发明提供进一步的说明。除非另有指明,本文使用的所有技术和科学术语具有与本发明所属技术领域的普通技术人员通常理解的相同含义。
需要注意的是,这里所使用的术语仅是为了描述具体实施方式,而非意图限制根据本发明的示例性实施方式。如在这里所使用的,除非上下文另外明确指出,否则单数形式也意图包括复数形式,此外,还应当理解的是,当在本说明书中使用术语“包含”和/或“包括”时,其指明存在特征、步骤、操作和/或它们的组合。
正如背景技术所介绍的,现有技术中关于随机堆积的颗粒的建模方法存在一定的不足,为了解决如上的技术问题,本发明提出了一种高致密度离散颗粒多相体系的建模方法,包括以下步骤:
(1)拟定模型的轮廓形状和尺寸,即确定建模空间及其边界;
(2)根据预定的级配,拟定各尺寸范围内的颗粒的体积和,即设定i~j尺寸范围内的颗粒相的体积和为V(i-j)
(3)在所述建模空间内,随机获得在i×α~j×α尺寸范围内(包括i×α和j×α尺寸)的若干个颗粒模型,其中,α<1,使各个颗粒模型的体积和为V(i-j)×β,各个颗粒均与周围的颗粒之间不发生干涉,由此获了i~j尺寸范围内的各颗粒的紧缩模型;
(4)重复步骤(3),生成所有尺寸范围内的颗粒的紧缩模型;
(5)在步骤(1)中所述的建模空间中,将步骤(4)中各颗粒的紧缩模型进行体积膨胀,即将各颗粒的紧缩模型的尺寸放大1/α倍,体积也随之放大1/β倍,从而将各颗粒的紧缩模型膨胀至符合步骤(2)中预定级配的正常尺寸模型,此时得到的颗粒分布模型即为所求的高致密度颗粒结构模型。
对于颗粒增强复合材料或软物质系颗粒材料等,颗粒之间的空隙作为第二相,并由此生成多相模型。
其中,步骤(5)中,各颗粒紧缩模型的膨胀方法,包括如下步骤:
a、将步骤(1)中建模空间的边界(称为模型边界)划分有限元网格,并赋予材料属性;
b、将步骤(4)中所述的颗粒划分有限元网格,并赋予材料属性;
c、定义颗粒之间以及颗粒与建模空间的边界之间的接触判断方式,使之不发生相互穿透;
d、用有限元方法,对颗粒施加温度或热流载荷,使之产生热膨胀;热膨胀1/α倍后,尺寸由为i×α~j×α的颗粒变为尺寸i~j,各颗粒的体积和也随之膨胀1×β倍,由V(i-j)×β变为V(i-j)
优选的,步骤a中,模型边界为与温度无关的刚性材料。
优选的,步骤b中,颗粒为热弹性材料或热弹塑性材料,进一步优选的,所述颗粒为热弹性材料。
优选的,步骤a~b中,将各个颗粒模型划分为实体单元,模型边界为实体元或壳单元。
优选的,步骤c中,所述接触方式为面-面接触,或点-面接触,或点-点接触。
优选的,步骤(3)中,所述α=0.5~0.9,β=α3,经验证,选取的α=0.5~0.9能够使得模型具有更加真实的特点。
优选的,所述高致密度离散颗粒多相体系为颗粒增强复合材料、软物质系颗粒材料(如混凝土或泥石流组成的软物质系颗粒材料)、颗粒堆积材料或短纤维增强复合材料。
优选的,步骤(1)~(3)中,i×α~j×α尺寸范围内的若干个颗粒模型的获得方法,包括如下步骤:
a、在所述建模空间内,随机产生一个点,以该点为中心,生成一个直径在i×α~j×α尺寸范围内的球;
b、判断步骤a中所生成的球是否与周围已生成的颗粒发生干涉,若发生干涉,则删除已生成的球,并重复步骤a;
c、在所述球内生成内接多面体,任选该多面体的几个顶点,使之沿球的半径方向内移,形成内凹和外凸的表面,以得到逼近真实颗粒形状的颗粒模型;
d、判断所生成的颗粒是否与模型边界发生干涉,若发生干涉,则删除颗粒在模型边界外的部分;
e、计算步骤d中得到的颗粒模型的体积;
f、重复步骤a~e,将得到的各个颗粒模型的体积求和,得到ΣV;
g、判断ΣV是否达到V(i-j)×β,若不是,则重复步骤a~f。
步骤a中,生成的模型的形状为球形或椭球型或其他类似球形的形状。
如果颗粒的形状能够直接用数学方程描述,则在上述步骤a~c中,直接通过数学方程生成颗粒模型,并省略步骤c。
步骤(5)中,各颗粒紧缩模型的膨胀方法,也可通过离散元等数值方法进行求解。
上述建模方法在二维细观结构建模上的应用,其特点是:将上述建模方法中的体积用面积代替,将在随机点生成的球用圆代替,将内接多面体用圆内接多边形代替,β=α2
上述建模方法在纤维增强复合材料建模上的应用,其特点是:将上述建模方法中随机点生成的球用柱代替,并由柱生成纤维模型。
为了使得本领域技术人员能够更加清楚地了解本发明的技术方案,以下将结合具体的实施例和附图详细说明本发明的技术方案。
实施例1
一种高致密度二维离散颗粒多相体系的建模方法,包括以下步骤:
(1)拟定模型的轮廓形状和尺寸,即确定建模空间及其边界,如图1中的模型边界,2为模型边界;
(2)根据预定的级配,生成各颗粒的紧缩模型,如图1中各颗粒的紧缩模型,1为颗粒紧缩模型;
(3)将各颗粒的紧缩模型划分为二维平面单元,并赋予热弹性材料属性;
(4)将模型边界划分为二维平面单元,并赋予与温度无关的刚体材料属性;
(5)定义颗粒之间以及颗粒与模型边界之间的接触为面—面接触方式,使之不发生相互穿透;
(6)在步骤(1)所拟定的建模空间中,通过有限元方法,将步骤(4)中各颗粒的紧缩模型施加温度或热流载荷,使之产生面积膨胀,即将各颗粒的紧缩模型的尺寸放大1/α倍,α=0.5~0.9,面积也随之放大1/β倍。膨胀1/α倍后,尺寸为i×α~j×α的颗粒,尺寸变为i~j,如图3所示,是紧缩模型与膨胀后模型的比较示意图,1为颗粒紧缩模型,4为膨胀后的正常尺寸颗粒模型。各颗粒的面积和也随之膨胀1×β倍,由S(i-j)×β变为S(i-j),从而将各颗粒的紧缩模型膨胀至符合步骤(2)中预定级配的正常尺寸模型。
此时得到的颗粒分布模型即为所求的高致密度颗粒结构模型。对于颗粒增强复合材料或软物质系颗粒材料等,颗粒之间的空隙作为第二相,并由此生成多相模型。
步骤(1)~(2)中,i×α~j×α尺寸范围内的若干个颗粒模型的获得方法,包括如下步骤:
a、在所述建模空间内,随机产生一个点,以该点为中心,生成一个直径在i×α~j×α尺寸范围内的圆;
b、判断步骤a中所生成的圆是否与周围已生成的颗粒发生干涉,若发生干涉,则删除已生成的圆,并重复步骤a;
c、在所述圆内生成内接多边形,任选该多边形的几个顶点,使之沿圆的半径方向内移,形成内凹和外凸的表面,以得到逼近真实颗粒形状的颗粒模型;
d、判断所生成的颗粒是否与模型边界发生干涉,若发生干涉,则删除颗粒在模型边界外 的部分;
e、计算步骤d中得到的颗粒模型的面积;
f、重复步骤a~e,将得到的各个颗粒模型的面积求和,得到ΣS;
g、判断ΣS是否达到S(i-j)×β,若不是,则重复步骤a~f。
实施例2
一种高致密度颗粒增强复合材料三维细观结构的建模方法,包括以下步骤:
(1)拟定模型的轮廓形状和尺寸,即确定建模空间及其边界;
(2)根据预定的级配,拟定各尺寸范围内的颗粒的体积和,即设定i~j尺寸范围内的颗粒相的体积和为V(i-j)
(3)在所述建模空间内,随机获得在i×α~j×α尺寸范围内的若干个颗粒模型,其中,α=0.8,使各个颗粒模型的体积和为V(i-j)×β,各个颗粒均与周围的颗粒之间不发生干涉,由此获了i~j尺寸范围内的各颗粒的紧缩模型;
i×α~j×α尺寸范围内的若干个颗粒模型的获得方法,包括如下步骤:
a、在所述建模空间内,随机产生一个点,以该点为中心,生成一个直径在i×α~j×α尺寸范围内的球;
b、判断步骤a中所生成的球是否与周围已生成的颗粒发生干涉,若发生干涉,则删除已生成的球,并重复步骤a;
c、在所述球内生成内接多面体,任选该多面体的几个顶点,使之沿球的半径方向内移,形成内凹和外凸的表面,以得到逼近真实颗粒形状的颗粒模型;
d、判断所生成的颗粒是否与模型边界发生干涉,若发生干涉,则删除颗粒在模型边界外的部分;
e、计算步骤d中得到的颗粒模型的体积;
f、重复步骤a~e,将得到的各个颗粒模型的体积求和,得到ΣV;
g、判断ΣV是否达到V(i-j)×β,若不是,则重复步骤a~f。
(4)重复步骤(3),生成所有尺寸范围内的颗粒的紧缩模型;
(5)在步骤(1)中所述的建模空间中,将步骤(4)中各颗粒的紧缩模型进行体积膨胀,即将各颗粒的紧缩模型的尺寸放大1/α倍,体积也随之放大1/β倍,从而将各颗粒的紧缩模型膨胀至符合步骤(2)中预定级配的正常尺寸模型,此时得到的颗粒分布模型即为所求的高致密度颗粒结构模型。
其中,各颗粒紧缩模型的膨胀方法,具体包括如下步骤:
a、将步骤(1)中建模空间的边界(称为模型边界)划分有限元网格,并赋予与温度无 关的刚体材料属性;
b、将步骤(4)中所述的颗粒划分有限元网格,并赋予热弹性材料属性;
c、定义颗粒之间以及颗粒与建模空间的边界之间的接触为点-面接触方式,使之不发生相互穿透;
d、用有限元方法,对颗粒施加温度或热流载荷,使之产生热膨胀;热膨胀1/α倍后,尺寸由为i×α~j×α的颗粒变为尺寸i~j,各颗粒的体积和也随之膨胀1×β倍,由V(i-j)×β变为V(i-j)
此时得到的颗粒分布模型即为所求的高致密度颗粒结构模型。对于颗粒增强复合材料或软物质系颗粒材料等,颗粒之间的空隙作为第二相,并由此生成多相模型。
以上所述仅为本发明的优选实施例而已,并不用于限制本发明,对于本领域的技术人员来说,本发明可以有各种更改和变化。凡在本发明的精神和原则之内,所作的任何修改、等同替换、改进等,均应包含在本发明的保护范围之内。

Claims (10)

  1. 一种高致密度离散颗粒多相体系的建模方法,其特征是,包括以下步骤:
    (1)拟定模型的轮廓形状和尺寸,即确定建模空间及其边界;
    (2)根据预定的级配,拟定各尺寸范围内的颗粒的体积和,即设定i~j尺寸范围内的颗粒相的体积和为V(i-j)
    (3)在所述建模空间内,随机获得在i×α~j×α尺寸范围内的若干个颗粒模型,其中,α<1,使各个颗粒模型的体积和为V(i-j)×β,各个颗粒均与周围的颗粒之间不发生干涉,由此获了i~j尺寸范围内的各颗粒的紧缩模型;
    (4)重复步骤(3),生成所有尺寸范围内的颗粒的紧缩模型;
    (5)在步骤(1)中所述的建模空间中,将步骤(4)中各颗粒的紧缩模型进行体积膨胀,即将各颗粒的紧缩模型的尺寸放大1/α倍,体积也随之放大1/β倍,从而将各颗粒的紧缩模型膨胀至符合步骤(2)中预定级配的正常尺寸模型,此时得到的颗粒分布模型即为所求的高致密度颗粒结构模型。
  2. 如权利要求1所述的建模方法,其特征是:所述高致密度离散颗粒多相体系为颗粒增强复合材料、软物质系颗粒材料、颗粒堆积材料或短纤维增强复合材料。
  3. 如权利要求1所述的建模方法,其特征是:步骤(5)中,各颗粒紧缩模型的膨胀方法,包括如下步骤:
    a、将步骤(1)中建模空间的边界划分有限元网格,并赋予材料属性;
    b、将步骤(4)中所述的颗粒划分有限元网格,并赋予材料属性;
    c、定义颗粒之间以及颗粒与建模空间的边界之间的接触判断方式,使之不发生相互穿透;
    d、用有限元方法,对颗粒施加温度或热流载荷,使之产生热膨胀;热膨胀1/α倍后,尺寸由为i×α~j×α的颗粒变为尺寸i~j,各颗粒的体积和也随之膨胀1×β倍,由V(i-j)×β变为V(i-j)
  4. 如权利要求3所述的建模方法,其特征是:步骤a中,模型边界为与温度无关的刚性材料;
    步骤b中,颗粒为热弹性材料或热弹塑性材料,优选的,所述颗粒为热弹性材料;
    步骤a~b中,将各个颗粒模型划分为实体单元,模型边界为实体元或壳单元;
    步骤c中,所述接触方式为面-面接触,或点-面接触,或点-点接触。
  5. 如权利要求1所述的建模方法,其特征是:步骤(1)~(3)中,i×α~j×α尺寸范围内的若干个颗粒模型的获得方法,包括如下步骤:
    a、在所述建模空间内,随机产生一个点,以该点为中心,生成一个直径在i×α~j×α尺寸范围内的球;
    b、判断步骤a中所生成的球是否与周围已生成的颗粒发生干涉,若发生干涉,则删除已生成的球,并重复步骤a;
    c、在所述球内生成内接多面体,任选该多面体的几个顶点,使之沿球的半径方向内移,形成内凹和外凸的表面,以得到逼近真实颗粒形状的颗粒模型;
    d、判断所生成的颗粒是否与模型边界发生干涉,若发生干涉,则删除颗粒在模型边界外的部分;
    e、计算步骤d中得到的颗粒模型的体积;
    f、重复步骤a~e,将得到的各个颗粒模型的体积求和,得到ΣV;
    g、判断ΣV是否达到V(i-j)×β,若不是,则重复步骤a~f;
    优选的,步骤a中,生成的模型的形状为球形或椭球型或其他类似球形的形状;
    优选的,如果颗粒的形状能够直接用数学方程描述,则在上述步骤a~c中,直接通过数学方程生成颗粒模型,并省略步骤c。
  6. 如权利要求1所述的建模方法,其特征是:将颗粒之间的空隙作为第二相,并由此生成多相模型。
  7. 如权利要求1所述的建模方法,其特征是:步骤(3)中,所述α=0.5~0.9,β=α3
  8. 如权利要求1所述的建模方法,其特征是:步骤(5)中,各颗粒紧缩模型的膨胀方法,能够通过离散元数值方法进行求解。
  9. 权利要求1~8中任一项所述的建模方法在二维细观结构建模上的应用,其特征是:将所述建模方法中的体积用面积代替,将在随机点生成的球用圆代替,将内接多面体用圆内接多边形代替,β=α2
  10. 权利要求1~8中任一项所述的建模方法在纤维增强复合材料建模上的应用,其特征是:将所述建模方法中随机点生成的球用柱代替,并由柱生成纤维模型。
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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006259910A (ja) * 2005-03-15 2006-09-28 Fuji Xerox Co Ltd 粒子充填シミュレーション装置及び粒子充填シミュレーション方法、並びにコンピュータ・プログラム
US20100211330A1 (en) * 2009-01-13 2010-08-19 New York University Packing Properties of Particulate Compositions
WO2012131372A1 (en) * 2011-03-31 2012-10-04 Dem Solutions Limited Method and apparatus for discrete element modeling involving a bulk material
EP2509009A1 (en) * 2011-04-05 2012-10-10 Japan Agency for Marine-Earth Science and Technology Particle simulator and method of simulating particles
CN104899393A (zh) * 2015-06-19 2015-09-09 山东大学 一种离散相增强复合材料细观结构的建模方法
CN105740532A (zh) * 2016-01-28 2016-07-06 重庆交通大学 母岩及其颗粒料的二维离散元模型构建方法
CN106777807A (zh) * 2017-01-13 2017-05-31 北京航空航天大学 一种粉末冶金随机粒度分布3d有限元建模与仿真方法

Family Cites Families (28)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7353153B2 (en) * 2001-10-17 2008-04-01 Maria-Grazia Ascenzi Method and system for modeling bone structure
US7131105B2 (en) * 2003-09-19 2006-10-31 Coventor, Inc. System and method for automatic mesh generation from a system-level MEMS design
US7142297B2 (en) * 2003-10-31 2006-11-28 Synopsys Switzerland Llc Method for simulating the movement of particles
US20070165948A1 (en) * 2004-01-13 2007-07-19 Koninklijke Philips Electronic, N.V. Mesh models with internal discrete elements
DE602004026566D1 (de) * 2004-07-28 2010-05-27 Procter & Gamble Indirekter Druck von AMG
EA014375B1 (ru) * 2006-07-12 2010-10-29 Де Юниверсити Оф Квинсленд Способ прогнозирования характеристик измельчения материала в виде частиц, подвергающегося ударному воздействию
US20080099569A1 (en) * 2006-10-31 2008-05-01 Husky Injection Molding Systems Ltd. Thermal Analysis of Apparatus having Multiple Thermal Control Zones
RU2009124594A (ru) * 2006-11-29 2011-01-10 Бейкер Хьюз Инкорпорейтед (Us) Моделирование разрушения породы в условиях высокого давления методом дискретных элементов
EP2481550B1 (en) * 2007-07-02 2014-10-08 MAGMA Giessereitechnologie GmbH Method for describing the statistical orientation distribution of particles in a simulation of a mould filling process and computer software product including a software code for performing said method.
WO2009057190A1 (ja) * 2007-10-29 2009-05-07 Japan Agency For Marine-Earth Science And Technology 気象シミュレーション装置、及び、方法
US8042736B2 (en) * 2008-04-23 2011-10-25 Engineering Consultants Group, Inc. Tracking and properties control system for bulk materials
FR2934050B1 (fr) * 2008-07-15 2016-01-29 Univ Paris Curie Procede et dispositif de lecture d'une emulsion
GB2462261A (en) * 2008-07-28 2010-02-03 Fujitsu Ltd Method, apparatus and computer program for simulating behaviou r of thermodynamic systems
US9342636B2 (en) * 2008-08-13 2016-05-17 Dem Solutions Ltd Method and apparatus for simulation by discrete element modeling and supporting customisable particle properties
US8126684B2 (en) * 2009-04-10 2012-02-28 Livermore Software Technology Corporation Topology optimization for designing engineering product
US8531463B2 (en) * 2009-08-10 2013-09-10 Dem Solutions Limited Method and apparatus for discrete element modeling with a virtual geometry object
EP2317348B1 (en) * 2009-10-30 2014-05-21 Services Pétroliers Schlumberger Method for building a depositional space corresponding to a geological domain
US9122821B2 (en) * 2010-05-25 2015-09-01 Siemens Products Lifecycle Management Software Inc. Method and system for simulation of automated processes
US8554527B2 (en) * 2011-04-08 2013-10-08 Japan Agency For Marine-Earth Science And Technology Particle simulator and method of simulating particles
US9020784B2 (en) * 2012-04-27 2015-04-28 Livermore Software Technology Corp. Methods for providing a bonded-particle model in computer aided engineering system
US20140278292A1 (en) * 2013-03-15 2014-09-18 Airbus Operations (Sas) Method for coupling non-destructive inspection data for a material or structure to an analysis tool
US9861569B2 (en) * 2013-05-01 2018-01-09 New York University Specificity, flexibility and valence of DNA bonds for guided emulsion architecture
CN103425899B (zh) * 2013-09-10 2017-03-29 南京大学 用于页岩气水力压裂的三维离散元建模和模拟方法
US20150112651A1 (en) * 2013-10-18 2015-04-23 Livermore Software Technology Corporation Bond Model For Representing Heterogeneous Material In Discrete Element Method
CN103745042A (zh) * 2013-12-25 2014-04-23 广西科技大学 一种壁流式颗粒捕集器的建模方法
EP3097148A4 (en) * 2014-01-23 2017-08-30 Peterson Chemical Technology LLC Cushioning foams containing reflective particulates with visually distinguishable reflective surfaces to create a unique appearance
US9792391B2 (en) * 2014-06-06 2017-10-17 Siemens Product Lifecyle Management Software Inc. Refining of material definitions for designed parts
US11783100B2 (en) * 2018-09-14 2023-10-10 Northwestern University Integrated process-structure-property modeling frameworks and methods for design optimization and/or performance prediction of material systems and applications of same

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006259910A (ja) * 2005-03-15 2006-09-28 Fuji Xerox Co Ltd 粒子充填シミュレーション装置及び粒子充填シミュレーション方法、並びにコンピュータ・プログラム
US20100211330A1 (en) * 2009-01-13 2010-08-19 New York University Packing Properties of Particulate Compositions
WO2012131372A1 (en) * 2011-03-31 2012-10-04 Dem Solutions Limited Method and apparatus for discrete element modeling involving a bulk material
EP2509009A1 (en) * 2011-04-05 2012-10-10 Japan Agency for Marine-Earth Science and Technology Particle simulator and method of simulating particles
CN104899393A (zh) * 2015-06-19 2015-09-09 山东大学 一种离散相增强复合材料细观结构的建模方法
CN105740532A (zh) * 2016-01-28 2016-07-06 重庆交通大学 母岩及其颗粒料的二维离散元模型构建方法
CN106777807A (zh) * 2017-01-13 2017-05-31 北京航空航天大学 一种粉末冶金随机粒度分布3d有限元建模与仿真方法

Cited By (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111783269A (zh) * 2019-03-18 2020-10-16 山东高速集团有限公司 一种基于离散元分析道路空隙结构由于扬尘造成阻塞的方法
CN111783269B (zh) * 2019-03-18 2024-01-12 山东高速集团有限公司 一种基于离散元分析道路空隙结构由于扬尘造成阻塞的方法
CN110210178A (zh) * 2019-06-26 2019-09-06 西安理工大学 一种基于Python再生混凝土三维随机球形骨料模型的构建方法
CN111027244A (zh) * 2019-12-03 2020-04-17 天津大学 一种百亿级颗粒模型的构建方法
CN111027244B (zh) * 2019-12-03 2024-03-12 天津大学 一种百亿级颗粒模型的构建方法
CN111428359A (zh) * 2020-03-23 2020-07-17 河海大学 一种考虑晶粒咬合的各向异性岩石建模方法
CN111428359B (zh) * 2020-03-23 2022-03-08 河海大学 一种考虑晶粒咬合的各向异性岩石建模方法
CN112084694B (zh) * 2020-09-15 2023-08-25 河南理工大学 一种考虑非理想界面的颗粒增强复合材料微观结构的几何建模方法
CN112084694A (zh) * 2020-09-15 2020-12-15 河南理工大学 一种考虑非理想界面的颗粒增强复合材料微观结构的几何建模方法
CN112461718A (zh) * 2020-11-18 2021-03-09 中国石油大学(华东) 孔隙度与颗粒粒径分布关系表征方法
CN113111560A (zh) * 2021-04-26 2021-07-13 山东大学 一种非均质矿物铸件细观结构模型生成方法及系统
CN113962066A (zh) * 2021-09-27 2022-01-21 太原理工大学 一种含六相组分的钢筋混凝土三维细观模型
CN113962066B (zh) * 2021-09-27 2024-03-19 太原理工大学 一种含六相组分的钢筋混凝土三维细观模型
CN115050431A (zh) * 2022-04-27 2022-09-13 中南大学 一种水泥稳定再生集料的三维细观结构的建模分析方法
CN115050431B (zh) * 2022-04-27 2024-05-03 中南大学 一种水泥稳定再生集料的三维细观结构的建模分析方法

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