Mathematical Problems in EngineeringVolume 2011 (2011), Article ID 585624, 19 pagesdoi:10.1155/2011/585624

Research Article

Finite Element Analysis with Iterated Multiscale Analysis for Mechanical Parameters of Composite Materials with Multiscale Random Grains

Youyun Li,1,2 Yuqing Pan,¹ Jianlong Zheng,¹ Chiqing Zhou,¹ and Desheng Wang³

¹School of Traffic and Transportation Engineering, Changsha University of Science and Technology, Hunan 410004, China²State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology, Dalian 116024, China³Division of Mathematical Sciences, School of Physical and Mathematical Science (SPMS), Nanyang Technological University, 21 Nanyang Link, Singapore, 637371

Received 11 August 2010; Revised 22 December 2010; Accepted 17 March 2011

Academic Editor: Moran Wang

Copyright © 2011 Youyun Li et al. Youyun Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Based on the iterated statistically multiscale analysis (SMSA), we present the convergence of the equivalent mechanical parameters (effective moduli), obtain the error result, and prove the symmetric, positive and definite property of the equivalent mechanical parameters tensor computed by the finite element method. The numerical results show the proved results and illustrate that the SMSA-FE algorithm is a rational method for predicting the equivalent mechanical parameters of the composite material with multiscale random grains. In conclusion, we discuss the future work for the inhomogeneous composite material with multiscale random grains.

1. Introduction

Predicting the mechanical parameters of a composite material with the multiscale random grains is a very difficult problem because there are too many random grains in the composite material and the range of the scale of the grains is very large in the material field shown in Figure 1. Many studies on predicting physical and mechanical properties of composite materials with random grains have been done: the law of mixtures [1], the Hashin-Shtrikman bounds [2], the self-consistent method [3], the Eshelbys equivalent inclusion method [4] and the Mori-Tanaka method [5], microanalysis method [6], and so forth. These methods effectively promoted the development of composite materials, but they simplified the microstructure of real materials in order to reduce the computational complexity. The composite materials with large numbers of grains can be divided into two classes according to the basic configuration: the composite materials with periodic configurations, such as braided composites, and the composite materials with random distribution, such as concrete, foamed plastics. Some physical methods and mathematical methods [7–13] have been proposed to solve these problems. However, most of these techniques and methods are based on empirical, semiempirical models or based on the homogenization methods in the periodical structure. Due to the difference of basic configuration, it is difficult for them to handle the composite material with large numbers of multiscale random grains. Hence, in order to evaluate the physical and mechanical performance of the composite material with random grains, it is necessary to make use of the different advanced numerical methods.

(a)

(b)

(c)

Figure 1: (a) with multiscale grains, (b) equivalent matrix with random grains, (c) cell.

In the recent decades, for the problems with the stationary random distribution, Jikov et al. [14] developed the homogenization method and proved the existence of the homogenization coefficients and the homogenization solution, however, not provided with the numerical techniques to carry out the methods for the stationary random distribution. In addition, their method only deals with the point randomly distributed, not with random grains.

For the porous medias with the random distribution, Hou and Wu [15] developed the the multiscale finite element base function method to compute these problems of the porous medias; this method is much valid to the problems with random coefficients and problems with continuous scale. As for the multiscale systems with stochastic effective, Vanden-Eijnden gave the specific step to carry the multiscale method out [16]; their methods are very effective to mainly solve the problem with time process. For the perforated domain with small holes, Wang et al. gave an effective macroscopic model for a stochastic microscopic system, and in theory, mainly proved that the solutions of the microscopic model converge to those of the effective macroscopic model in probability distribution as the size of holes diminishes to zero [17]. However, the above methods are not for the equivalent performance of the composite material with large numbers of the multiscale random grains.

Duschlbauer et al. developed the homogenization method with the Mori-Tanaka scheme averaging microfields extracted for individual fibers and the finite element analysis to estimate the linear thermoelastic and thermophysical behavior of a short fiber reinforced composite material with planar random fiber arrangement [18]. Kari et al. developed a representative volume element (RVE) approach that was used to calculate effective material properties of randomly distributed short fibre composites and analyzed the properties for the volume of random short fibres [19]. Recently, Kalamkarov et al. gave an asymptotic homogenization model for the 3D grid-reinforced composite structures with the orthotropic reinforcements [20, 21], and Wang and Pan obtained the elastic property for the multiphase composites with the random microstructures [22, 23]. Their methods are the effective homogenous methods for the equivalent performance of the composites with the random grains. However, in fact, in the engineering fields, for the composite materials with a large number of multiscale random grains, such as concrete, asphalt mixture, rock mass, and foam plastics, owing to the random complexity in configuration and for that the grain scale range is very large from 10⁻¹ m to 10⁻⁶ m [24], shown in Figure 1, the above methods find it difficult to analyze the mechanical and physical performance. Hence, in order to deal with the composite materials with multiscale random grains, authors proposed a kind of statistically multiscale analysis (SMSA) method to predict the effective mechanical parameters of the composite materials with a large number of random multiscale grains [25–27]. In previous papers [25–27], we proposed an expression for predicting the equivalent mechanical parameters of a composite material with multiscale grains. This method cannot only show the macrocharacteristics and random configurations of a composite material, but also show the contribution of the small-scale grains. In addition, this method can greatly decrease the computing time for the required numerical result.

In these previous papers [25–27], we gave the multiscale formula to compute the composite material and proved that the expected displacement field is convergent to the equivalent displacement field based on the multiscale methods in the mathematical theory. However, we did not discuss the convergence, the error result, and the symmetric, positive definite property of the equivalent mechanical parameter tensor of the composite material with random grains by the SMSA-FE method. Therefore, in this paper, the convergence and the error result based on statistical multiscale analysis (SMSA) shall be presented, and the symmetric, positive definite property of the equivalent parameters tensor (the random parameters subjected to the uniform distribution) shall be proved.

The next section reviews a representation of a composite material with multiscale random grains, some results, and the SMSA-FE procedure [27]. Section 3 is devoted to proving the convergence of the equivalent mechanical parameters computed by the SMSA-FE algorithm. Section 4 obtains the error results of the SMSA-FE algorithm on iterated multiscale analysis. In Section 5, the symmetric, positive definite property of the equivalent mechanics parameters tensor computed by the SMSA-FE algorithm is proved. In Section 6, the numerical results are presented to demonstrate the validity, the convergence, and the proved results of the SMSA-FE algorithm. Finally, we discuss the future work for the inhomogeneous composites with multiscale random grains.

2. Iterated Multiscale Analysis Model and Algorithm

In the previous papers [27], the author had given the algorithm to compute the equivalent mechanical parameters in detail. In order to prove the finite element error and the convergence of the iterated multiscale computed model, we shall review the model and the algorithm in the brief.

2.1. Iterated Multiscale Analysis Model

For the brief, all of the grains are assumed as the ellipsoids. Set a domain to represent a composite with multiscale random grains shown in Figure 1(a). Set to be a set of cube cells of the size shown in Figure 1(b). Based on [27], the iterated multiscale analysis model can be represented as follows. (1)Obtain the statistical data of the composites and specify the distributions of the ellipsoid’s parameters.(2)Set to denote the number of the th scale ellipsoids in the cell ; we can describe the th scale cube cell as follows: where is the center point, is the long axis, is the middle axis, is the short axis, and , , and are the directions for the axis and of the ellipsoids, respectively. One sample is shown in Figure 1(c).(3)Set the domain to be logically composed of -sized samples: shown in Figure 1(b). It can be defined as . By the SMSA-FE algorithm [27], the equivalent mechanical parameters can be predicted. Thus, the equivalent material with th scale random grains in can be formed.(4)Set to be the scale number in the composites with multiscale random grains. For , using the above representation from step to step , recursively and successively, the multiscale random model of can be described.

For example, the asphalt concrete [24] can be considered as the composites with multiscale random grains, respectively. Set and , their configuration can be shown in Figure 1.

2.2. SMSA Algorithm Based on Finite Element Method

In the previous section, we introduced the equivalent composites with the th scale grains. In this section, we shall review the mathematical theory that predicts the equivalent mechanical parameters of these composites with random grains by the statistical multiscale analysis (SMSA) [27].

For the domain shown in Figure 1(b), their corresponding elasticity equation system and the essential boundary condition can be shown as follows: where , for , , is the probability space, shown in Figure 1, are the displacement field, are the loads, and is the boundary displacement vector.

In the paper [25–27], we had given the SMSA method [25] and established the finite element method [27] to compute the equivalent mechanical parameters. If the FE space can be established and , the equivalent mechanical parameters can be computed.

Theorem 2.1. If a composite material with random grains is subjected to the probability distribution , the equivalent mechanical parameters of a composite material can be approximated as where and is computed as are the FE solutions of (2.6) on unit cell .

Therefore, the equivalent mechanical parameters can be determined by the following SMSA-FE algorithm.

SMSA-FE Algorithm (1)Specify the scale number of random grains in the composites and set the iterative number .(2)Model -scale random grains in and generate meshes of the sample domain according to the algorithm in [27].(3)If , in can be indicated as follows: where and , denotes the domain of the basic configuration, denotes the domain of the matrix, denotes the domain of the random grains in , and denotes the interface domain between the grains and the matrix. Go to step .(4)If , in can be indicated as follows: where and , denotes the domain of the basic configuration, denotes the domain of the equivalent matrix, and denotes the domain of random grains. Go to step .(5)Compute the FE approximation according to (2.6), obtain the FE approximation of sample on the th scale according to (2.4), and compute the FE approximation of the equivalent mechanical parameter tensor on the th scale using (2.3).(6)Set equal to on the th scale and . If , go to step . Otherwise, the equivalent mechanical parameter tensor is the equivalent mechanical parameter tensor of the composite material with multiscale random grains in .

3. Convergence of SMSA-FE Algorithm

Lemma 3.1. If and are the finite element approximations of the random variables and , respectively, then there exists a constant such that .

Proof. If for any sample , (2.6) has one unique finite element solution such that where and are constants and independent of and .From (2.4) and (3.1), one can obtain where and is independent of both the random variables and the local coordinate . Therefore, for the random variable in Section 2,

Lemma 3.2. If is a random variable and are defined as above, then one unique expected value of the equivalent mechanical parameters tensor exists in the probability space .

Proof. From the definition of the long axis , the middle axis , the short axis , the directions of the long axis and the middle axis, , , and , , and the coordinates of the central points of the ellipsoids , their probability density functions are denoted by , , , , , , , , , and , respectively. The united random variable has the the united probability density function . From Lemma 3.1, one can obtain Therefore, there exists one unique expected value of the equivalent mechanical parameters tensor in the probability space .

Lemma 3.3. If have the expected value in the probability space and is the random variable, one obtain

Proof. Because are the independent and identical distribution random variables and from Lemma 3.2, . Set ; from Kolmogorov’s classical strong law of large numbers, one obtains Therefore, we have , that is,

Theorem 3.4. If are computed as the equivalent mechanical parameter tensor of the composite material with -scale random grains by the SMSA-FE algorithm, then the expected values of the equivalent mechanical parameters tensor exist in the probability space .

Proof. Set ; define because both and are constants satisfying . It is easy to see that are bounded and measurable random variables. Based on Lemmas 3.1, 3.2, and 3.3, there exist the expected values of the equivalent mechanical parameter tensor of the material with only the th random grains. Therefore, the equivalent mechanical parameter tensor of the composites with the th random grains can exist as follows: Set and , the mechanical parameters of the equivalent matrix material and the grain material in the equivalent composite material with the th random grains can be obtained. That is, . It is easy to see that are bounded and measurable random variables. By the iterated loop proof for as above, based on Lemmas 3.2 and 3.3, the convergence of the equivalent mechanical parameter tensor can be obtained.

4. Error Analysis for Equivalent Mechanical Parameter Tensor Computed by SMSA-FE Algorithm

Based on the SMSA-FE algorithm, if the equivalent mechanical parameter tensor is computed, three kinds of errors are considered: the homogenization error, the random error based on Monte Carlo simulation method, and the finite element computation error. For the homogenization error, the composite materials with multiscale random grains are the special cases of the random coefficient problems whose convergence was proved in [14]. For the random error, we have obtained the convergence of the equivalent mechanical parameter tensor of the composite material with multiscale random grains as above. Therefore, in the following section, we will devote to analyzing the finite element error based on SMSA.

Firstly, we give the finite element error estimation of the statistical two-scale analysis. Then we give the error estimation of the SMSA-FE algorithm.

Lemma 4.1. If , is the variational solution of (2.6) and is the corresponding finite element solution of (2.6) and , then we have where the constant is independent of the size of mesh.

Proof. Since , based on Korn inequality and the interpolation theorem, we have that That is, Aubin-Nitsche lemma [28] yields

Lemma 4.2. Let , be the variational solution of the (2.6) and the finite element solution such that , and then one has

In fact, based on the idea and the method in [28], it is easy to prove Lemma 4.2.

Lemma 4.3. Let be the equivalent mechanical parameter tensor matrix based on STSA and its finite element approximation as above, if there exists one constant such that . Then one has

Proof. From the above algorithm, the following equation is held. where is defined by where denotes and denotes Since denote the maximum norm of matrix , applying Lemma 4.2 to the above (4.8), we deduce Since there exists one constant such that the function matrices , the above inequality (4.9) yields Then inequality (4.6) follows from the above inequality (4.10).Based on the SMSA-FE algorithm, the equivalent mechanical parameter tensor of the equivalent material with the th random grains can be obtained. Therefore, the matrix material with th random grains can be considered as the equivalent matrix material. Using the loop proof by Lemmas 4.2 and 4.3, it is easy to obtain the following theorem on the equivalent mechanical parameter tensor of a composite material with -scale random grains.

Theorem 4.4. Let be the equivalent mechanical parameters tensor of the composite material with r th scale random grains and its finite element approximation; set the size of the last mesh of the finite element in to be ; based on the SMSA-FE algorithm, if there exists one constant such that for all, , then one has where is one constant that is independent of but dependent on the sizes of the other finite element mesh in the cell with rth random grains.

From Theorem 4.4, it is easy to see the error of the equivalent mechanical parameter tensor of the composite material with the biggest grains being the main error by the SMSA-FE algorithm. Hence, we only need to consider the error in the composite material with the biggest random grains.

5. Symmetry and Positive Definite Property for Equivalent Mechanical Parameter Tensor

From [7, 26], if the parameters of the ellipsoids are subjected to the uniform probability , the equivalent mechanical parameter tensor shall satisfy the following conditions: where for any symmetry matrix , , and .

Therefore, if the parameters of the ellipsoids are subjected to the uniform probability , it is important to keep the symmetric, positive definite property of mechanical parameter tensor computed by the finite element method. So we shall give the following theorem to illustrate it.

Lemma 5.1. Let be the equivalent mechanical parameter tensor based on STSA [26] and its finite element approximation; if there exists one constant such that for all , the matrix satisfies the following property: for any symmetric matrix , where are positive constants.

Proof. Taking into consideration the fact that , based on the concept of , taking into account the idea in [7], we deduce that So we have proved . Let us establish the first equality in (5.2), which is equivalent to prove , where denotes the transpose of the matrix and is the matrix .From the integral identity for solution of problem (2.6), for any matrix , we have Based on the relations and for matrices , it is easy to obtain from (5.5) that If we set , the following equations are obtained. Taking into account the idea in [7], (5.7), and the concept of , the following equations can be obtained. In the second equation of the above equations, the property of is used; in the third equation, (5.7) is applied. It follows that the equivalent mechanical parameter matrices can be written in the following form by the relationship of : Thus Hence, we see that . Thus we obtain the following equations: Making use of the relations (5.11), one has That is (5.2) is proved.In the sequel, we shall prove (5.3). From the inequality (4.6) and Theorem 4.4, there exists one that is small enough such that We have Setting and yields the inequality (5.3).By the iterated multiscale analysis and Lemma 5.1, the finite element approximation of the equivalent mechanical parameter tensor of the composite material with multiscale random grains satisfies the following property.

Theorem 5.2. Let be the equivalent mechanics parameter tensor of the composite material with r-scale random grains based on the SMSA algorithm and their finite element approximation by (2.3). Set the size of the last mesh of the finite element in to be ; if there exists the constant such that for all, , then satisfy the conditions where is the symmetry matrix and are the positive constants that are independent of but dependent on the sizes of the other finite element meshes in the th scale cells .

6. Numerical Experiment

To test the validity of the error result, the convergence, and the symmetric, positive definite property of the mechanical parameter tensor computed by the SMSA-FE algorithm, two numerical examples are given as follows.

The first example models a composite material. The grains are divided into two classes according to the sizes of their long axis shown in Table 1. We use one statistical window to predict the mechanical parameters of the equivalent matrix with small random grains. In each window, small grains occupy approximate 30% of the volume. Their long axis , short axis , and angle are subjected to the uniform distributions shown in Table 1. Using the different finite element sizes , based on the data of Table 2, we obtain the equivalent mechanical parameters tensor that are given in Table 3.

Table 1: Probability distributions of grains in composite material.

Small grains
Large grains

Table 2: Mechanical parameters of the matrix and grains.

Matrix
Grains

0

0

0

0

0
0

0
0

Table 3: Equivalent mechanical parameters for different mesh sizes .

.

0.04

0.02

0.01

0.005

From Table 3, it is easy to see that convergence of the equivalent mechanical parameter tensor computed by the SMSA-FE algorithm exists. From Table 3, the symmetric, positive definite property of the equivalent mechanical parameter tensor and the convergence of the finite element errors with the different mesh sizes are proved.

The second example is a concrete named as C30 with three-scale random rock grains whose sizes are from 0.3 mm to 19 mm. Its matrix is made up of the cement and the sand. Their sizes of the three-scale rock grains in the concrete are shown in Table 4. Their elastic parameters are shown in Table 5. If all grains are generated in a large statistical window of 500 mm, the number of grains is approximately 6360. In each window, small grains, middle grains, and the large grains occupy approximately 57.7% of the volume and are subjected to the uniform probability distribution in the range of their sizes. Therefore, we set three kinds of sizes of the statistical windows: mm, mm, and mm to compute the equivalent mechanical parameter tensor with the different scale random grains.

Table 4: Size of rock random grains and the number in a statistical window.

Class
Grain size range
Average size
Number of rock grains
Sizes of statistical windows

Large grains
10–19 mm
14.5 mm
8
mm

Middle grains
1–10 mm
5 mm
20
mm

Small Grains
0.3–1 mm
0.6 mm
28
mm

Table 5: Elasticity mechanical parameters of the matrix, grains, and joint interface materials.

Class
Young’s modulus (GPa)
Poisson's ratio

Rock grains
74.5
0.15

Matrix
13.5
0.25

Joint interface
50.5
0.20

Numerical results for the mechanical parameter tensor of a composite with only small, middle, and large rock grains are listed in Tables 6, 7, and 8 by the SMSA-FE algorithm. Tables 6–8 also show that the equivalent parameter tensor computed by the SMSA-FE algorithm possess the symmetrical, positive, and definite properties. The expected values of Young's modulus and Poisson's ratio for the different number of samples with three-scale random grains are shown in Table 9 and in Figure 2. Table 9 and Figure 2 show that the equivalent Young's modulus and Poisson's ratio are convergent.

Table 6: Equivalent mechanical parameters of a composites with small rock random grains by the SMSA-FE algorithm (kPa).

20341770.6
6054537.6
6054645.8
−119951.6
164516.8
−12764.7

6054537.6
20128556.8
6060485.8
−106579.5
19649.2
−141765.4

6054645.8
6060485.8
20061719.4
−18765.7
140779.4
−116731.9

0.000000
0.000000
0.000000
6923128.0
0.000000
0.000000

164516.8
19649.25
140779.48
0.000000
6902337.6
−81061.0

−12764.7
−141765.4
0.000000
98172.3
−81061.0
6859146.1

Table 7: Equivalent mechanical parameter tensor for concrete with middle rock random grains by the SMSA-FE algorithm (kPa).

25464900.2
6882577.0
6881924.2
−135341.116667
111669.7
−2238.3

6882577.0
25410941.8
6916568.5
−123921.9
7075.0
−187899.58

6881924.2
6916568.5
25291123.2
−17145.9
89634.5
−192916.5

0.000000
0.000000
0.000000
9071093.3
0.000000
0.000000

111669.7
7075.0
89634.5
0.000000
9042380.8
−82547.1

−2238.3
-187899.5
0.000000
69773.7
−82547.1
9060481.0

Table 8: Equivalent mechanical parameter tensor for concrete with large rock random grains by the SMSA-FE algorithm (kPa).

34510679.4
8073028.8
8076036.2
−75074.2
111127.6
−4769.6

8073025.0
34121732.2
8092588.8
−36814.0
5862.0
−110837.9

8076036.8
8092589.3
34065284.2
−531.7
123664.7
−79363.6

0.000000
0.000000
0.000000
12860237.8
0.000000
0.000000

111127.6
5862.0
123664.7
0.000000
12862877.8
−30079.5

−4769.6
−110837.9
0.000000
78302.2
−30079.5
12783465.6

Table 9: Equivalent mechanical parameter tensor for concrete with large rock random grains by the SMSA-FE algorithm (GPa).

Iterative number
5
10
15
20
25
30

17.4985392
16.9160384
16.9151317
17.0004448
17.0245196
16.9342101

0.231375
0.234653
0.234271
0.233951
0.233942
0.233926

22.5917136
22.2098256
22.2298752
22.1302736
22.1057408
22.0321386

0.214631
0.215408
0.215342
0.215443
0.215552
0.215537

30.644806.4
30.3689408
30.5923968
30.6815936
30.6936934
30.6862218

0.192951
0.193419
0.193065
0.193116
0.193066
0.193082

(a)

(b)

Figure 2: Young's modulus and Poisson's ratio for 5, 10, 15,…, 30 samples with different scale random grains by the SMSA-FE procedure.

A comparison of the numerical results for Young's modulus and Poisson's ratio by the SMSA-FE procedure, by the mixed volume method, and by the experiment method in the lab is also shown in Table 10. Table 10 also shows that Young's modulus and Poisson's ratios produced by SMSA-FE procedure are very close to that by the experiment method. It proves the SMSA-FE algorithm to be feasible and valid for predicting the effective modulus of the composites with random grains.

Table 10: Equivalent mechanical parameters for a concrete by the different methods (GPa).

7. Conclusion

In this paper, we proved that the equivalent mechanical parameter tensor for the composite materials with multiscale random grains is convergent and obtained the error result by the finite element analysis. At the same time, we also prove that the equivalent parameter tensor matrix should satisfy the symmetric, positive, and definite property.

Various test examples were solved by the SMSA-FE procedure. The numerical results show that the SMSA-FE procedure is feasible and valid and that these data satisfy the properties of the equivalent mechanical parameter tensor proved in the pervious sections.

The procedure can also be extended to other composite materials with random short fibers, random foams, random cavities, and so forth. Although we have given the specific steps, some theory results and numerical examples to carry out the SMSA-FE method and to illustrate the validity, the influence of shape, size, component, orientation, spatial distribution, and volume fraction of inclusions on inhomogeneous macromechanical properties, analyze the calculated results, and capture the information of microbehaviors to the macromechanical properties are also our important future work for the composite materials with random grains.

Acknowledgments

This work is supported by National Natural Science Foundation of China (NSFC) Grants (11072041), by State Key Laboratory of Structural Analysis for Industrial Equipment, Dalian University of Technology (GZ1005), by Hunan province Natural Science Foundation Grants of China (10JJ6065), by Scientific Research Starting Foundation for Returned Overseas Chinese Scholars, Ministry of Education, China (20091001), and by China Postdoctoral Science Foundation (20100480944).

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