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Applied RVE Reconstruction and Homogenization of Heterogeneous Materials [Kõva köide]

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  • Formaat: Hardback, 208 pages, kõrgus x laius x paksus: 241x165x18 mm, kaal: 472 g
  • Ilmumisaeg: 07-Jun-2016
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1848219016
  • ISBN-13: 9781848219014
Teised raamatud teemal:
Applied RVE Reconstruction and Homogenization of Heterogeneous MaterialsSuurem pilt
  • Formaat: Hardback, 208 pages, kõrgus x laius x paksus: 241x165x18 mm, kaal: 472 g
  • Ilmumisaeg: 07-Jun-2016
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1848219016
  • ISBN-13: 9781848219014
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781848219014.html
Märksõnad:
Applied RVE Reconstruction and Homogenization of Heterogeneous Materials Statistical correlation functions are a well-known class of statistical descriptors that can be used to describe the morphology and the microstructure-properties relationship. A comprehensive study has been performed for the use of these correlation functions for the reconstruction and homogenization in nano­composite materials. Correlation functions are measured from different techniques such as microscopy (SEM or TEM), small angle X-ray scattering (SAXS) and can be generated through Monte Carlo simulations. In this book, different experimental techniques such as SAXS and image processing are presented, which are used to measure two-point correlation function correlation for multi-phase polymer composites.

Higher order correlation functions must be calculated or measured to increase the precision of the statistical continuum approach. To achieve this aim, a new approximation methodology is utilized to obtain N-point correlation functions for multiphase heterogeneous materials. The two-point functions measured by different techniques have been exploited to reconstruct the microstructure of heterogeneous media.

Statistical continuum theory is used to predict the effective thermal conductivity and elastic modulus of polymer composites. N-point probability functions as statistical descriptors of inclusions have been exploited to solve strong contrast homogenization for effective thermal conductivity and elastic modulus properties of heterogeneous materials.

Finally, reconstructed microstructure is used to calculate effective properties and damage modeling of heterogeneous materials.
Preface ix
Introduction xiii
Chapter 1 Literature Survey
1(14)
1.1 Random heterogeneous material
1(1)
1.2 Two-point probability functions
2(2)
1.3 Two-point cluster functions
4(1)
1.4 Lineal-path function
4(1)
1.5 Reconstruction
4(7)
1.5.1 X-ray computed tomography (experimental)
4(2)
1.5.2 X-ray computed tomography (applications to nanocomposites)
6(1)
1.5.3 FIB/SEM (experimental)
6(4)
1.5.4 Reconstruction using statistical descriptor (numerical)
10(1)
1.6 Homogenization methods for effective properties
11(1)
1.7 Assumption of statistical continuum mechanics
12(1)
1.8 Representative volume element
13(2)
Chapter 2 Calculation of Two-Point Correlation Functions
15(28)
2.1 Introduction
15(2)
2.2 Monte Carlo calculation of TPCF
17(2)
2.3 Two-point correlation functions of eigen microstructure
19(2)
2.4 Calculation of two-point correlation functions using SAXS or SANS data
21(7)
2.4.1 Case study for structural characterization using SAXS data
24(4)
2.5 Necessary conditions for two-point correlation functions
28(2)
2.6 Approximation of two-point correlation functions
30(12)
2.6.1 Examination of the necessary conditions for the proposed estimation
34(5)
2.6.2 Case study for the approximation of a TPCF
39(3)
2.7 Conclusion
42(1)
Chapter 3 Approximate Solution for N-Point Correlation Functions for Heterogeneous Materials
43(24)
3.1 Introduction
43(2)
3.2 Approximation of three-point correlation functions
45(6)
3.2.1 Decomposition of higher order statistics
45(1)
3.2.2 Decomposition of two-point correlation functions
46(1)
3.2.3 Decomposition of three-point correlation functions
47(4)
3.3 Approximation of four-point correlation functions
51(5)
3.4 Approximation of N-point correlation functions
56(4)
3.5 Results
60(6)
3.5.1 Computational verification
60(2)
3.5.2 Experimental validation
62(4)
3.6 Conclusions
66(1)
Chapter 4 Reconstruction of Heterogeneous Materials Using Two-Point Correlation Functions
67(36)
4.1 Introduction
67(2)
4.2 Monte Carlo reconstruction methodology
69(17)
4.2.1 3D cell generation
72(3)
4.2.2 Cell distribution
75(2)
4.2.3 Cell growth
77(2)
4.2.4 Optimization of the statistical correlation functions
79(1)
4.2.5 Percolation
79(2)
4.2.6 Three-phase solid oxide fuel cell anode microstructure
81(1)
4.2.7 Reconstruction of multiphase heterogeneous materials
82(4)
4.3 Reconstruction procedure using the simulated annealing (SA) algorithm
86(5)
4.4 Phase recovery algorithm
91(5)
4.5 3D reconstruction of non-eigen microstructure using correlation functions
96(5)
4.5.1 Microstructure reconstruction using Monte Carlo methodology
96(1)
4.5.2 Sample production
97(1)
4.5.3 Monte Carlo calculation of a two-point correlation function
98(1)
4.5.4 Microstructure optimization
99(1)
4.5.5 Results and discussion
99(2)
4.6 Conclusion
101(2)
Chapter 5 Homogenization of Mechanical and Thermal Behavior of Nanocomposites Using Statistical Correlation Functions: Application to Nanoclay-based Polymer Nanocomposites
103(30)
5.1 Introduction
103(1)
5.2 Modified strong-contrast approach for anisotropic stiffness tensor of multiphase heterogeneous materials
104(8)
5.3 Strong-contrast approach to effective thermal conductivity of multiphase heterogeneous materials
112(5)
5.4 Simulation and experimental verification
117(10)
5.4.1 Computer-generated model
118(2)
5.4.2 Thermal conductivity
120(2)
5.4.3 Mechanical model
122(3)
5.4.4 Experimental part
125(2)
5.5 Results and discussion
127(3)
5.5.1 Thermal conductivity
127(1)
5.5.2 Thermo-mechanical properties
128(2)
5.6 Conclusion
130(3)
Chapter 6 Homogenization of Reconstructed RVE
133(36)
6.1 Introduction
133(1)
6.2 Finite element homogenization of the reconstructed RVEs
134(7)
6.2.1 Reconstruction of FIB-SEM RVEs
134(4)
6.2.2 Finite element analysis of RVEs
138(3)
6.3 Finite element homogenization of the statistical reconstructed RVEs
141(8)
6.3.1 FEM analysis of reconstruction RVE using statistical correlation functions
141(2)
6.3.2 Finite element analysis of RVEs
143(6)
6.4 FEM analysis of debonding-induced damage model for polymer composites
149(17)
6.4.1 Representative volume element (RVE)
150(2)
6.4.2 Cohesive zone model
152(5)
6.4.3 Material behavior and FE simulation
157(1)
6.4.4 The effect of the GNP's volume fraction and aspect ratio in perfectly bonded nanocomposite
158(2)
6.4.5 Comparing the effect of the GNP's volume fraction and aspect ratio in perfectly bonded and cohesively bonded nanocomposites
160(3)
6.4.6 The effect of the GNP's aspect ratio and volume fraction in weakly bonded nanocomposite
163(3)
6.5 Conclusion and future work
166(3)
Appendices 169(2)
Appendix A 171(4)
Appendix B 175(4)
Bibliography 179(6)
Index 185
Yves Rémond is Distinguished Professor (Exceptional Class) at the University of Strasbourg in France.

Saïd Ahzi is a Research Director of the Materials Science and Engineering group at Qatar Environment and Energy Research Institute (QEERI) and Professor at the College of Science & Engineering, Hamad Bin Khalifa University, Qatar Foundation, Qatar.

Majid Baniassadi is Assistant Professor at the School of Mechanical Engineering, University of Tehran, Iran.

Hamid Garmestani is Professor of Materials Science and Engineering at Georgia Institute of Technology, USA and a Fellow of the American Society of Materials (ASM International).