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Applied Micromechanics of Complex Microstructures explains the fundamental concepts of continuum modeling of various complicated microstructures, covering nanocomposites, multiphase composites, biomaterials, biological materials, and more. The authors outline the calculation of effective mechanical and thermal properties, allowing readers to understand the step-by-step modeling and homogenization of complicated microstructures, and the book also features a chapter on microstructure hull and material design. Modeling of complex samples with nonlinear properties such as neural tissue, bone microstructure, and liver tissue is also explained and analyzed. - Explains the core concepts of continuum modeling of different complex microstructures, including nanocomposites, multiphase composites, biomaterials, and biological materials - Provides detailed calculations of eff ective mechanical and thermal properties allowing the audience to understand the modeling and homogenization of complex microstructures - Covers several methods for designing the microstructure of heterogeneous materials

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.

This book outlines methods for assessing the properties of advanced polymer composite materials, discussing sample preparation, microscopy and scattering techniques, data analysis and interpretation, microstructure identification, constitutive models, and more. It introduces the constituting laws of soft materials and demonstrates how to identify microstructure based on microscopy images. Techniques for measuring the thermal properties, electrical, and thermal characterization of these materials are covered including scanning electron microscopy, transmission electron microscopy, focused ion beam microscopy, as well as scattering techniques such as wide-angle X-ray, small-angle x-ray, and small-angle neutron. Each chapter starts with a brief explanation of the characterization technique followed by practical examples demonstrating methods of interpreting experimental results.• Focuses on the experimental characterization of the microstructure, thermomechanical, and electrical properties of polymer composites and nanocomposites• Discusses sample preparation for characterization, outlining various mixing methods and emphasizing the importance of uniform dispersion and homogeneity of fillers or reinforcements• Provides methods for experimental data analysis and microstructure identification for heterogeneous materials and polymer-based composites• Examines different constitutive models and their application to soft materials• Analyzes the thermal, electrical, mechanical, and microstructural properties of polymer nanocomposites, shape memory polymers, and polymer composites

Computational Modelling of Intelligent Soft Matter: Shape Memory Polymers and Hydrogels covers the multiphysics response of various smart polymer materials, such as temperature-sensitive shape memory polymers and temperature/ chemosensitive hydrogels. Several thermo–chemo-mechanical constitutive models for these smart polymers are outlined, and their real-world applications are highlighted. The numerical counterpart of each introduced constitutive model is also presented, empowering readers to solve practical problems requiring thermomechanical responses of these materials as well as design and analyze real-world structures made of them. - Introduces constitutive models based on continuum thermodynamics for intelligent soft materials - Presents calibration methods for identifying material model parameters as well as finite element implementation of the featured models - Allows readers to solve practical problems requiring thermomechanical responses from these materials as well as the design and analysis of real-world structures made of them

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.

Applied Micromechanics of Complex Microstructures explains the fundamental concepts of continuum modeling of various complicated microstructures, covering nanocomposites, multiphase composites, biomaterials, biological materials, and more. The authors outline the calculation of effective mechanical and thermal properties, allowing readers to understand the step-by-step modeling and homogenization of complicated microstructures, and the book also features a chapter on microstructure hull and material design. Modeling of complex samples with nonlinear properties such as neural tissue, bone microstructure, and liver tissue is also explained and analyzed. - Explains the core concepts of continuum modeling of different complex microstructures, including nanocomposites, multiphase composites, biomaterials, and biological materials - Provides detailed calculations of eff ective mechanical and thermal properties allowing the audience to understand the modeling and homogenization of complex microstructures - Covers several methods for designing the microstructure of heterogeneous materials

Computational Modelling of Intelligent Soft Matter: Shape Memory Polymers and Hydrogels covers the multiphysics response of various smart polymer materials, such as temperature-sensitive shape memory polymers and temperature/ chemosensitive hydrogels. Several thermo–chemo-mechanical constitutive models for these smart polymers are outlined, and their real-world applications are highlighted. The numerical counterpart of each introduced constitutive model is also presented, empowering readers to solve practical problems requiring thermomechanical responses of these materials as well as design and analyze real-world structures made of them. - Introduces constitutive models based on continuum thermodynamics for intelligent soft materials - Presents calibration methods for identifying material model parameters as well as finite element implementation of the featured models - Allows readers to solve practical problems requiring thermomechanical responses from these materials as well as the design and analysis of real-world structures made of them

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.

In this research, A comprehensive study has been performed in the use of two-point correlation functions for reconstruction and homogenization in nano-composite materials. 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 from different techniques have been measured and exploited to reconstruct microstructure of heterogeneous media. A new Monte Carlo methodology is developed as a mean for three-dimensional (3D) reconstruction, of the microstructure of heterogeneous materials, based on two-point statistical functions. The salient feature of the presented reconstruction methodology is the ability to realize the 3D microstructure from its 2D SEM image for a three-phase medium extendable to n-phase media. Finally, Statistical continuum theory is used to predict the effective thermal conductivity and elastic modulus of polymer composites. Two-point and three-point probability functions as statistical descriptor of inclusions have been exploited to solve strong contrast homogenization for effective thermal and mechanical properties of nanoclay based polymer composites. To validate our modeling approach, we conducted several experimental measurements for nanoclay /polymer of composite. Comparison of our predictions with the experimental results led to a good agreement. this allows us to conclude that the proposed methodlogy is accurate.