Stochastic Uncertainty Quantification for Multiscale Modeling of Polymeric Nanocomposites PDF Download
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Author: Nam Vu-Bac
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Nam Vu-Bac
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Bokai Liu
Publisher:
ISBN:
Category :
Languages : en
Pages :
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N recent years, lightweight materials, such as polymer composite materials (PNCs) have been studied and developed due to their excellent physical and chemical properties. Structures composed of these composite materials are widely used in aerospace engineering structures, automotive components, and electrical devices. The excellent and outstanding mechanical, thermal, and electrical properties of Carbon nanotube (CNT) make it an ideal filler to strengthen polymer materials' comparable properties. The heat transfer of composite materials has very promising engineering applications in many fields, especially in electronic devices and energy storage equipment. It is essential in high-energy density systems since electronic components need heat dissipation functionality. Or in other words, in electronic devices the generated heat should ideally be dissipated by light and small heat sinks. Polymeric composites consist of fillers embedded in a polymer matrix, the first ones will significantly affect the overall (macroscopic) performance of the material. There are many common carbon-based fillers such as single-walled carbon nanotubes (SWCNT), multi-walled carbon nanotubes (MWCNT), carbon nanobuds (CNB), fullerene, and graphene. Additives inside the matrix have become a popular subject for researchers. Some extraordinary characters, such as high-performance load, lightweight design, excellent chemical resistance, easy processing, and heat transfer, make the design of polymeric nanotube composites (PNCs) flexible. Due to the reinforcing effects with different fillers on composite materials, it has a higher degree of freedom and can be designed for the structure according to specific applications' needs. As already stated, our research focus will be on SWCNT enhanced PNCs. Since experiments are timeconsuming, sometimes expensive and cannot shed light into phenomena taking place for instance at the interfaces/interphases of composites, they are often complemented through theoretical and computational analysis. While most studies are based on deterministic approaches, there is a comparatively lower number of stochastic methods accounting for uncertainties in the input parameters. In deterministic models, the output of the model is fully determined by the parameter values and the initial conditions. However, uncertainties in the input parameters such as aspect ratio, volume fraction, thermal properties of fiber and matrix need to be taken into account for reliable predictions. In this research, a stochastic multiscale method is provided to study the influence of numerous uncertain input parameters on the thermal conductivity of the composite. Therefore, a hierarchical multi-scale method based on computational homogenization is presented in to predict the macroscopic thermal conductivity based on the fine-scale structure. In order to study the inner mechanism, we use the finite element method and employ surrogate models to conduct a Global Sensitivity Analysis (GSA). The SA is performed in order to quantify the influence of the conductivity of the fiber, matrix, Kapitza resistance, volume fraction and aspect ratio on the macroscopic conductivity. Therefore, we compute first-order and total-effect sensitivity indices with different surrogate models. As stochastic multiscale models are computational expensive, surrogate approaches are commonly exploited. With the emergence of high performance computing and artificial intelligence, machine learning has become a popular modeling tool for numerous applications. Machine learning (ML) is commonly used in regression and maps data through specific rules with algorithms to build input and output models. They are particularly useful for nonlinear input-output relationships when sufficient data is available. ML has also been used in the design of new materials and multiscale analysis. For instance, Artificial neural networks and integrated learning seem to be ideally for such a task. They can theoretically simulate any non-linear relationship through the connection of neurons. Mapping relationships are employed to carry out data-driven simulations of inputs and outputs in stochastic modeling. This research aims to develop a stochastic multi-scale computational models of PNCs in heat transfer. ...
Author: Hung Nguyen-Xuan
Publisher: Springer
ISBN: 9811071497
Category : Technology & Engineering
Languages : en
Pages : 1137
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This book provides an overview of state-of-the-art methods in computational engineering for modeling and simulation. This proceedings volume includes a selection of refereed papers presented at the International Conference on Advances in Computational Mechanics (ACOME) 2017, which took place on Phu Quoc Island, Vietnam on August 2-4, 2017. The contributions highlight recent advances in and innovative applications of computational mechanics. Subjects covered include: biological systems; damage, fracture and failure; flow problems; multiscale multiphysics problems; composites and hybrid structures; optimization and inverse problems; lightweight structures; computational mechatronics; computational dynamics; numerical methods; and high-performance computing. The book is intended for academics, including graduate students and experienced researchers interested in state-of-the-art computational methods for solving challenging problems in engineering.
Author: Roham Rafiee
Publisher: Elsevier
ISBN: 0323482228
Category : Science
Languages : en
Pages : 588
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Book Description
Carbon Nanotube-Reinforced Polymers: From Nanoscale to Macroscale addresses the advances in nanotechnology that have led to the development of a new class of composite materials known as CNT-reinforced polymers. The low density and high aspect ratio, together with their exceptional mechanical, electrical and thermal properties, render carbon nanotubes as a good reinforcing agent for composites. In addition, these simulation and modeling techniques play a significant role in characterizing their properties and understanding their mechanical behavior, and are thus discussed and demonstrated in this comprehensive book that presents the state-of-the-art research in the field of modeling, characterization and processing. The book separates the theoretical studies on the mechanical properties of CNTs and their composites into atomistic modeling and continuum mechanics-based approaches, including both analytical and numerical ones, along with multi-scale modeling techniques. Different efforts have been done in this field to address the mechanical behavior of isolated CNTs and their composites by numerous researchers, signaling that this area of study is ongoing. Explains modeling approaches to carbon nanotubes, together with their application, strengths and limitations Outlines the properties of different carbon nanotube-based composites, exploring how they are used in the mechanical and structural components Analyzes the behavior of carbon nanotube-based composites in different conditions
Author: Xiang Ma
Publisher:
ISBN:
Category :
Languages : en
Pages : 224
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Book Description
To accurately predict the performance of physical systems, it becomes essential for one to include the effects of input uncertainties into the model system and understand how they propagate and alter the final solution. The presence of uncertainties can be modeled in the system through reformulation of the governing equations as stochastic partial differential equations (SPDEs). The spectral stochastic finite element method (SSFEM) and stochastic collocation methods are the most popular simulation methods for SPDEs. However, both methods utilize global polynomials in the stochastic space. Thus when there are steep gradients or finite discontinuities in the stochastic space, these methods converge slowly or even fail to converge. In order to resolve the above mentioned issues, an adaptive sparse grid collocation (ASGC) strategy is developed using piecewise multi-linear hierarchical basis functions. Hierarchical surplus is used as an error indicator to automatically detect the discontinuity region in the stochastic space and adaptively refine the collocation points in this region. However, this method is limited to a moderate number of random variables. To address the solution of high-dimensional stochastic problems, a computational methodology is further introduced that utilizes the High Dimensional Model Representation (HDMR) technique in the stochastic space to represent the model output as a finite hierarchical correlated function expansion in terms of the stochastic inputs starting from lower-order to higher-order component functions. An adaptive version of HDMR is also developed to automatically detect the important dimensions and construct higherorder terms using only the important dimensions. The ASGC is integrated with HDMR to solve the resulting sub-problems. Uncertainty quantification for fluid transport in porous media in the presence of both stochastic permeability and multiple scales is addressed using the developed HDMR framework. In order to capture the small scale heterogeneity, a new mixed multiscale finite element method is developed within the framework of the heterogeneous multiscale method in the spatial domain. Several numerical examples are considered to examine the accuracy of the multiscale and stochastic frameworks developed. A summary of suggestions for future research in the area of stochastic multiscale modeling are given at the end of the thesis.
Author: Magd Abdel Wahab
Publisher: Springer
ISBN: 9811304114
Category : Science
Languages : en
Pages : 831
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Book Description
These proceedings gather a selection of peer-reviewed papers presented at the 7th International Conference on Fracture Fatigue and Wear (FFW 2018), held at Ghent University, Belgium on 9–10 July 2018. The contributions, prepared by international scientists and engineers, cover the latest advances in and innovative applications of fracture mechanics, fatigue of materials, tribology and wear of materials. The book is intended for academics, including graduate students and researchers, as well as industrial practitioners working in the areas of fracture fatigue and wear.
Author: Vikas Mittal
Publisher: John Wiley & Sons
ISBN: 3527644350
Category : Technology & Engineering
Languages : en
Pages : 312
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Book Description
The book series 'Polymer Nano-, Micro- and Macrocomposites' provides complete and comprehensive information on all important aspects of polymer composite research and development, including, but not limited to synthesis, filler modification, modeling, characterization as well as application and commercialization issues. Each book focuses on a particular topic and gives a balanced in-depth overview of the respective subfi eld of polymer composite science and its relation to industrial applications. With the books the readers obtain dedicated resources with information relevant to their research, thereby helping to save time and money. This book lays the theoretical foundations and emphasizes the close connection between theory and experiment to optimize models and real-life procedures for the various stages of polymer composite development. As such, it covers quantum-mechanical approaches to understand the chemical processes on an atomistic level, molecular mechanics simulations to predict the filler surface dynamics, finite element methods to investigate the macro-mechanical behavior, and thermodynamic models to assess the temperature stability. The whole is rounded off by a look at multiscale models that can simulate properties at various length and time scales in one go - and with predictive accuracy.
Author: Liu Chu
Publisher: CRC Press
ISBN: 1000506096
Category : Technology & Engineering
Languages : en
Pages : 179
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Book Description
Uncertainty Quantification of Stochastic Defects in Materials investigates the uncertainty quantification methods for stochastic defects in material microstructures. It provides effective supplementary approaches for conventional experimental observation with the consideration of stochastic factors and uncertainty propagation. Pursuing a comprehensive numerical analytical system, this book establishes a fundamental framework for this topic, while emphasizing the importance of stochastic and uncertainty quantification analysis and the significant influence of microstructure defects on the material macro properties. Key Features Consists of two parts: one exploring methods and theories and the other detailing related examples Defines stochastic defects in materials and presents the uncertainty quantification for defect location, size, geometrical configuration, and instability Introduces general Monte Carlo methods, polynomial chaos expansion, stochastic finite element methods, and machine learning methods Provides a variety of examples to support the introduced methods and theories Applicable to MATLAB® and ANSYS software This book is intended for advanced students interested in material defect quantification methods and material reliability assessment, researchers investigating artificial material microstructure optimization, and engineers working on defect influence analysis and nondestructive defect testing.
Author: Valeriy V. Ginzburg
Publisher: Springer Nature
ISBN: 3030604438
Category : Technology & Engineering
Languages : en
Pages : 330
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Book Description
This edited volume brings together the state of the art in polymer nanocomposite theory and modeling, creating a roadmap for scientists and engineers seeking to design new advanced materials. The book opens with a review of molecular and mesoscale models predicting equilibrium and non-equilibrium nanoscale structure of hybrid materials as a function of composition and, especially, filler types. Subsequent chapters cover the methods and analyses used for describing the dynamics of nanocomposites and their mechanical and physical properties. Dedicated chapters present best practices for predicting materials properties of practical interest, including thermal and electrical conductivity, optical properties, barrier properties, and flammability. Each chapter is written by leading academic and industrial scientists working in each respective sub-field. The overview of modeling methodology combined with detailed examples of property predictions for specific systems will make this book useful for academic and industrial practitioners alike.
Author: Sumit Sharma
Publisher: John Wiley & Sons
ISBN: 1119653630
Category : Technology & Engineering
Languages : en
Pages : 320
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Book Description
Learn to model your own problems for predicting the properties of polymer-based composites Mechanics of Particle- and Fiber-Reinforced Polymer Nanocomposites: Nanoscale to Continuum Simulations provides readers with a thorough and up-to-date overview of nano, micro, and continuum approaches for the multiscale modeling of polymer-based composites. Covering nanocomposite development, theoretical models, and common simulation methods, the text includes a variety of case studies and scripting tutorials that enable readers to apply and further develop the supplied simulations. The book describes the foundations of molecular dynamics and continuum mechanics methods, guides readers through the basic steps required for multiscale modeling of any material, and correlates the results between the experimental and theoretical work performed. Focused primarily on nanocomposites, the methods covered in the book are applicable to various other materials such as carbon nanotubes, polymers, metals, and ceramics. Throughout the book, readers are introduced to key topics of relevance to nanocomposite materials and structures—supported by journal articles that discuss recent developments in modeling techniques and in the prediction of mechanical and thermal properties. This timely, highly practical resource: Explains the molecular dynamics (MD) simulation procedure for nanofiber and nanoparticle reinforced polymer composites Compares results of experimental and theoretical results from mechanical models at different length scales Covers different types of fibers and matrix materials that constitute composite materials, including glass, boron, carbon, and Kevlar Reviews models that predict the stiffness of short-fiber composites, including the self-consistent model for finite-length fibers, bounding models, and the Halpin-Tsai equation Describes various molecular modeling methods such as Monte Carlo, Brownian dynamics, dissipative particle dynamics, and lattice Boltzmann methods Highlights the potential of nanocomposites for defense and space applications Perfect for materials scientists, materials engineers, polymer scientists, and mechanical engineers, Mechanics of Particle- and Fiber-Reinforced Polymer Nanocomposites is also a must-have reference for computer simulation scientists seeking to improve their understanding of reinforced polymer nanocomposites.
