Uncertainty Quantification In Multiscale Materials Modeling
Download Uncertainty Quantification In Multiscale Materials Modeling full books in PDF, epub, and Kindle. Read online free Uncertainty Quantification In Multiscale Materials Modeling ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
| Author | : Yan Wang |
| Publisher | : Woodhead Publishing Limited |
| Total Pages | : 604 |
| Release | : 2020-03-12 |
| Genre | : Materials science |
| ISBN | : 0081029411 |
Uncertainty Quantification in Multiscale Materials Modeling provides a complete overview of uncertainty quantification (UQ) in computational materials science. It provides practical tools and methods along with examples of their application to problems in materials modeling. UQ methods are applied to various multiscale models ranging from the nanoscale to macroscale. This book presents a thorough synthesis of the state-of-the-art in UQ methods for materials modeling, including Bayesian inference, surrogate modeling, random fields, interval analysis, and sensitivity analysis, providing insight into the unique characteristics of models framed at each scale, as well as common issues in modeling across scales.
| Author | : |
| Publisher | : |
| Total Pages | : |
| Release | : 2011 |
| Genre | : |
| ISBN | : |
| Author | : Manolis Papadrakakis |
| Publisher | : Springer |
| Total Pages | : 303 |
| Release | : 2014-07-02 |
| Genre | : Science |
| ISBN | : 3319063316 |
This book contains the proceedings of the IUTAM Symposium on Multiscale Modeling and Uncertainty Quantification of Materials and Structures that was held at Santorini, Greece, September 9 – 11, 2013. It consists of 20 chapters which are divided in five thematic topics: Damage and fracture, homogenization, inverse problems–identification, multiscale stochastic mechanics and stochastic dynamics. Over the last few years, the intense research activity at micro scale and nano scale reflected the need to account for disparate levels of uncertainty from various sources and across scales. As even over-refined deterministic approaches are not able to account for this issue, an efficient blending of stochastic and multiscale methodologies is required to provide a rational framework for the analysis and design of materials and structures. The purpose of this IUTAM Symposium was to promote achievements in uncertainty quantification combined with multiscale modeling and to encourage research and development in this growing field with the aim of improving the safety and reliability of engineered materials and structures. Special emphasis was placed on multiscale material modeling and simulation as well as on the multiscale analysis and uncertainty quantification of fracture mechanics of heterogeneous media. The homogenization of two-phase random media was also thoroughly examined in several presentations. Various topics of multiscale stochastic mechanics, such as identification of material models, scale coupling, modeling of random microstructures, analysis of CNT-reinforced composites and stochastic finite elements, have been analyzed and discussed. A large number of papers were finally devoted to innovative methods in stochastic dynamics.
| Author | : Yan Wang |
| Publisher | : Woodhead Publishing |
| Total Pages | : 606 |
| Release | : 2020-03-10 |
| Genre | : Technology & Engineering |
| ISBN | : 008102942X |
Uncertainty Quantification in Multiscale Materials Modeling provides a complete overview of uncertainty quantification (UQ) in computational materials science. It provides practical tools and methods along with examples of their application to problems in materials modeling. UQ methods are applied to various multiscale models ranging from the nanoscale to macroscale. This book presents a thorough synthesis of the state-of-the-art in UQ methods for materials modeling, including Bayesian inference, surrogate modeling, random fields, interval analysis, and sensitivity analysis, providing insight into the unique characteristics of models framed at each scale, as well as common issues in modeling across scales. Synthesizes available UQ methods for materials modeling Provides practical tools and examples for problem solving in modeling material behavior across various length scales Demonstrates UQ in density functional theory, molecular dynamics, kinetic Monte Carlo, phase field, finite element method, multiscale modeling, and to support decision making in materials design Covers quantum, atomistic, mesoscale, and engineering structure-level modeling and simulation
| Author | : |
| Publisher | : |
| Total Pages | : 52 |
| Release | : 2015 |
| Genre | : |
| ISBN | : |
Understanding and improving microstructural mechanical stability in metals and alloys is central to the development of high strength and high ductility materials for cladding and cores structures in advanced fast reactors. Design and enhancement of radiation-induced damage tolerant alloys are facilitated by better understanding the connection of various unit processes to collective responses in a multiscale model chain, including: dislocation nucleation, absorption and desorption at interfaces; vacancy production, radiation-induced segregation of Cr and Ni at defect clusters (point defect sinks) in BCC Fe-Cr ferritic/martensitic steels; investigation of interaction of interstitials and vacancies with impurities (V, Nb, Ta, Mo, W, Al, Si, P, S); time evolution of swelling (cluster growth) phenomena of irradiated materials; and energetics and kinetics of dislocation bypass of defects formed by interstitial clustering and formation of prismatic loops, informing statistical models of continuum character with regard to processes of dislocation glide, vacancy agglomeration and swelling, climb and cross slip.
| Author | : Nam Vu-Bac |
| Publisher | : |
| Total Pages | : |
| Release | : 2015 |
| Genre | : |
| ISBN | : |
| Author | : Denielle E. Ricciardi |
| Publisher | : |
| Total Pages | : 231 |
| Release | : 2020 |
| Genre | : Gaussian processes |
| ISBN | : |
Achieving a statistical confidence in a simulation output requires, first, the identification of the various sources of error and uncertainty affecting the simulation results. These sources include machine and user error in collecting calibration data, uncertain model parameters, random error from natural processes, and model inadequacy in capturing the true material property or behavior. Statistical inference can then be used to recover information about unknown model parameters by conditioning on available data while taking into account the various sources of uncertainty.
| Author | : Bin Wen |
| Publisher | : |
| Total Pages | : 225 |
| Release | : 2013 |
| Genre | : |
| ISBN | : |
Mechanical properties of engineering materials are sensitive to the underlying random microstructure. Quantification of mechanical property variability induced by microstructure variation is essential for the prediction of extreme properties and microstructure-sensitive design of materials. Recent advances in high throughput characterization of polycrystalline microstructures have resulted in huge data sets of microstructural descriptors and image snapshots. To utilize these large scale experimental data for computing the resulting variability of macroscopic properties, appropriate mathematical representation of microstructures is needed. By exploring the space containing all admissible microstructures that are statistically similar to the available data, one can estimate the distribution/envelope of possible properties by employing efficient stochastic simulation methodologies along with robust physics-based deterministic simulators. The focus of this thesis is on the construction of lowdimensional representations of random microstructures and the development of efficient physics-based simulators for polycrystalline materials. By adopting appropriate stochastic methods, such as Monte Carlo and Adaptive Sparse Grid Collocation methods, the variability of microstructure-sensitive properties of polycrystalline materials is investigated. The primary outcomes of this thesis include: - Development of data-driven reduced-order representations of microstruc- ture variations to construct the admissible space of random polycrystalline microstructures. - Development of accurate and efficient physics-based simulators for the estimation of material properties based on mesoscale microstructures. - Investigating property variability of polycrystalline materials using efficient stochastic simulation methods in combination with the above two developments. The uncertainty quantification framework developed in this work integrates information science and materials science, and provides a new outlook to multiscale materials modeling accounting for microstructure and process uncertainties. Predictive materials modeling will accelerate the development of new materials and processes for critical applications in industry.
| Author | : Dongwei Ye |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2022 |
| Genre | : |
| ISBN | : 9789464219197 |
| Author | : Yan Wang |
| Publisher | : Elsevier |
| Total Pages | : 0 |
| Release | : 2024-10-01 |
| Genre | : Technology & Engineering |
| ISBN | : 0443136629 |
Fundamentals of Uncertainty Quantification for Engineers: Methods and Models provides a comprehensive introduction to uncertainty quantification (UQ) accompanied by a wide variety of applied examples, implementation details, and practical exercises to reinforce the concepts outlined in the book. Sections start with a review of the history of probability theory and recent developments of UQ methods in the domains of applied mathematics and data science. Major concepts of probability axioms, conditional probability, and Bayes’ rule are discussed and examples of probability distributions in parametric data analysis, reliability, risk analysis, and materials informatics are included. Random processes, sampling methods, and surrogate modeling techniques including multivariate polynomial regression, Gaussian process regression, multi-fidelity surrogate, support-vector machine, and decision tress are also covered. Methods for model selection, calibration, and validation are introduced next, followed by chapters on sensitivity analysis, stochastic expansion methods, Markov models, and non-probabilistic methods. The book concludes with a chapter describing the methods that can be used to predict UQ in systems, such as Monte Carlo, stochastic expansion, upscaling, Langevin dynamics, and inverse problems, with example applications in multiscale modeling, simulations, and materials design. Introduces all major topics of uncertainty quantification with engineering examples, implementation details, and practical exercises provided in all chapters Features examples from a wide variety of science and engineering disciplines (e.g. aerospace, mechanical, material, manufacturing, multiscale simulation) Discusses materials informatics, sampling methods, surrogate modeling techniques, decision tress, multivariate polynomial regression, and more
