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 examplesDefines stochastic defects in materials and presents the uncertainty quantification for defect location, size, geometrical configuration, and instabilityIntroduces general Monte Carlo methods, polynomial chaos expansion, stochastic finite element methods, and machine learning methodsProvides a variety of examples to support the introduced methods and theoriesApplicable 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.
Pursuing a comprehensive numerical analytical system, the 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 in the material macro properties.
1. Overview.
2. Stochastic Defects. Part I: Methods and Theories.
3.
Monte Carlo Methods.
4. Polynomial Chaos Expansion.
5. Stochastic Finite
Element Method.
6. Machine Learning Methods. Part II: Examples.
7. Numerical
Examples.
8. Monte Carlo-based Finite Element Method.
9. Impacts of Vacancy
Defects in Resonant Vibration.
10. Uncertainty Quantification in
Nanomaterial.
11. Equivalent Youngs Modulus Prediction.
12. Strengthen
Possibility by Random Vacancy Defects.
Dr. Liu Chu received her B.E. degree in Materials Science and Engineering, and M.E. degree in Mechanics from Dalian Maritime University, China, and the Ph.D. in Mechanics from the Institut national des sciences appliquées de Rouen (INSA Rouen), France. Dr. Chu focuses on research in computational material mechanics and structural reliability. Her recent research interests include low-dimensional nanomaterial vacancy defects quantification, artificial material microstructure optimization, and mechanical structure reliability analysis. Since 2018, Dr. Chu has published 18 peer-reviewed science and technical papers in international journals and conferences. She is a member of IEEE and has served as a reviewer of several international journals.
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