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Full 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.
Contents
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 Young's Modulus Prediction. 12. Strengthen Possibility by Random Vacancy Defects.
