Uncertainty Quantification of Stochastic Defects in Materials  book cover
SAVE
$30.00
1st Edition

Uncertainty Quantification of Stochastic Defects in Materials

By

Liu Chu



  • Available for pre-order. Item will ship after December 16, 2021
ISBN 9781032128733
December 16, 2021 Forthcoming by CRC Press
248 Pages 158 B/W Illustrations

 
SAVE ~ $30.00
was $150.00
USD $120.00

Prices & shipping based on shipping country


Preview

Book Description

Uncertainty Quantification of Stochastic Defects in Materials investigates uncertainty quantification methods for stochastic defects in material microstructure. It provides effective supplementary approaches for conventional experimental observation with the consideration of stochastic factor 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 in the material macro properties.

  • 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
  • Features MATLAB and ANSYS computer code

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 non-destructive defect testing.

Table of 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.

...
View More

Author(s)

Biography

Dr. Liu Chu received her B.E. degree in material science and engineering, and M.E. degree in mechanics from Dalian Maritime University, Dalian, China, in 2010 and 2012 respectively, and the Ph.D. degree in Mechanics from the Institut national des sciences appliquées de Rouen (INSA Rouen), Rouen, France, in 2017. Dr. Chu focuses on the research of computational material mechanics and structural reliability. Her recent research interests include: low dimensional nanomaterial vacancy defects quantification, artificial material microstructure optimization, mechanical structure reliability analysis, etc. 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 served as a reviewer of several international journals.