Uncertainty Quantification of Stochastic Defects in Materials (e-bog) af Chu, Liu
Chu, Liu (forfatter)

Uncertainty Quantification of Stochastic Defects in Materials e-bog

436,85 DKK (inkl. moms 546,06 DKK)
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 esta...
E-bog 436,85 DKK
Forfattere Chu, Liu (forfatter)
Forlag CRC Press
Udgivet 24 december 2021
Længde 196 sider
Genrer PHFC
Sprog English
Format pdf
Beskyttelse LCP
ISBN 9781000506068
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 FeaturesConsists 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 softwareThis 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.