Advances in Predictive Models and Methodologies for Numerically Efficient Linear and Nonlinear Analysis of Composites

Petrolo, Marco

  • 出版商: Springer
  • 出版日期: 2019-03-07
  • 售價: $6,470
  • 貴賓價: 9.5$6,147
  • 語言: 英文
  • 頁數: 193
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 3030119688
  • ISBN-13: 9783030119683
  • 相關分類: Machine Learning
  • 海外代購書籍(需單獨結帳)

商品描述

This book gathers contributions addressing issues related to the analysis of composite structures, whose most relevant common thread is augmented numerical efficiency, which is more accurate for given computational costs than existing methods and methodologies. It first presents structural theories to deal with the anisotropy of composites and to embed multifield and nonlinear effects to extend design capabilities and provide methods of augmenting the fidelity of structural theories and lowering computational costs, including the finite element method. The second part of the book focuses on damage analysis; the multiscale and multicomponent nature of composites leads to extremely complex failure mechanisms, and predictive tools require physics-based models to reduce the need for fitting and tuning based on costly and lengthy experiments, and to lower computational costs; furthermore the correct monitoring of in-service damage is decisive in the context of damage tolerance. The third part then presents recent advances in embedding characterization and manufacturing effects in virtual testing. The book summarizes the outcomes of the FULLCOMP (FULLy integrated analysis, design, manufacturing, and health-monitoring of COMPosite structures) research project.

作者簡介

Marco Petrolo is assistant professor and member of MUL2 (Multilayered Structures Multifield Analysis) research group at the Department of Mechanical and Aerospace Engineering of Politecnico di Torino, Italy. His research activity deals with the structural analysis of composite structures; refined beam, plate and shell models; component-wise approaches, damage analysis, and axiomatic/asymptotic analyses.