Inverse Analyses with Model Reduction Proper Orthogonal Decompositio
In this self-consistent monograph, the author gathers and describes different mathematical techniques and combines all together to form practical procedures for the inverse analyses.It puts together topics coming from mathematical programming, with soft c
- PDF / 7,943,807 Bytes
- 215 Pages / 439.37 x 666.142 pts Page_size
- 52 Downloads / 229 Views
Series Editor K.J. Bathe Massachusetts Institute of Technology, Cambridge, MA, USA
For other titles published in this series, go to http://www.springer.com/series/4449
.
Vladimir Buljak
Inverse Analyses with Model Reduction Proper Orthogonal Decomposition in Structural Mechanics
Vladimir Buljak Politecnico di Milano Dipartimento di Ingegneria Strutturale Piazza Leonardo da Vinci 32 20133 Milano Italy [email protected]
ISSN 1860-482X e-ISSN 1860-4838 ISBN 978-3-642-22702-8 e-ISBN 978-3-642-22703-5 DOI 10.1007/978-3-642-22703-5 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2011941196 # Springer-Verlag Berlin Heidelberg 2012 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Foreword
The methodology of inverse analysis, the origins of which may be regarded as remote and deeply rooted in the history of structural mechanics, has in relatively recent times, emerged as a modern and fast growing area of engineering sciences. In several technological fields, evident are importance and usefulness of reliable transition from experimental data on systems, structures “in primus”, to quantitative assessments of crucial properties and possible damages in those systems. Such a kind of assessment means accurate estimations of parameters hidden in mathematical models at present available to simulate and predict the system behavior in service. The achievement of such ambitious task clearly requires a synergistic convergence of diverse scientific fields: mathematics, usually with their traditional concepts and solution methods (e.g., ill-posedness of problems; minimization/ maximization of nonconvex functions), experimental developments in terms of suitable devices, computational techniques and relevant tools. The growth of computers according to “Moore’s law” in the last four-five decades has been, and still is, essential also for the expansion of the inverse analysis developments and applications. Such a link was evidenced in 1986 by Richard Feynman through his celebrated warning on computers (“garbage in, garbage out”) after the disaster of the Challenger. The role of mathematics (specifically, math
Data Loading...
