Applications and Metrology at Nanometer Scale 1. Abdelkhalak El Hami

165

      178  166

      179  167

      180  168

      181  169

      182  170

      183  171

      184  172

      185  173

      186  174

      187 175

      188  176

      189  177

      190 178

      191  179

      192  180

      193  181

      194  182

      195  183

      196  184

      197 185

      198  186

      199  187

      200  188

      201  189

      202  190

      203  191

      204 192

      205 193

      206 194

      207 195

      208  196

      209  197

      210  198

      211  199

      212  200

      213  201

      214  202

      215  203

      216  204

      217  205

      218  206

      219  207

      220  208

      221  209

      222  210

      223  211

      224 212

      225 213

      226 214

      227 215

      228 216

      229  217

      230 218

      231  219

      232  220

      233  221

      234  222

      235  223

      236  224

      237  225

      238  226

      239  227

       Reliability of Multiphysical Systems Set

      coordinated by

      Abdelkhalak El Hami

      Volume 9

      Applications and Metrology at Nanometer Scale 1

       Smart Materials, Electromagnetic Waves and Uncertainties

      Pierre-Richard Dahoo

      Philippe Pougnet

      Abdelkhalak El Hami

First published 2021 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.

      Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:

      ISTE Ltd

      27-37 St George’s Road

      London SW19 4EU

      UK

       www.iste.co.uk

      John Wiley & Sons, Inc.

      111 River Street

      Hoboken, NJ 07030

      USA

       www.wiley.com

      © ISTE Ltd 2021

      The rights of Pierre-Richard Dahoo, Philippe Pougnet and Abdelkhalak El Hami to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.

      Library of Congress Control Number: 2020946679

      British Library Cataloguing-in-Publication Data

      A CIP record for this book is available from the British Library

      ISBN 978-1-78630-640-1

      Preface

      The various actions taken worldwide in support of sustainable development and greenhouse gas emission control have led to increasingly restrictive regulations. Manufacturers in the automotive field have thus developed innovative mechatronic systems, enabling various vehicle functions to go electric. Confronted with the globalization of exchanges that has generated stronger competition and a surge of new product performances, companies in the sector of embedded mechatronic systems are developing new products at an increasingly faster rate.

      In other domains, to achieve volume or mass reduction or curb energy dissipation, manufacturers of mechatronic systems are developing new assembly methods based on multimaterials (e.g. composite materials, hybrids) or on innovative nanomaterials (e.g. carbon nanotubes). Modeling is essential to reduce product development cost, shrink time to market, understand failure mechanisms occurring in the operating conditions and optimize design before launching production. The reliability-based design optimization (RBDO) method is a modeling approach that optimizes design, guarantees the required high level of performance and takes into account the variability in the manufacturing process as well as the climatic variability in the use conditions. The efficiency of RBDO, however, depends on a solid understanding of the failure mechanisms caused by aging.

Creating a model of a dynamic system often involves developing a simplified model of its behavior based on realistic hypotheses and on the key parameters that are required for its functioning. The dynamic behavior of this modeled system is ruled by partial differential equations (PDEs). The model is then improved by introducing elements or parameters that were not initially included and by improving the set