001476556 000__ 04403cam\\22006257i\4500
001476556 001__ 1476556
001476556 003__ OCoLC
001476556 005__ 20231003174426.0
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001476556 008__ 230906s2023\\\\sz\a\\\\ob\\\\000\0\eng\d
001476556 019__ $$a1395947430$$a1396063698
001476556 020__ $$a9783031370199$$q(electronic bk.)
001476556 020__ $$a3031370198$$q(electronic bk.)
001476556 020__ $$z9783031370182
001476556 020__ $$z303137018X
001476556 0247_ $$a10.1007/978-3-031-37019-9$$2doi
001476556 035__ $$aSP(OCoLC)1396235565
001476556 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dYDX
001476556 049__ $$aISEA
001476556 050_4 $$aTK452
001476556 08204 $$a621.31042$$223/eng/20230906
001476556 1001_ $$aFuhrländer, Mona,$$eauthor.
001476556 24510 $$aDesign methods for reducing failure probabilities with examples from electrical engineering /$$cMona Fuhrländer.
001476556 264_1 $$aCham :$$bSpringer,$$c2023.
001476556 300__ $$a1 online resource (xxii, 153 pages) :$$billustrations (some color).
001476556 336__ $$atext$$btxt$$2rdacontent
001476556 337__ $$acomputer$$bc$$2rdamedia
001476556 338__ $$aonline resource$$bcr$$2rdacarrier
001476556 4901_ $$aSpringer theses,$$x2190-5061
001476556 500__ $$a"Doctoral thesis accepted by Technische Universität Darmstadt, Germany."
001476556 504__ $$aIncludes bibliographical references.
001476556 5050_ $$a1. Introduction -- 2. Modeling -- 3. Mathematical foundations of robust design -- 4. Yield Estimation -- 5. Yield optimization -- 6. Numerical applications and results -- 7. Conclusion and outlook -- Appendix A: Geometry and material specifications for the PMSM.
001476556 506__ $$aAccess limited to authorized users.
001476556 520__ $$aThis book deals with efficient estimation and optimization methods to improve the design of electrotechnical devices under uncertainty. Uncertainties caused by manufacturing imperfections, natural material variations, or unpredictable environmental influences, may lead, in turn, to deviations in operation. This book describes two novel methods for yield (or failure probability) estimation. Both are hybrid methods that combine the accuracy of Monte Carlo with the efficiency of surrogate models. The SC-Hybrid approach uses stochastic collocation and adjoint error indicators. The non-intrusive GPR-Hybrid approach consists of a Gaussian process regression that allows surrogate model updates on the fly. Furthermore, the book proposes an adaptive Newton-Monte-Carlo (Newton-MC) method for efficient yield optimization. In turn, to solve optimization problems with mixed gradient information, two novel Hermite-type optimization methods are described. All the proposed methods have been numerically evaluated on two benchmark problems, such as a rectangular waveguide and a permanent magnet synchronous machine. Results showed that the new methods can significantly reduce the computational effort of yield estimation, and of single- and multi-objective yield optimization under uncertainty. All in all, this book presents novel strategies for quantification of uncertainty and optimization under uncertainty, with practical details to improve the design of electrotechnical devices, yet the methods can be used for any design process affected by uncertainties.
001476556 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed September 6, 2023).
001476556 650_0 $$aElectric apparatus and appliances$$xDesign and construction.
001476556 650_0 $$aFailure analysis (Engineering)
001476556 655_0 $$aElectronic books.
001476556 77608 $$iPrint version:$$aFuhrländer, Mona$$tDesign Methods for Reducing Failure Probabilities with Examples from Electrical Engineering$$dCham : Springer,c2023$$z9783031370182
001476556 830_0 $$aSpringer theses,$$x2190-5061
001476556 852__ $$bebk
001476556 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-37019-9$$zOnline Access$$91397441.1
001476556 909CO $$ooai:library.usi.edu:1476556$$pGLOBAL_SET
001476556 980__ $$aBIB
001476556 980__ $$aEBOOK
001476556 982__ $$aEbook
001476556 983__ $$aOnline
001476556 994__ $$a92$$bISE