LEADER 04265nam 22006735 450 001 9910742495303321 005 20240628100813.0 010 $a3-031-37019-8 024 7 $a10.1007/978-3-031-37019-9 035 $a(MiAaPQ)EBC30722812 035 $a(Au-PeEL)EBL30722812 035 $a(DE-He213)978-3-031-37019-9 035 $a(PPN)272262986 035 $a(CKB)28100205600041 035 $a(EXLCZ)9928100205600041 100 $a20230828d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDesign Methods for Reducing Failure Probabilities with Examples from Electrical Engineering /$fby Mona Fuhrländer 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (168 pages) 225 1 $aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5061 311 08$aPrint version: Fuhrländer, Mona Design Methods for Reducing Failure Probabilities with Examples from Electrical Engineering Cham : Springer,c2023 9783031370182 327 $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. 330 $aThis book deals with efficient estimation and optimization methods to improve the design of electrotechnical devices under uncertainty. Uncertainties caused by manufacturing imperfections, natural material variations, or unpredictable environmental influences, may lead, in turn, to deviations in operation. This book describes two novel methods for yield (or failure probability) estimation. Both are hybrid methods that combine the accuracy of Monte Carlo with the efficiency of surrogate models. The SC-Hybrid approach uses stochastic collocation and adjoint error indicators. The non-intrusive GPR-Hybrid approach consists of a Gaussian process regression that allows surrogate model updates on the fly. Furthermore, the book proposes an adaptive Newton-Monte-Carlo (Newton-MC) method for efficient yield optimization. In turn, to solve optimization problems with mixed gradient information, two novel Hermite-type optimization methods are described. All the proposed methods have been numerically evaluated on two benchmark problems, such as a rectangular waveguide and a permanent magnet synchronous machine. Results showed that the new methods can significantly reduce the computational effort of yield estimation, and of single- and multi-objective yield optimization under uncertainty. All in all, this book presents novel strategies for quantification of uncertainty and optimization under uncertainty, with practical details to improve the design of electrotechnical devices, yet the methods can be used for any design process affected by uncertainties. . 410 0$aSpringer Theses, Recognizing Outstanding Ph.D. Research,$x2190-5061 606 $aTelecommunication 606 $aEngineering design 606 $aMathematical models 606 $aMicrowaves, RF Engineering and Optical Communications 606 $aEngineering Design 606 $aMathematical Modeling and Industrial Mathematics 606 $aAparells electrònics$2thub 606 $aDisseny$2thub 606 $aFallades de sistemes (Enginyeria)$2thub 608 $aLlibres electrònics$2thub 615 0$aTelecommunication. 615 0$aEngineering design. 615 0$aMathematical models. 615 14$aMicrowaves, RF Engineering and Optical Communications. 615 24$aEngineering Design. 615 24$aMathematical Modeling and Industrial Mathematics. 615 7$aAparells electrònics. 615 7$aDisseny 615 7$aFallades de sistemes (Enginyeria) 676 $a621.31042 676 $a621.31042 700 $aFuhrländer$b Mona$01425292 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910742495303321 996 $aDesign Methods for Reducing Failure Probabilities with Examples from Electrical Engineering$93555502 997 $aUNINA