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Design methods for reducing failure ...

Fuhrlander, Mona.

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Design methods for reducing failure probabilities with examples from electrical engineering

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Design methods for reducing failure probabilities with examples from electrical engineering/ by Mona Fuhrlander.
作者:	Fuhrlander, Mona.
出版者:	Cham :Springer Nature Switzerland : : 2023.,
面頁冊數:	xxii, 153 p. :ill. (some col.), digital ;24 cm.
附註:	"Doctoral Thesis accepted by Technische Universität Darmstadt, Germany."
內容註:	1. 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.
Contained By:	Springer Nature eBook
標題:	Electric apparatus and appliances - Design and construction. -
電子資源:	https://doi.org/10.1007/978-3-031-37019-9
ISBN:	9783031370199

Design methods for reducing failure probabilities with examples from electrical engineering
Fuhrlander, Mona.

Design methods for reducing failure probabilities with examples from electrical engineering[electronic resource] /by Mona Fuhrlander. - Cham :Springer Nature Switzerland :2023. - xxii, 153 p. :ill. (some col.), digital ;24 cm. - Springer theses,2190-5061. - Springer theses..

"Doctoral Thesis accepted by Technische Universität Darmstadt, Germany."

1. 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.

This 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.

ISBN: 9783031370199

Standard No.: 10.1007/978-3-031-37019-9doiSubjects--Topical Terms:

3665379
Electric apparatus and appliances
--Design and construction.

LC Class. No.: TK452

Dewey Class. No.: 621.31042

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520 $a This 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.
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