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Numerical approximation of the magne...

Romer, Ulrich.

Numerical approximation of the magnetoquasistatic model with uncertaintiesapplications in magnet design /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Numerical approximation of the magnetoquasistatic model with uncertaintiesby Ulrich Romer.
Reminder of title:	applications in magnet design /
Author:	Romer, Ulrich.
Published:	Cham :Springer International Publishing :2016.
Description:	xxii, 114 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Approximation theory.
Online resource:	http://dx.doi.org/10.1007/978-3-319-41294-8
ISBN:	9783319412948$q(electronic bk.)

Numerical approximation of the magnetoquasistatic model with uncertaintiesapplications in magnet design /
Romer, Ulrich.

Numerical approximation of the magnetoquasistatic model with uncertaintiesapplications in magnet design /[electronic resource] :by Ulrich Romer. - Cham :Springer International Publishing :2016. - xxii, 114 p. :ill., digital ;24 cm. - Springer theses,2190-5053. - Springer theses..

Introduction -- Magnetoquasistatic Approximation of Maxwell's Equations, Uncertainty Quantification Principles -- Magnetoquasistatic Model and its Numerical Approximation -- Parametric Model, Continuity and First Order Sensitivity Analysis -- Uncertainty Quantification -- Uncertainty Quantification for Magnets -- Conclusion and Outlook.

This book presents a comprehensive mathematical approach for solving stochastic magnetic field problems. It discusses variability in material properties and geometry, with an emphasis on the preservation of structural physical and mathematical properties. It especially addresses uncertainties in the computer simulation of magnetic fields originating from the manufacturing process. Uncertainties are quantified by approximating a stochastic reformulation of the governing partial differential equation, demonstrating how statistics of physical quantities of interest, such as Fourier harmonics in accelerator magnets, can be used to achieve robust designs. The book covers a number of key methods and results such as: a stochastic model of the geometry and material properties of magnetic devices based on measurement data; a detailed description of numerical algorithms based on sensitivities or on a higher-order collocation; an analysis of convergence and efficiency; and the application of the developed model and algorithms to uncertainty quantification in the complex magnet systems used in particle accelerators.

ISBN: 9783319412948$q(electronic bk.)

Standard No.: 10.1007/978-3-319-41294-8doiSubjects--Topical Terms:

185327
Approximation theory.

LC Class. No.: QA221

Dewey Class. No.: 511.4

Numerical approximation of the magnetoquasistatic model with uncertaintiesapplications in magnet design /
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505 0 $a Introduction -- Magnetoquasistatic Approximation of Maxwell's Equations, Uncertainty Quantification Principles -- Magnetoquasistatic Model and its Numerical Approximation -- Parametric Model, Continuity and First Order Sensitivity Analysis -- Uncertainty Quantification -- Uncertainty Quantification for Magnets -- Conclusion and Outlook.
520 $a This book presents a comprehensive mathematical approach for solving stochastic magnetic field problems. It discusses variability in material properties and geometry, with an emphasis on the preservation of structural physical and mathematical properties. It especially addresses uncertainties in the computer simulation of magnetic fields originating from the manufacturing process. Uncertainties are quantified by approximating a stochastic reformulation of the governing partial differential equation, demonstrating how statistics of physical quantities of interest, such as Fourier harmonics in accelerator magnets, can be used to achieve robust designs. The book covers a number of key methods and results such as: a stochastic model of the geometry and material properties of magnetic devices based on measurement data; a detailed description of numerical algorithms based on sensitivities or on a higher-order collocation; an analysis of convergence and efficiency; and the application of the developed model and algorithms to uncertainty quantification in the complex magnet systems used in particle accelerators.
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