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Proper Orthogonal Decomposition and Model Order Reduction in Computational Electromagnetics.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Proper Orthogonal Decomposition and Model Order Reduction in Computational Electromagnetics./
作者:	de Lima Nicolini, Julio.
面頁冊數:	1 online resource (153 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-04, Section: B.
Contained By:	Dissertations Abstracts International85-04B.
標題:	Electrical engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798380595490

Proper Orthogonal Decomposition and Model Order Reduction in Computational Electromagnetics.
de Lima Nicolini, Julio.

Proper Orthogonal Decomposition and Model Order Reduction in Computational Electromagnetics. - 1 online resource (153 pages)

Source: Dissertations Abstracts International, Volume: 85-04, Section: B.

Thesis (Ph.D.)--The Ohio State University, 2023.

Includes bibliographical references

We present a discussion on the reduced-order modeling of electromagnetic simulation in general, and kinetic plasma simulations in particular, using the Proper Orthogonal Decomposition technique. Computational electromagnetics has been an important tool for physicists and engineers since the mid-1960s, when the increasing availability of modern high-speed computers started to allow the numerical solution of practical problems for which closed-form analytic solutions did not exist or were impractical to calculate.The study of kinetic plasmas is of great interest both for theoretical exploration and technological applications such as design of vacuum electronic devices, the study of the interaction of space-borne assets and cosmic radiation, fusion experiments, among others. Due to the theoretical complexity of these problems and the difficulty in performing physical experiments, simulations are instrumental for obtaining new insights or developing new device designs by resolving the field and plasma behaviors when changes are made. Several variants of simulations exist, but particle-in-cell algorithms for solving particle dynamics coupled with finite-differences or finite-elements field solvers are particularly successful. Despite their success, such algorithms are still constrained by computational cost such as processing time and memory/storage limitations.The Proper Orthogonal Decomposition is a technique that extracts the spatiotemporal behavior from a function of interest or a set of data points. This spatiotemporal behavior is characterized by a set of coupled spatial and temporal modes, which makes the Proper Orthogonal Decomposition especially suitable for analyses and applications in dynamic systems; it has been used for creation of reduced-order models in the past, especially in the fluid dynamics community where it originated from but also in many other areas.We have explored the application of the Proper Orthogonal Decomposition technique to computational electromagnetics with a focus on kinetic plasma simulations, using finite-element-based particle-in-cell algorithms. We show that the decomposition of the electromagnetic field behavior in such simulations is able to generate greatly reduced models while keeping a controllable accuracy threshold even for complicated non-linear cases, and extend the method to be applicable in advection-dominated problems, which are historically problematic for mode-based algorithms.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380595490Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

Finite-elementsIndex Terms--Genre/Form:

554714
Electronic books.

Proper Orthogonal Decomposition and Model Order Reduction in Computational Electromagnetics.
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520 $a We present a discussion on the reduced-order modeling of electromagnetic simulation in general, and kinetic plasma simulations in particular, using the Proper Orthogonal Decomposition technique. Computational electromagnetics has been an important tool for physicists and engineers since the mid-1960s, when the increasing availability of modern high-speed computers started to allow the numerical solution of practical problems for which closed-form analytic solutions did not exist or were impractical to calculate.The study of kinetic plasmas is of great interest both for theoretical exploration and technological applications such as design of vacuum electronic devices, the study of the interaction of space-borne assets and cosmic radiation, fusion experiments, among others. Due to the theoretical complexity of these problems and the difficulty in performing physical experiments, simulations are instrumental for obtaining new insights or developing new device designs by resolving the field and plasma behaviors when changes are made. Several variants of simulations exist, but particle-in-cell algorithms for solving particle dynamics coupled with finite-differences or finite-elements field solvers are particularly successful. Despite their success, such algorithms are still constrained by computational cost such as processing time and memory/storage limitations.The Proper Orthogonal Decomposition is a technique that extracts the spatiotemporal behavior from a function of interest or a set of data points. This spatiotemporal behavior is characterized by a set of coupled spatial and temporal modes, which makes the Proper Orthogonal Decomposition especially suitable for analyses and applications in dynamic systems; it has been used for creation of reduced-order models in the past, especially in the fluid dynamics community where it originated from but also in many other areas.We have explored the application of the Proper Orthogonal Decomposition technique to computational electromagnetics with a focus on kinetic plasma simulations, using finite-element-based particle-in-cell algorithms. We show that the decomposition of the electromagnetic field behavior in such simulations is able to generate greatly reduced models while keeping a controllable accuracy threshold even for complicated non-linear cases, and extend the method to be applicable in advection-dominated problems, which are historically problematic for mode-based algorithms.
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