Generalized spectral decomposition approach to a stochastic finite integration technique electrokinetic formulation

IF 0.6 4区计算机科学 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC

Applied Computational Electromagnetics Society Journal Pub Date : 2014-06-16 DOI:10.1049/CP.2014.0171

L. Codecasa, L. Rienzo

引用次数: 3

Abstract

In order to efficiently solve a stochastic finite integration technique electrokinetic formulation a recently proposed generalized spectral decomposition approach is applied. Compared to a classical polynomial chaos expansion, the proposed approach dramatically reduces the computational burden. The results are validated by comparison with those obtained with high order polynomial chaos expansion, taken as the reference solution.

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本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Applied Computational Electromagnetics Society Journal 工程技术-电信学

CiteScore

1.60

自引率

28.60%

发文量

审稿时长

9 months

期刊介绍： The ACES Journal is devoted to the exchange of information in computational electromagnetics, to the advancement of the state of the art, and to the promotion of related technical activities. A primary objective of the information exchange is the elimination of the need to "re-invent the wheel" to solve a previously solved computational problem in electrical engineering, physics, or related fields of study. The ACES Journal welcomes original, previously unpublished papers, relating to applied computational electromagnetics. All papers are refereed. A unique feature of ACES Journal is the publication of unsuccessful efforts in applied computational electromagnetics. Publication of such material provides a means to discuss problem areas in electromagnetic modeling. Manuscripts representing an unsuccessful application or negative result in computational electromagnetics is considered for publication only if a reasonable expectation of success (and a reasonable effort) are reflected. The technical activities promoted by this publication include code validation, performance analysis, and input/output standardization; code or technique optimization and error minimization; innovations in solution technique or in data input/output; identification of new applications for electromagnetics modeling codes and techniques; integration of computational electromagnetics techniques with new computer architectures; and correlation of computational parameters with physical mechanisms.