Generalized summation by parts operators: Second derivative and time-marching methods

  • David C. Del Rey Fernández*
  • , Pieter D. Boom
  • , David W. Zingg
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper describes extensions of the generalized summation-by-parts (GSBP) framework to the approximation of the second derivative with a variable coefficient and to time integration. GSBP operators for the second derivative lead to more efficient discretizations, relative to the classical finite-difference SBP approach, as they can require fewer nodes for a given order of accuracy. Similarly, for time integration, time-marching methods based on GSBP operators can be more efficient than those based on classical SBP operators, as they minimize the number of solution points which must be solved simultaneously. Furthermore, we demonstrate the link between GSBP operators and Runge-Kutta time-marching methods.

Original languageEnglish
Title of host publicationSpectral and High Order Methods for Partial Differential Equations, ICOSAHOM 2014, Selected papers from the ICOSAHOM
EditorsRobert M. Kirby, Martin Berzins, Jan S. Hesthaven
PublisherSpringer Verlag
Pages207-215
Number of pages9
ISBN (Print)9783319197999
DOIs
StatePublished - 2015
Externally publishedYes
Event10th International Conference on Spectral and High-Order Methods, ICOSAHOM 2014 - Salt Lake City, United States
Duration: 23 Jun 201427 Jun 2014

Publication series

NameLecture Notes in Computational Science and Engineering
Volume106
ISSN (Print)1439-7358

Conference

Conference10th International Conference on Spectral and High-Order Methods, ICOSAHOM 2014
Country/TerritoryUnited States
CitySalt Lake City
Period23/06/1427/06/14

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2015.

ASJC Scopus subject areas

  • Modeling and Simulation
  • General Engineering
  • Discrete Mathematics and Combinatorics
  • Control and Optimization
  • Computational Mathematics

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