Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-14669
For citation please use:
Main Title: Adaptive Refinement Strategies for the Simulation of Gas Flow in Networks using a Model Hierarchy
Author(s): Domschke, Pia
Dua, Aseem
Stolwijk, Jeroen J.
Lang, Jens
Mehrmann, Volker
Type: Research Paper
URI: https://depositonce.tu-berlin.de/handle/11303/15896
http://dx.doi.org/10.14279/depositonce-14669
License: http://rightsstatements.org/vocab/InC/1.0/
Abstract: A model hierarchy that is based on the one-dimensional isothermal Euler equations of fluid dynamics is used for the simulation and optimisation of gas flow through a pipeline network. Adaptive refinement strategies have the aim of bringing the simulation error below a prescribed tolerance while keeping the computational costs low. While spatial and temporal stepsize adaptivity is well studied in the literature, model adaptivity is a new field of research. The problem of finding an optimal refinement strategy that combines these three types of adaptivity is a generalisation of the unbounded knapsack problem. A refinement strategy that is currently used in gas flow simulation software is compared to two novel greedy-like strategies. Both a theoretical experiment and a realistic gas flow simulation show that the novel strategies significantly outperform the current refinement strategy with respect to the computational cost incurred.
Subject(s): gas supply networks
model hierarchy
error estimators
model adaptivity
refinement strategies
Issue Date: 31-Jan-2017
Date Available: 17-Dec-2021
Language Code: en
DDC Class: 510 Mathematik
MSC 2000: 65K99 None of the above, but in this section
65Z05 Applications to physics
Series: Preprint-Reihe des Instituts für Mathematik, Technische Universität Berlin
Series Number: 2017, 03
ISSN: 2197-8085
TU Affiliation(s): Fak. 2 Mathematik und Naturwissenschaften » Inst. Mathematik
Appears in Collections:Technische Universität Berlin » Publications

Files in This Item:
Preprint-03-2017.pdf
Format: Adobe PDF | Size: 503.43 kB
DownloadShow Preview
Thumbnail

Item Export Bar

Items in DepositOnce are protected by copyright, with all rights reserved, unless otherwise indicated.