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Adaptive Single- and Multilevel Stochastic Collocation Methods for Uncertain Gas Transport in Large-Scale Networks

Lang, Jens ; Domschke, Pia ; Strauch, Elisa
Hrsg.: Sevilla, Rubén ; Perotto, Simona ; Morgan, Kenneth (2022)
Adaptive Single- and Multilevel Stochastic Collocation Methods for Uncertain Gas Transport in Large-Scale Networks.
In: Mesh Generation and Adaptation
doi: 10.1007/978-3-030-92540-6_6
Buchkapitel, Bibliographie

Kurzbeschreibung (Abstract)

In this paper, we are concerned with the quantification of uncertainties that arise from intra-day oscillations in the demand for natural gas transported through large-scale networks. The short-term transient dynamics of the gas flow is modelled by a hierarchy of hyperbolic systems of balance laws based on the isentropic Euler equations. We extend a novel adaptive strategy for solving elliptic PDEs with random data, recently proposed and analysed by Lang, Scheichl, and Silvester [J. Comput. Phys., 419:109692, 2020], to uncertain gas transport problems. Sample-dependent adaptive meshes and a model refinement in the physical space is combined with adaptive anisotropic sparse Smolyak grids in the stochastic space. A single-level approach which balances the discretization errors of the physical and stochastic approximations and a multilevel approach which additionally minimizes the computational costs are considered. Two examples taken from a public gas library demonstrate the reliability of the error control of expectations calculated from random quantities of interest, and the further use of stochastic interpolants to, e.g., approximate probability density functions of minimum and maximum pressure values at the exits of the network.

Typ des Eintrags: Buchkapitel
Erschienen: 2022
Herausgeber: Sevilla, Rubén ; Perotto, Simona ; Morgan, Kenneth
Autor(en): Lang, Jens ; Domschke, Pia ; Strauch, Elisa
Art des Eintrags: Bibliographie
Titel: Adaptive Single- and Multilevel Stochastic Collocation Methods for Uncertain Gas Transport in Large-Scale Networks
Sprache: Englisch
Publikationsjahr: 21 Februar 2022
Verlag: Springer
Buchtitel: Mesh Generation and Adaptation
Reihe: SEMA SIMAI Springer Series
Band einer Reihe: 30
DOI: 10.1007/978-3-030-92540-6_6
Kurzbeschreibung (Abstract):

In this paper, we are concerned with the quantification of uncertainties that arise from intra-day oscillations in the demand for natural gas transported through large-scale networks. The short-term transient dynamics of the gas flow is modelled by a hierarchy of hyperbolic systems of balance laws based on the isentropic Euler equations. We extend a novel adaptive strategy for solving elliptic PDEs with random data, recently proposed and analysed by Lang, Scheichl, and Silvester [J. Comput. Phys., 419:109692, 2020], to uncertain gas transport problems. Sample-dependent adaptive meshes and a model refinement in the physical space is combined with adaptive anisotropic sparse Smolyak grids in the stochastic space. A single-level approach which balances the discretization errors of the physical and stochastic approximations and a multilevel approach which additionally minimizes the computational costs are considered. Two examples taken from a public gas library demonstrate the reliability of the error control of expectations calculated from random quantities of interest, and the further use of stochastic interpolants to, e.g., approximate probability density functions of minimum and maximum pressure values at the exits of the network.

Fachbereich(e)/-gebiet(e): DFG-Sonderforschungsbereiche (inkl. Transregio)
DFG-Sonderforschungsbereiche (inkl. Transregio) > Transregios
DFG-Sonderforschungsbereiche (inkl. Transregio) > Transregios > TRR 154 Mathematische Modellierung, Simulation und Optimierung am Beispiel von Gasnetzwerken
04 Fachbereich Mathematik
04 Fachbereich Mathematik > Numerik und wissenschaftliches Rechnen
TU-Projekte: DFG|TRR154|B01 Fr. Dr. Domschke
Hinterlegungsdatum: 29 Nov 2022 06:36
Letzte Änderung: 29 Nov 2022 06:36
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