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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