Filtering and Model Reduction of PDAEs with Stochastic Boundary Data
Abstract: In this paper we investigate state reconstruction for gas pipeline networks using model hierarchies derived from model order reduction techniques. The pipeline network is described by partial differential algebraic equations (PDAEs), for which model order reduction was extensively studied in [1]. We use and extend upon the aforementioned results to estimate the system state, when the boundary data is perturbated by an Ornstein‐Uhlenbeck process. We study the performance of state reconstruction based on the derived model hierarchy. Of special interest, hereby, is the relationship between the quality of the state estimation and the underlying reduced order model.
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Filtering and Model Reduction of PDAEs with Stochastic Boundary Data ; volume:19 ; number:1 ; year:2019 ; extent:2
Proceedings in applied mathematics and mechanics ; 19, Heft 1 (2019) (gesamt 2)
- Creator
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Stahl, Nadine
Marheineke, Nicole
- DOI
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10.1002/pamm.201900130
- URN
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urn:nbn:de:101:1-2022072208202920218362
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:27 AM CEST
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Associated
- Stahl, Nadine
- Marheineke, Nicole
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