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Geodesic motion on groups of diffeomorphisms with H1 metric as geometric generalised Lagrangian mean theory


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Oliver, Marcel ; Vasylkevych, Sergiy:
Geodesic motion on groups of diffeomorphisms with H1 metric as geometric generalised Lagrangian mean theory.
In: Geophysical & astrophysical fluid dynamics. 113 (2019) 5-6. - S. 466-490.
ISSN 1029-0419 ; 0309-1929


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Generalized Lagrangian mean theories are used to analyse the interactions between mean flows and fluctuations, where the decomposition is based on a Lagrangian description of the flow. A systematic geometric framework was recently developed by Gilbert and Vanneste who cast the decomposition in terms of intrinsic operations on the group of volume preserving diffeomorphisms or on the full diffeomorphism group. In this setting, the mean of an ensemble of maps can be defined as the Riemannian centre of mass on either of these groups. We apply this decomposition in the context of Lagrangian averaging where equations of motion for the mean flow arise via a variational principle from a mean Lagrangian, obtained from the kinetic energy Lagrangian of ideal fluid flow via a small amplitude expansion for the fluctuations. We show that the Euler-α equations arise as Lagrangian averaged Euler equations when using the L2-geodesic mean on the volume preserving diffeomorphism group of a manifold without boundaries, imposing a “Taylor hypothesis”, which states that first order fluctuations are transported as a vector field by the mean flow, and assuming that fluctuations are statistically nearly isotropic. Similarly, the EPDiff equations arise as the Lagrangian averaged Burgers' equations using the same argument on the full diffeomorphism group. A serious drawback of this construction is that the assumptions of Lie transport of the fluctuation vector field and isotropy of fluctuations cannot persist except for an asymptotically vanishing interval of time. To remedy the problem of persistence of isotropy, we suggest adding strong mean-reverting stochastic term to the Taylor hypothesis and identify a scaling regime in which the inclusion of the stochastic term leads to the same averaged equations up to a constant.

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Sprache des Eintrags:Englisch
Institutionen der Universität:Mathematisch-Geographische Fakultät > Mathematik > Lehrstuhl für Mathematik - Angewandte Mathematik
Mathematisch-Geographische Fakultät > Mathematik > Mathematisches Institut für Maschinelles Lernen und Data Science (MIDS)
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DOI / URN / ID:10.1080/03091929.2019.1639697
Open Access: Freie Zugänglichkeit des Volltexts?:Nein
Verlag:Taylor & Francis
Die Zeitschrift ist nachgewiesen in:
Titel an der KU entstanden:Nein
Eingestellt am: 14. Apr 2022 15:21
Letzte Änderung: 06. Jun 2023 15:28
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