Rypina Irina I.

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Last Name
Rypina
First Name
Irina I.
ORCID
0000-0002-2115-4522

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Now showing 1 - 2 of 2
  • Preprint
    Generalized Lagrangian coherent structures
    ( 2018-01-19) Balasuriya, Sanjeeva ; Ouellette, Nicholas T. ; Rypina, Irina I.
    The notion of a Lagrangian Coherent Structure (LCS) is by now well established as a way to capture transient coherent transport dynamics in unsteady and aperiodic fluid flows that are known over finite time. We show that the concept of an LCS can be generalized to capture coherence in other quantities of interest that are transported by, but not fully locked to, the fluid. Such quantities include those with dynamic, biological, chemical, or thermodynamic relevance, such as temperature, pollutant concentration, vorticity, kinetic energy, plankton density, and so on. We provide a conceptual framework for identifying the Generalized Lagrangian Coherent Structures (GLCSs) associated with such evolving quantities. We show how LCSs can be seen as a special case within this framework, and provide an overarching discussion of various methods for identifying LCSs. The utility of this more general viewpoint is highlighted through a variety of examples. We also show that although LCSs approximate GLCSs in certain limiting situations under restrictive assumptions on how the velocity field affects the additional quantities of interest, LCSs are not in general sufficient to describe their coherent transport.
  • Preprint
    Assimilating Lagrangian data for parameter estimation in a multiple-inlet system
    ( 2017-04) Slivinski, Laura ; Pratt, Lawrence J. ; Rypina, Irina I. ; Orescanin, Mara M. ; Raubenheimer, Britt ; MacMahan, Jamie ; Elgar, Steve
    Numerical models of ocean circulation often depend on parameters that must be tuned to match either results from laboratory experiments or field observations. This study demonstrates that an initial, suboptimal estimate of a parameter in a model of a small bay can be improved by assimilating observations of trajectories of passive drifters. The parameter of interest is the Manning's n coefficient of friction in a small inlet of the bay, which had been tuned to match velocity observations from 2011. In 2013, the geometry of the inlet had changed, and the friction parameter was no longer optimal. Results from synthetic experiments demonstrate that assimilation of drifter trajectories improves the estimate of n, both when the drifters are located in the same region as the parameter of interest and when the drifters are located in a different region of the bay. Real drifter trajectories from field experiments in 2013 also are assimilated, and results are compared with velocity observations. When the real drifters are located away from the region of interest, the results depend on the time interval (with respect to the full available trajectories) over which assimilation is performed. When the drifters are in the same region as the parameter of interest, the value of n estimated with assimilation yields improved estimates of velocity throughout the bay.