@Article{CiCP-19-4,
author = {},
title = {A Computational Study of a Data Assimilation Algorithm for the Two-Dimensional Navier-Stokes Equations},
journal = {Communications in Computational Physics},
year = {2016},
volume = {19},
number = {4},
pages = {1094--1110},
abstract = {<p style="text-align: justify;">We study the numerical performance of a continuous data assimilation
(downscaling) algorithm, based on ideas from feedback control theory, in the context
of the two-dimensional incompressible Navier-Stokes equations. Our model problem
is to recover an unknown reference solution, asymptotically in time, by using
continuous-in-time coarse-mesh nodal-point observational measurements of the velocity
field of this reference solution (subsampling), as might be measured by an array
of weather vane anemometers. Our calculations show that the required nodal observation
density is remarkably less than what is suggested by the analytical study; and
is in fact comparable to the number of numerically determining Fourier modes, which was
reported in an earlier computational study by the authors. Thus, this method is computationally
efficient and performs far better than the analytical estimates suggest.</p>},
issn = {1991-7120},
doi = {https://doi.org/10.4208/cicp.060515.161115a},
url = {https://global-sci.com/article/80286/a-computational-study-of-a-data-assimilation-algorithm-for-the-two-dimensional-navier-stokes-equations}
}