scholarly journals Discrete data assimilation in the Lorenz and 2D Navier–Stokes equations

2011 ◽  
Vol 240 (18) ◽  
pp. 1416-1425 ◽  
Author(s):  
Kevin Hayden ◽  
Eric Olson ◽  
Edriss S. Titi
Keyword(s):  
Data Assimilation ◽  
Stokes Equations ◽  
Discrete Data ◽  
Navier Stokes ◽  
Navier Stokes Equations

scholarly journals A Computational Study of a Data Assimilation Algorithm for the Two-dimensional Navier-Stokes Equations

2016 ◽  
Vol 19 (4) ◽  
pp. 1094-1110 ◽  
Author(s):  
Masakazu Gesho ◽  
Eric Olson ◽  
Edriss S. Titi
Keyword(s):  
Data Assimilation ◽  
Stokes Equations ◽  
Computational Study ◽  
Nodal Point ◽  
Navier Stokes ◽  
Reference Solution ◽  
Two Dimensional ◽  
Computationally Efficient ◽  
Navier Stokes Equations ◽  
Feedback Control Theory

AbstractWe 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.

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