A Computational Study of a Data Assimilation Algorithm for the Two-dimensional Navier--Stokes Equations
For researchers in computational fluid dynamics and data assimilation, this work provides evidence that a feedback control-based algorithm can recover reference solutions with surprisingly sparse observations, outperforming theoretical bounds.
The paper studies a continuous data assimilation algorithm for the 2D Navier-Stokes equations, showing that the required observation density is much lower than analytical predictions and comparable to the number of determining Fourier modes, demonstrating computational efficiency.
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 that what is suggested by the analytical study; and is in fact comparable to the {\it 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.