Continuous Data Assimilation for the Three Dimensional Navier-Stokes Equations

In this paper, we provide conditions, \emph{based solely on the observed data}, for the global well-posedness, regularity and convergence of the Azouni-Olson-Titi data assimilation algorithm (AOT algorithm) for a Leray-Hopf weak solutions of the three dimensional Navier-Stokes equations (3D NSE). The aforementioned conditions on the observations, which in this case comprise either of \emph{modal} or \emph{volume element observations}, are automatically satisfied for solutions that are globally regular and are uniformly bounded in the $H^1$-norm. However, neither regularity nor uniqueness is necessary for the efficacy of the AOT algorithm. To the best of our knowledge, this is the first such rigorous analysis of the AOT data assimilation algorithm for the 3D NSE.

[1]  Edriss S. Titi,et al.  Continuous Data Assimilation Using General Interpolant Observables , 2013, J. Nonlinear Sci..

[2]  E. Titi,et al.  Non-uniqueness of weak solutions to hyperviscous Navier–Stokes equations: on sharpness of J.-L. Lions exponent , 2018, Calculus of Variations and Partial Differential Equations.

[3]  Edriss S. Titi,et al.  Discrete data assimilation in the Lorenz and 2D Navier–Stokes equations , 2010, 1010.6105.

[4]  R. Temam,et al.  Determination of the solutions of the Navier-Stokes equations by a set of nodal values , 1984 .

[5]  E. Titi,et al.  A Data Assimilation Algorithm: the Paradigm of the 3D Leray-α Model of Turbulence , 2019, Partial Differential Equations Arising from Physics and Geometry.

[6]  V. Vicol,et al.  Nonuniqueness of weak solutions to the Navier-Stokes equation , 2017, Annals of Mathematics.

[7]  Edriss S. Titi,et al.  Feedback Control of Nonlinear Dissipative Systems by Finite Determining Parameters - A Reaction-diffusion Paradigm , 2013, 1301.6992.

[8]  Donald A. Jones,et al.  Determining finite volume elements for the 2D Navier-Stokes equations , 1992 .

[9]  Mimi Dai,et al.  Determining modes for the surface quasi-geostrophic equation , 2015, Physica D: Nonlinear Phenomena.

[10]  A. Farhat,et al.  Data Assimilation in Large Prandtl Rayleigh-Bénard Convection from Thermal Measurements , 2019, SIAM J. Appl. Dyn. Syst..

[11]  Edriss S. Titi,et al.  A Discrete Data Assimilation Scheme for the Solutions of the Two-Dimensional Navier-Stokes Equations and Their Statistics , 2016, SIAM J. Appl. Dyn. Syst..

[12]  Andrew J. Majda,et al.  Filtering Complex Turbulent Systems , 2012 .

[13]  Pierre Gilles Lemarié-Rieusset,et al.  Recent Developments in the Navier-Stokes Problem , 2002 .

[14]  Edriss S. Titi,et al.  Determining nodes, finite difference schemes and inertial manifolds , 1991 .

[15]  Mimi Dai,et al.  Determining modes for the 3D Navier-Stokes equations , 2015, 1507.05908.

[16]  Hakima Bessaih,et al.  Continuous data assimilation with stochastically noisy data , 2014, 1406.1533.

[17]  Marc Bocquet,et al.  Data Assimilation: Methods, Algorithms, and Applications , 2016 .

[18]  M. U. Altaf,et al.  Downscaling the 2D Bénard convection equations using continuous data assimilation , 2015, Computational Geosciences.

[19]  Edriss S. Titi,et al.  Assimilation of Nearly Turbulent Rayleigh–Bénard Flow Through Vorticity or Local Circulation Measurements: A Computational Study , 2017, Journal of Scientific Computing.

[20]  Edriss S. Titi,et al.  Continuous data assimilation for the three-dimensional Brinkman–Forchheimer-extended Darcy model , 2015, 1502.00964.

[21]  Edriss S. Titi,et al.  Continuous data assimilation for the 2D Bénard convection through velocity measurements alone , 2014, 1410.1767.

[22]  Eugenia Kalnay,et al.  Atmospheric Modeling, Data Assimilation and Predictability , 2002 .

[23]  D. S. McCormick,et al.  Accuracy and stability of filters for dissipative PDEs , 2012, 1203.5845.

[24]  Adam Larios,et al.  Parameter Recovery for the 2 Dimensional Navier-Stokes Equations via Continuous Data Assimilation , 2020, SIAM J. Sci. Comput..

[25]  Xin T. Tong,et al.  Nonlinear stability and ergodicity of ensemble based Kalman filters , 2015, 1507.08307.

[26]  Animikh Biswas,et al.  Higher-order synchronization for a data assimilation algorithm for the 2D Navier–Stokes equations , 2017 .

[27]  Xiaoming Wang,et al.  Asymptotic behavior of the global attractors to the Boussinesq system for Rayleigh‐Bénard convection at large Prandtl number , 2007 .

[28]  Andrew J Majda,et al.  Nonlinear stability of the ensemble Kalman filter with adaptive covariance inflation , 2015, 1507.08319.

[29]  Colin J. Cotter,et al.  Probabilistic Forecasting and Bayesian Data Assimilation , 2015 .

[30]  Joshua Hudson,et al.  Numerical efficacy study of data assimilation for the 2D magnetohydrodynamic equations , 2019, Journal of Computational Dynamics.

[31]  J. Blum,et al.  A nudging-based data assimilation method: the Back and Forth Nudging (BFN) algorithm , 2008 .

[32]  James C. Robinson Infinite-Dimensional Dynamical Systems: An Introduction to Dissipative Parabolic PDEs and the Theory of Global Attractors , 2001 .

[33]  L. E. Fraenkel,et al.  NAVIER-STOKES EQUATIONS (Chicago Lectures in Mathematics) , 1990 .

[34]  Débora A. F. Albanez,et al.  Continuous data assimilation algorithm for simplified Bardina model , 2018 .

[35]  Leo G. Rebholz,et al.  Global in time stability and accuracy of IMEX-FEM data assimilation schemes for Navier–Stokes equations , 2018, Computer Methods in Applied Mechanics and Engineering.

[36]  A. M. Stuart,et al.  Accuracy and stability of the continuous-time 3DVAR filter for the Navier–Stokes equation , 2012, 1210.1594.

[37]  F. Thau Observing the state of non-linear dynamic systems† , 1973 .

[38]  A. Stuart,et al.  Well-posedness and accuracy of the ensemble Kalman filter in discrete and continuous time , 2013, 1310.3167.

[39]  Mimi Dai,et al.  Kolmogorov's dissipation number and the number of degrees of freedom for the 3D Navier–Stokes equations , 2015, Proceedings of the Royal Society of Edinburgh: Section A Mathematics.

[40]  Edriss S. Titi,et al.  Continuous data assimilation for the three-dimensional Navier-Stokes-α model , 2016, Asymptot. Anal..

[41]  E. Titi,et al.  On the Charney Conjecture of Data Assimilation Employing Temperature Measurements Alone: The Paradigm of 3D Planetary Geostrophic Model , 2016, 1608.04770.

[42]  Yuan Pei Continuous data assimilation for the 3D primitive equations of the ocean , 2018, Communications on Pure & Applied Analysis.

[43]  G. Seregin A Certain Necessary Condition of Potential Blow up for Navier-Stokes Equations , 2012 .

[44]  Edriss S. Titi,et al.  Downscaling data assimilation algorithm with applications to statistical solutions of the Navier-Stokes equations , 2017 .

[45]  J. Serrin The initial value problem for the Navier-Stokes equations , 1963 .

[46]  Xiaoming Wang A Note on Long Time Behavior of Solutions to the Boussinesq System at Large , 2004 .

[47]  C. Foiaș,et al.  Sur le comportement global des solutions non-stationnaires des équations de Navier-Stokes en dimension $2$ , 1967 .

[48]  Edriss S. Titi,et al.  A Computational Study of a Data Assimilation Algorithm for the Two-dimensional Navier-Stokes Equations , 2015, 1505.01234.

[49]  Traian Iliescu,et al.  Continuous data assimilation reduced order models of fluid flow , 2019, Computer Methods in Applied Mechanics and Engineering.

[50]  O. Knio,et al.  Efficient dynamical downscaling of general circulation models using continuous data assimilation , 2019, Quarterly Journal of the Royal Meteorological Society.

[51]  Edriss S. Titi,et al.  Data Assimilation algorithm for 3D B\'enard convection in porous media employing only temperature measurements , 2015, 1506.08678.

[52]  L. Biferale,et al.  Synchronization to Big Data: Nudging the Navier-Stokes Equations for Data Assimilation of Turbulent Flows , 2019, Physical Review X.