Hostname: page-component-8448b6f56d-dnltx Total loading time: 0 Render date: 2024-04-25T05:56:17.626Z Has data issue: false hasContentIssue false

State estimation in wall-bounded flow systems. Part 3. The ensemble Kalman filter

Published online by Cambridge University Press:  03 August 2011

C. H. COLBURN*
Affiliation:
Department of Mechanical Engineering, University of California, San Diego La Jolla, CA 92093, USA
J. B. CESSNA
Affiliation:
Numerica Corporation, 4850 Hahns Peak Drive, Suite 200, Loveland, CO 80538, USA
T. R. BEWLEY
Affiliation:
Department of Mechanical Engineering, University of California, San Diego La Jolla, CA 92093, USA
*
Email address for correspondence: ccolburn@ucsd.edu

Abstract

State estimation of turbulent near-wall flows based on wall measurements is one of the key pacing items in model-based flow control, with low-Re channel flow providing the canonical testbed. Model-based control formulations in such settings are often separated into two subproblems: estimation of the near-wall flow state via skin friction and pressure measurements at the wall, and (based on this estimate) control of the near-wall flow field fluctuations via actuation of the fluid velocity at the wall. In our experience, the turbulent state estimation sub-problem has consistently proven to be the more difficult of the two. Though many estimation strategies have been tested on this problem (by our group and others), none have accurately captured the turbulent flow state at the outer boundary of the buffer layer (5 ≤ y+ ≤ 30), which is deemed to be an important milestone, as this is the approximate range of the characteristic near-wall turbulent structures, the accurate estimation of which is important for the control problem. Leveraging the ensemble Kalman filter (an effective variant of the Kalman filter which scales well to high-dimensional systems), the present paper achieves at least an order of magnitude improvement (in the near-wall region) over the best results available in the published literature on the estimation of low-Reynolds number turbulent channel flow based on wall information alone.

Type
Papers
Copyright
Copyright © Cambridge University Press 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Anderson, B. D. O. & Moore, J. B. 1979 Optimal Filtering. Prentice-Hall.Google Scholar
Anderson, J. L. 2007 An adaptive covariance inflation error correction algorithm for ensemble filters. Tellus 59 (2), 210224.CrossRefGoogle Scholar
Anderson, J. L. 2009 Spatially and temporally varying adaptive covariance inflation for ensemble filters. Tellus A 61 (1), 7283.CrossRefGoogle Scholar
Anderson, J. L. & Anderson, S. L. 1999 A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts. Monthly Weather Rev. 127 (12), 27412758.2.0.CO;2>CrossRefGoogle Scholar
Arulampalam, S., Maskell, S., Gordon, N. & Clapp, T. 2002 A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans. Signal Process. 50, 174188.CrossRefGoogle Scholar
Bertsekas, D. P. 2001 Dynamic Programming and Optimal Control, 2nd edn. Athena Scientific.Google Scholar
Bewley, T. R. 2001 Flow control: new challenges for a new Renaissance. Prog. Aerosp. Sci. 37 (1), 2158.CrossRefGoogle Scholar
Bewley, T. R. & Liu, S. 1998 Optimal and robust control and estimation of linear paths to transition. J. Fluid Mech. 365, 305349.CrossRefGoogle Scholar
Bewley, T. R., Moin, P. & Temam, R. 2001 DNS-based predictive control of turbulence: an optimal benchmark for feedback algorithms. J. Fluid Mech. 447, 179225.CrossRefGoogle Scholar
Bewley, T. R. & Protas, B. 2004 Skin friction and pressure: the ‘footprints’ of turbulence. Physica D: Nonlinear Phenom. 196 (1–2), 2844.CrossRefGoogle Scholar
Bewley, T. & Sharma, A. 2009 Grid-based Bayesian estimation exploiting sparsity for systems with non-Gaussian uncertainty. In Bulletin of the American Physical Society, vol. 54.Google Scholar
Bouttier, F. & Courtier, P. 1999 Data assimilation concepts and methods. ECMWF Meteorol. Train. Course Lecture Ser. 1–58.Google Scholar
Boyd, S., El Ghaoui, L., Feron, E. & Balakrishnan, V. 1994 Linear Matrix Inequalities in System and Control Theory. Society for Industrial Mathematics.CrossRefGoogle Scholar
Butala, M. D., Yun, J., Chen, Y., Frazin, R. A. & Kamalabadi, F. 2008 Asymptotic convergence of the ensemble Kalman filter. In 15th IEEE International Conference on Image Processing, pp. 825–828.Google Scholar
Cessna, J. B. 2010 The hybrid ensemble smoother (HEnS) & noncartesian computational interconnects. PhD thesis, University of California, San Diego.Google Scholar
Chevalier, M., Hœpffner, J., Bewley, T. R. & Henningson, D. S. 2006 State estimation in wall-bounded flow systems. Part 2. Turbulent flows. J. Fluid Mech. 552, 167187.CrossRefGoogle Scholar
Evensen, G. 1994 Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J. Geophys. Res. 99 (C5), 143162.Google Scholar
Evensen, G. 2003 The ensemble Kalman filter: Theoretical formulation and practical implementation. Ocean Dyn. 53 (4), 343367.CrossRefGoogle Scholar
Evensen, G. 2009 a Data Assimilation: The Ensemble Kalman Filter. Springer Verlag.CrossRefGoogle Scholar
Evensen, G. 2009 b The ensemble Kalman filter for combined state and parameter estimation. IEEE Control Syst. Magazine 29 (3), 83104.CrossRefGoogle Scholar
Furrer, R. & Bengtsson, T. 2007 Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants. J. Multivariate Anal. 98 (2), 227255.CrossRefGoogle Scholar
Gut, A. 2005 Probability: A Graduate Course. Springer Verlag.Google Scholar
Hœpffner, J., Chevalier, M., Bewley, T. R. & Henningson, D. S. 2005 State estimation in wall-bounded flow systems. Part 1. Perturbed laminar flows. J. Fluid Mech. 534, 263294.CrossRefGoogle Scholar
Högberg, M., Bewley, T. R. & Henningson, D. S. 2003 Linear feedback control and estimation of transition in plane channel flow. J. Fluid Mech. 481, 149175.CrossRefGoogle Scholar
Houtekamer, P. L. & Mitchell, H. L. 2001 A sequential ensemble Kalman filter for atmospheric data assimilation. Monthly Weather Rev. 129 (1), 123137.2.0.CO;2>CrossRefGoogle Scholar
Jazwinski, A. H. 1970 Stochastic Processes and Filtering Theory. Academic Press.Google Scholar
Jimenez, J. & Moin, P. 1991 The minimal flow unit in near-wall turbulence. J. Fluid Mech. 225, 213240.CrossRefGoogle Scholar
Kailath, T. 1973 Some new algorithms for recursive estimation in constant linear systems. IEEE Trans. Inf. Theory 19, 750760.CrossRefGoogle Scholar
Kalman, R. E. 1960 A new approach to linear filtering and prediction problems. J. Basic Engng 82 (1), 3545.CrossRefGoogle Scholar
Kalman, R. E. & Bucy, R. S. 1961 New results in linear filtering and prediction theory. J. Basic Engng 83 (3), 95108.CrossRefGoogle Scholar
Kim, J. & Bewley, T. R. 2007 A linear systems approach to flow control. Ann. Rev. Fluid Mech. 39, 383417.CrossRefGoogle Scholar
Kim, J., Moin, P. & Moser, R. 1987 Turbulence statistics in fully developed channel flow at low Reynolds number. J. Fluid Mech. 177 (1), 133166.CrossRefGoogle Scholar
Lorenz, E. N. 1963 Deterministic nonperiodic flow. J. Atmos. Sci. 20, 130141.2.0.CO;2>CrossRefGoogle Scholar
Martinelli, F. 2009 Feedback control of turbulent wall flows. PhD thesis, Politecnico di Milano.Google Scholar
Rauch, H. E., Tung, F. & Striebel, C. T. 1965 Maximum likelihood estimates of linear dynamic systems. AIAA J. 3 (8), 14451450.CrossRefGoogle Scholar
Rogallo, R. S. 1981 Numerical experiments in homogeneous turbulence. NASA Tech Rep. 81315, 191.Google Scholar
Rogallo, R. S. & Moin, P. 1984 Numerical simulation of turbulent flows. Ann. Rev. Fluid Mech. 16 (1), 99137.CrossRefGoogle Scholar
Scherer, C., Gahinet, P. & Chilali, M. 2002 Multiobjective Output-feedback Control via LMI Optimization. IEEE Trans. Autom. Control 42 (7), 896911.CrossRefGoogle Scholar
Slutsky, E. 1925 Über stochastische Asymptoten und Grenzwerte. Metron 5 (3), 390.Google Scholar
Wang, X. & Bishop, C. H. 2003 A comparison of breeding and ensemble transform Kalman filter ensemble forecast schemes. J. Atmos. Sci. 60 (9), 11401158.2.0.CO;2>CrossRefGoogle Scholar
Zhou, K., Doyle, J. C. & Glover, K. 1996 Robust and Optimal Control. Prentice Hall.Google Scholar