Data assimilation with an extended Kalman filter for impact-produced shock-wave dynamics

Journal of Computational Physics

Volumn 196, Issue 2, 2004, Pages 705-723

  Kao, Jim ^a   Flicker, Dawn ^a   Henninger, Rudy ^a   Frey, Sarah ^b   Ghil, Michael ^c   Ide, Kayo ^c  

^a LOS ALAMOS NATIONAL LABORATORY   (United States)

^b UNIVERSITY OF ARIZONA   (United States)

^c UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BANDPASS FILTERS; BLENDING; COVARIANCE MATRIX; EXTENDED KALMAN FILTERS;

DATA ASSIMILATION; ERROR COVARIANCE MATRIX; FILTER METHOD; HIGH SPEED IMPACT; NONLINEAR STATE; PHYSICAL SYSTEMS; SHOCK-WAVE DYNAMICS; STATE ESTIMATES; STATE EVOLUTIONS; STATE-VARIABLES;

SHOCK WAVES;

EID: 2442542445     PISSN: 00219991     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jcp.2003.11.028     Document Type: Article

References (33)

1. F.L. Addessio, J.N. Johnson, Rate-dependent ductile failure model, J. Appl. Phys. 74 (1993) 1640-1648.
2. J.L. Anerson, An ensemble adjustment filter for data assimilation, Mon. Weather Rev. 129 (2001) 2884-2903.
3. L.M. Baker, R.E. Hollenbach, Laser interferometer for measuring high velocities of any reflecting surface, J. Appl. Phys. 43 (11) (1972) 4669-4675.
4. K.M. Baker, M. Shahinpoor, L.C. Chhabildas, in: JR. Asay, M. Shahinpoor (Eds.), High-pressure Shock Compression of Solids, Springer, Berlin, 1993, p. 43.
5. L. Bengtsson, M. Ghil, E. Källén (Eds.), Dynamic Meteorology: Data Assimilation Methods, Springer, New York, 1981, p. 330.
6. C. Bischof, A. Carle, P. Khademi, A. Mauer, The ADFOR 2.0 system for the automatic differentiation of Fortran 77 programs, Argonne National Laboratory Report, ANL-MCS-P481-1194, 1995.
7. D.J. Cagliostro, D.A. Mandell, L.A. Schwalbe, T.F. Adams, E.J. Chapyak, MESA 3D calculations of armor penetration by projectiles with combined obliquity and yaw, Int. J. Impact Eng. 10 (1990).
8. B. DeVolder, J. Glimm, J.W. Grove, Y. Kang, K. Pao, D.H. Sharp, A statistical study of errors in simulations of shock wave interaction, Los Alamos Unclassified Release, LA-UR-01-3671, 2002.
9. G. Evensen, Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res. 99 (1994) 1043-1062.
10. A. Gelb (Ed.), Applied Optimal Estimation, MIT Press, Cambridge, MA, 1974, 347pp.
11. M. Ghil, P. Malanotte-Rizzoli, Data assimilation in meteorology and oceanography, Adv. Geophys. 33 (1991) 141-226.
12. M. Ghil, Advances in sequential estimation for atmospheric and oceanic flows, J. Meteorol. Soc. Jpn. 75 (1997) 289-304.
13. F.A. Grunbaum, Limited angle reconstruction problems in X-Ray and NMR tomography P. Soc., Photo-Opt. Inst. 372 (1982) 185-187.
14. F.A. Grunbaum, A nonlinear inverse problem inspired by three-dimensional diffuse tomography, Inverse Probl. 17 (2001) 1907-1922.
15. R.J. Henninger, P.J. Maudlin, M.L. Rightley, Accuracy of differential sensitivity for one-dimensional shock problems. APS Shock Compression of Condensed Matter Conference, Amherst, MA. (1997).
16. G.R. Hills, T.G. Trucano, Statistical validation of engineering and scientific models with application to CTH. Sandia National Laboratory Rep., SAND2001-0312 (2001) 118pp.
17. P.L. Houtekamer, H.L. Mitchell, Data assimilation using an ensemble Kalman filter techniques, Mon. Weather Rev. 126 (1998) 796-811.
18. K. Ide, M. Ghil, Extended Kalman filtering for vortex systems. Part I: Methodology and point vortices, Dyn. Atmos. Oceans 27 (1997) 301-332.
19. K. Ide, M. Ghil, Extended Kalman filtering for vortex systems. Part II: Rankine vortices and observing-system design, Dyn. Atmos. Oceans 27 (1997) 333-350.
20. K. Ide, P. Courtier, M. Ghil, A. Lorenc, Unified notation for data assimilation: operational, sequential and variational, J. Meteorol. Soc. Jpn. 75 (1997) 181-189.
21. A.H. Jazwinski, Stochastic Processes and Filtering Theory, Academic Press, New York, 1970, 376 pp.
22. R.E. Kalman, A new approach to linear filtering and prediction problems, Trans. ASME, Ser. D 82 (1960) 35-45.
23. R.E. Kalman, R.S. Bucy, New results in linear filtering and prediction problems, Trans. ASME, Ser. D 83 (1961) 95-108.
24. T. Kwan, A. Mathews, P. Christenson, C. Snell, Integrated system simulation in X-ray radiography, Comput. Phys. Commun. 142 (2001) 263-269.
25. P.M. Lyster, S.E. Cohn, R. Menard, L.-P. Chang, S.-J. Lin, R. Olsen, Parallel implementation of a Kalman filter for constituent data assimilation, Mon. Weather Rev. 125 (1997) 1674-1686.
26. R. Miller, M. Ghil, F. Gauthiez, Advanced data assimilation in strongly nonlinear dynamical systems, J. Atmos. Sci. 51 (1994) 1037-1056.
27. I.M. Navon, Practical and theoretical aspects of adjoint parameter estimation and identifiability in meteorology and oceanography, Dynam. Atmos. Oceans 27 (1998) 55-79.
28. O.M. Smedstad, J. O'brien, Variational data assimilation and parameter estimation in an equatorial Pacific ocean model, Prog. Oceanogr. 26 (1991) 179-241.
29. C. Sun, Z. Hao, M. Ghil, J.D. Neelin, Data assimilation for a coupled ocean-atmosphere model. Part I: Sequential state estimation, Mon. Weather Rev. 130 (2002) 1073-1099.
30. M.K. Tippett, J.L. Anderson, C.H. Bishop, T.M. Hamill, J.S. Whitaker, Ensemble square root filters, Mon. Weather Rev. 131 (2003) 1485-1490.
31. B. Van Leer, Towards the ultimate conservation difference scheme. V. A second-order sequel to Godunov's method, J. Comput. Phys. 32 (1979) 101-135.
32. D.L. Youngs, An interfacing tracking method for a 3D Eulerian hydrodynamics code, UK Atomic Weapons Establishment, AWRE/44/92/35, 1987.
33. J.A. Zukas, High Velocity Impact Dynamics, Wiley, New York, 1990, 935pp.