Treffer: Data assimilation for magnetohydrodynamics systems

Title:
Data assimilation for magnetohydrodynamics systems
Authors:
MENDOZA, O. Barrero, DE MOOR, B, BERNSTEIN, D. S
Source:
Proceedings of the 11th International Congress on Computational and Applied Mathematics (ICCAM-2004)Journal of computational and applied mathematics. 189(1-2):242-259
Publisher Information:
Amsterdam: Elsevier, 2006.
Publication Year:
2006
Physical Description:
print, 11 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Mathematics, Mathématiques, Sciences exactes et technologie, Exact sciences and technology, Sciences et techniques communes, Sciences and techniques of general use, Mathematiques, Mathematics, Probabilités et statistiques, Probability and statistics, Statistiques, Statistics, Inférence à partir de processus stochastiques; analyse des séries temporelles, Inference from stochastic processes; time series analysis, Analyse numérique. Calcul scientifique, Numerical analysis. Scientific computation, Analyse numérique, Numerical analysis, Probabilités et statistiques numériques, Numerical methods in probability and statistics, Physique, Physics, Domaines classiques de la physique (y compris les applications), Fundamental areas of phenomenology (including applications), Mécanique des fluides, Fluid dynamics, Magnétohydrodynamique et électrohydrodynamique, Magnetohydrodynamics and electrohydrodynamics, Analyse numérique, Numerical analysis, Análisis numérico, Assimilation donnée, Data assimilation, Asimilación dato, Calcul 2 dimensions, Two-dimensional calculations, Estimation erreur, Error estimation, Estimación error, FORTRAN, Filtre Kalman, Kalman filter, Filtro Kalman, Logiciel, Software, Logicial, Magnétohydrodynamique, Magnetohydrodynamics, Magnetohidrodinámica, Mathématiques appliquées, Applied mathematics, Matemáticas aplicadas, Méthode Monte Carlo, Monte Carlo method, Método Monte Carlo, Méthode stochastique, Stochastic method, Método estocástico, Progiciel, Software package, Paquete programa, Prévision météorologique, Weather forecast, Previsión meteorológica, Système grande taille, Large scale system, Sistema gran escala, Système non linéaire, Non linear system, Sistema no lineal, 52.30.Cv, 52.65.Kj, 52.65.Pp Data assimilation, Ensemble Kalman filter, Large scale systems, Magnetohydrodynamics systems, Space weather forecast
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Electrical Engineering, ESAT/SCD-SISTA. Katholieke Universiteit Leuven, 3001 Leuven, Belgium
Department of Aerospace Engineering, The University of Michigan, Ann Arbor, MI 48109-2140, United States
ISSN:
0377-0427
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=17583366
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Mathematics

Physics: fluid mechanics
Accession Number:
edscal.17583366
Database:
PASCAL Archive

Weitere Informationen

Prediction of solar storms has become a very important issue due to the fact that they can affect dramatically the telecommunication and electrical power systems at the earth. As a result, a lot of research is being done in this direction, space weather forecast. Magnetohydrodynamics systems are being studied in order to analyse the space plasma dynamics, and techniques which have been broadly used in the prediction of earth environmental variables like the Kalman filter (KF), the ensemble Kalman filter (EnKF), the extended Kalman filter (EKF), etc., are being studied and adapted to this new framework. The assimilation of a wide range of space environment data into first-principles-based global numerical models will improve our understanding of the physics of the geospace environment and the forecasting of its behaviour. Therefore, the aim of this paper is to study the performance of nonlinear observers in magnetohydrodynamics systems, namely, the EnKF. The EnKF is based on a Monte Carlo simulation approach for propagation of process and measurement errors. In this paper, the EnKF for a nonlinear two-dimensional magnetohydrodynamic (2D-MHD) system is considered. For its implementation, two software packages are merged, namely, the Versatile Advection Code (VAC) written in Fortran and Matlab of Mathworks. The 2D-MHD is simulated with the VAC code while the EnKF is computed in Matlab. In order to study the performance of the EnKF in MHD systems, different number of measurement points as well as ensemble members are set.