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Article Dans Une Revue ESAIM: Proceedings and Surveys Année : 2018

Front shape similarity measure for shape-oriented sensitivity analysis and data assimilation for Eikonal equation

Résumé

We present a shape-oriented data assimilation strategy suitable for front-tracking problems through the example of wildfire. The concept of " front " is used to model, at regional scales, the burning area delimitation that moves, undergoes shape and topological changes under heterogeneous orography, biomass fuel and micrometeorology. The simulation-observation discrepancies are represented using a front shape similarity measure deriving from image processing and based on the Chan-Vese contour fitting functional. We show that consistent corrections of the front location and uncertain physical parameters can be obtained using this measure applied on a level-set fire growth model solving for an eikonal equation. This study involves a Luenberger observer for state estimation, including a topological gradient term to track multiple fronts, and of a reduced-order Kalman filter for joint parameter estimation. We also highlight the need – prior to parameter estimation – for sensitivity analysis based on the same discrepancy measure, and for instance using polynomial chaos metamodels, to ensure a meaningful inverse solution is achieved. The performance of the shape-oriented data assimilation strategy is assessed on a synthetic configuration subject to uncertainties in front initial position, near-surface wind magnitude and direction. The use of a robust front shape similarity measure paves the way toward the direct assimilation of infrared images and is a valuable asset in the perspective of data-driven wildfire modeling.
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Dates et versions

hal-01625575 , version 1 (27-10-2017)

Identifiants

Citer

Mélanie Rochoux, Annabelle Collin, Cong Zhang, Arnaud Trouvé, Didier Lucor, et al.. Front shape similarity measure for shape-oriented sensitivity analysis and data assimilation for Eikonal equation. ESAIM: Proceedings and Surveys, 2018, pp.258 - 279. ⟨10.1051/proc/201863258⟩. ⟨hal-01625575⟩
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