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Real-time flow estimation from reduced order models and sparse measurements

Agustin Martin Picard 1 Matheus Ladvig 1 Valentin Resseguier 1 Dominique Heitz 2 Etienne Mémin 3 Bertrand Chapron 4 
3 FLUMINANCE - Fluid Flow Analysis, Description and Control from Image Sequences
IRMAR - Institut de Recherche Mathématique de Rennes, Inria Rennes – Bretagne Atlantique , INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement
Abstract : To successfully monitor and actively control hydrody-namic and aerodynamic systems (e.g. aircraft wings), it can be critical to estimate and predict the unsteady flow around them in real-time. Thus, we introduce a new algorithm to couple on-board measurements with fluid dynamics simulations and prior data in real-time without the need to rely on large computational infrastructure. This is achieved through a combination of a Proper Orthogonal Decomposition Galerkin method, stochastic closure-model under location uncertainty-and a particle filtering scheme. Impressive numerical results have been obtained for a 3-dimensional wake flows at moderate Reynolds for up to 14 vortex shedding cycles after the learning window , using a single measurement point.
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Submitted on : Thursday, October 14, 2021 - 5:06:20 PM
Last modification on : Tuesday, September 13, 2022 - 2:14:33 PM


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  • HAL Id : hal-02969086, version 2


Agustin Martin Picard, Matheus Ladvig, Valentin Resseguier, Dominique Heitz, Etienne Mémin, et al.. Real-time flow estimation from reduced order models and sparse measurements. AERO 2020+1, Apr 2021, Visioconference, France. ⟨hal-02969086v2⟩



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