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

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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https://hal.inria.fr/hal-02969086
Contributor : Valentin Resseguier Connect in order to contact the contributor
Submitted on : Thursday, October 14, 2021 - 5:06:20 PM
Last modification on : Tuesday, October 19, 2021 - 10:48:10 AM

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  • HAL Id : hal-02969086, version 2
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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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