Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

REAL-TIME FLOW ESTIMATION FROM REDUCED ORDER MODELS AND SPARSE MEASUREMENTS

A Picard 1 M Ladvig 1 Valentin Resseguier 1 D Heitz 2 E Mémin 3 B 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.
Complete list of metadatas

Cited literature [6 references]  Display  Hide  Download

https://hal.inria.fr/hal-02969086
Contributor : Valentin Resseguier <>
Submitted on : Friday, October 16, 2020 - 12:42:30 PM
Last modification on : Sunday, October 18, 2020 - 3:09:55 AM

File

Paper_Picard_et_al_Preprint.pd...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-02969086, version 1

Citation

A Picard, M Ladvig, Valentin Resseguier, D Heitz, E Mémin, et al.. REAL-TIME FLOW ESTIMATION FROM REDUCED ORDER MODELS AND SPARSE MEASUREMENTS. 2020. ⟨hal-02969086⟩

Share

Metrics

Record views

46

Files downloads

36