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