Real-time estimation and prediction of unsteady flows using reduced-order models coupled with few measurements

Abstract

The estimation and prediction of unsteady flows in real time offers significant advantages for the monitoring and active control of complex hydrodynamic and aerodynamic systems, such as wind turbine blades, hydrofoils and aircraft wings. A new data assimilation algorithm is proposed for the estimation and prediction of unsteady flows, coupling in real time onboard measurements and fluid dynamics simulations at minimal computational expense. The procedure combines a Proper Orthogonal Decomposition Galerkin method, a model under location uncertainty stochastic closure, and a particle filtering scheme. The algorithm is validated using case studies of two- and three-dimensional wake flows at low and moderate Reynolds numbers respectively. Following an initial learning window to train the algorithm, and using only a single measurement point, our method is shown to perform well against conventional reduced data assimilation algorithms for up to 14 vortex shedding cycles.