Flow state estimation in the presence of discretization errors

Silva, Andre F. C. da; Colonius, Tim

doi:10.1017/jfm.2020.103

Cited by 10 publications

(9 citation statements)

References 46 publications

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“…In parallel with the continuation of more classical approaches to address the closure problem in the RANS equations (Durbin 2018), the latter problem is currently revisited through the consideration of alternative strategies which may be interlinked, as will be detailed in the following: uncertainty quantification (Xiao & Cinnella 2019), data assimilation (Lewis, Lakshmivarahan & Dhall 2006) and data-driven modelling (Duraisamy, Iaccarino & Xiao 2019). In particular, data assimilation aims to merge experimental and numerical approaches in order to overcome their inherent limitations, namely the difficulty in accessing the whole state of the flow in experiments (Heitz, Mémin & Schnörr 2010; Suzuki 2012; Gillissen, Bouffanais & Yue 2019) and the lack of knowledge of the inputs and models in numerical simulations (Hayase 2015; Meldi & Poux 2017; Chandramouli, Mémin & Heitz 2020; Da Silva & Colonius 2020; Li et al. 2020).…”

Section: Introductionmentioning

confidence: 99%

Linear and nonlinear sensor placement strategies for mean-flow reconstruction via data assimilation

Mons

Marquet

2021

J. Fluid Mech.

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Section: Introductionmentioning

confidence: 99%

Linear and nonlinear sensor placement strategies for mean-flow reconstruction via data assimilation

Mons

Marquet

2021

J. Fluid Mech.

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“…In [21,24], authors use neural network based methods to estimate the leading edge suction parameter(LESP). The model studied in this work has a Reynolds number of 17,500, substantially higher than other computational works which focus on Reynolds number in the range of O(10 2 ) − O( 103 ) [24,8,6,29]. This is closer to the lower end of the range considered by experimental work [7,15].…”

Section: Introductionmentioning

confidence: 82%

“…In particular, recent works have explored the use of machine learning to estimate flow field and aerodynamic data from sensors on the surface of the airfoil. These include methods for flow reconstruction from limited sensors using neural networks [29,15], filtering based flow estimation [8,6], as well as prediction of aerodynamic coefficients [7]. Deep learning has also been used for estimating properties of the flow used in low order vortex models.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Intermittent Fluctuations from Surface Pressure Measurements on a Turbulent Airfoil

Rudy¹,

Sapsis²

2021

Preprint

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This work studies the effectiveness of several machine learning techniques for predicting extreme events occurring in the flow around an airfoil at low Reynolds. For certain Reynolds numbers the aerodynamic forces exhibit intermittent fluctuations caused by changes in the behavior of vortices in the airfoil wake. Such events are prototypical of the unsteady behavior observed in airfoils at low Reynolds and their prediction is extremely challenging due to their intermittency and the chaotic nature of the flow. We seek to forecast these fluctuations in advance of their occurrence by a specified length of time. We assume knowledge only of the pressure at a discrete set of points on the surface of the airfoil, as well as offline knowledge of the state of the flow. Methods include direct prediction from historical pressure measurements, flow reconstruction followed by forward integration using a full order solver, and data-driven dynamic models in various low dimensional quantities. Methods are compared using several criteria tailored for extreme event prediction. We show that methods using data-driven models of low order dynamic variables outperform those without dynamic models and that unlike previous works, low dimensional initializations do not accurately predict observables with extreme events such as drag.

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“…The dynamical operator in a flow estimation framework can take various forms, including high-fidelity Navier-Stokes simulation. Indeed, this has been the basis for recent work by da Silva and Colonius [13,14]. In [13], they advanced the state vector for flow past an airfoil with an operator derived from an immersed boundary projection method [15].…”

Section: Introductionmentioning

confidence: 99%

“…Since an ostensible goal for flow estimation is real-time control, dynamical models obtained from CFD will generally not be fast enough to update the state. In their subsequent work in [14], da Silva and Colonius utilized a dynamical model for the airfoil flow consisting of cheaper coarse-grid simulations. Their state vector was then augmented with a bias error, which they successfully estimated along with the coarsened grid state from surface observations of the higher-fidelity truth data.…”

Section: Introductionmentioning

confidence: 99%

Ensemble-filtered vortex modeling of strongly disturbed aerodynamic flows

Provost,

Eldredge

2020

Preprint

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The task of dynamic flow estimation is to construct an instantaneous approximation of an evolving flow-and particularly, its response to disturbances-using measurements from available sensors. Building from previous work by Darakananda et al. (Phys Rev Fluids 2018), we further develop an ensemble Kalman filter (EnKF) framework for aerodynamic flows based on an ensemble of randomlyperturbed inviscid vortex models of flow about an infinitely-thin plate. In the forecast step, vortex elements in each ensemble member are advected by the flow and new elements are released from each edge of the plate; the elements are modestly aggregated to maintain an efficient representation. The vortex elements and leading edge constraint are corrected in the analysis step by assimilating the surface pressure differences across the plate measured from the truth system. We show that the overall framework can be physically interpreted as a series of adjustments to the position and shape of an elliptical region of uncertainty associated with each vortex element. In this work, we compare the previously-used stochastic EnKF with the ensemble transform Kalman filter (ETKF), which uses a deterministic analysis step. We examine the response of the flat plate at 20 • in two perturbed flows, with truth data obtained from high-fidelity Navier-Stokes simulation at Reynolds number 500. In the first case, we apply a sequence of large-amplitude pulses near the leading edge of the plate to mimic flow actuation. In the second, we place the plate in a vortex street wake behind a cylinder. In both cases, we show that the vortex-based framework accurately estimates the pressure distribution and normal force, with no a priori knowledge of the perturbations or their structure. We show that, in each case, the ETKF is consistently more robust than the stochastic EnKF and is qualitatively better at representing the coherent structures of the true flow. Finally, we examine the mapping from measurements to state update in the analysis step through singular value decomposition of the Kalman gain.

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Flow state estimation in the presence of discretization errors

Cited by 10 publications

References 46 publications

Linear and nonlinear sensor placement strategies for mean-flow reconstruction via data assimilation

Linear and nonlinear sensor placement strategies for mean-flow reconstruction via data assimilation

Prediction of Intermittent Fluctuations from Surface Pressure Measurements on a Turbulent Airfoil

Ensemble-filtered vortex modeling of strongly disturbed aerodynamic flows

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