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

Mons, Vincent; Marquet, Olivier

doi:10.1017/jfm.2021.488

Cited by 16 publications

(11 citation statements)

References 68 publications

(113 reference statements)

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“…The augmented prediction obtained via manipulation of the sources of information can also be actively used to infer an optimized parametric description of the model, with the aim to obtain a predictive tool that can provide accurate predictions without having to rely on observation. DA has been traditionally used in environmental and weather sciences, but applications in fluid mechanics have seen a rapid rise in recent times [22,23,7,24,25,26,27,8,28,29]. A great variety of methods exists, but two groups can be identified [6,30]:…”

Section: Data Assimilation: Ensemble Kalman Filtermentioning

confidence: 99%

Augmented state estimation of urban settings using intrusive sequential Data Assimilation

Villanueva¹,

Valero²,

Glumac³

et al. 2023

Preprint

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A data-driven investigation of the flow around a high-rise building is performed combining heterogeneous experimental samples and RANS CFD. The coupling is performed using techniques based on the Ensemble Kalman Filter (EnKF), including advanced manipulations such as localization and inflation. The augmented state estimation obtained via EnKF has also been employed to improve the predictive features of the model via an optimization of the five free global model constant of the K − ε turbulence model used to close the equations. The optimized values are very far from the classical values prescribed as general recommendations and implemented in codes, but also different from other data-driven analyses reported in the literature. The results obtained with this new optimized parametric description show a global improvement for both the velocity field and the pressure field. In addition, some topological improvement for the flow organization are observed downstream, far from the location of the sensors.

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Section: Data Assimilation: Ensemble Kalman Filtermentioning

confidence: 99%

Augmented state estimation of urban settings using intrusive sequential Data Assimilation

Villanueva¹,

Valero²,

Glumac³

et al. 2023

Preprint

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“…More details about the above derivations may be found in [26,32]. From these expressions, one may employ an iterative gradient-based descent method to solve (6) which may be summarized as follows:…”

Section: Adjoint-based Optimization Proceduresmentioning

confidence: 99%

“…As scattered pointwise/PTV measurements are here considered, regularization has to be considered in the data assimilation procedure in order not to introduce spurious discontinuities linked to the introduction of such pointwise information. A first known strategy to address this aspect may be penalization (see, e.g., [35,32]), namely adding terms in the cost function J to minimize in (6) that would penalize the spatial gradient (or higher-order derivatives) of the control vector f . As an alternative, we here consider H 1 -like regularization of the gradient dJ df in (12) [28,29,30].…”

Section: Gradient Regularizationmentioning

confidence: 99%

Dense velocity, pressure and Eulerian acceleration fields from single-instant scattered velocities through Navier–Stokes-based data assimilation

Mons¹,

Marquet²,

Leclaire³

et al. 2022

Meas. Sci. Technol.

Self Cite

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In this study, a reconstruction procedure to infer full 3D instantaneous velocity and pressure fields from sparse velocity measurements is proposed, here focusing on the case of scattered data as provided by Particle Tracking Velocimetry (PTV). A key characteristic of the present approach is that it only relies on single-instant velocity measurements, and does not require any time-resolved or acceleration information. It is based on a strong enforcement of the Navier-Stokes equations where the partial time derivative of the velocity field, namely Eulerian acceleration, is considered as a control vector to minimize the discrepancies between the single-instant measurements and the reconstructed flow. Eulerian acceleration is thus a byproduct of the present methodology in addition to the identification of the full velocity and pressure fields. The reconstruction performances of the proposed Navier-Stokes-based data-assimilation approach for single-instant velocity measurements (NS-DA-SIM) are assessed using a numerical dataset for the 3D flow past a cylinder. Comparisons with existing data assimilation methodologies allow to further illustrate the merits of the present approach. The latter is finally applied to the instantaneous reconstruction of an experimental air jet flow from volumetric PTV data, confirming its robustness and high efficacy.

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“…Data assimilation methods in fluid dynamics leverage available measurement data, in combination with a partially or fully known model, to extract more information about the flows from which the data originated (Foures et al, 2014;Symon et al, 2017;Fukami et al, 2019;Franceschini et al, 2020;Mons and Marquet, 2021). The growth of machine learning techniques over the last decade has brought numerous new methods for analysis and assimilation of flow data (Brunton et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Mean flow reconstruction of unsteady flows using physics-informed neural networks

Sliwinski

Rigas

2023

DCE

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Data assimilation of flow measurements is an essential tool for extracting information in fluid dynamics problems. Recent works have shown that the physics-informed neural networks (PINNs) enable the reconstruction of unsteady fluid flows, governed by the Navier–Stokes equations, if the network is given enough flow measurements that are appropriately distributed in time and space. In many practical applications, however, experimental measurements involve only time-averaged quantities or their higher order statistics which are governed by the under-determined Reynolds-averaged Navier–Stokes (RANS) equations. In this study, we perform PINN-based reconstruction of time-averaged quantities of an unsteady flow from sparse velocity data. The applied technique leverages the time-averaged velocity data to infer unknown closure quantities (curl of unsteady RANS forcing), as well as to interpolate the fields from sparse measurements. Furthermore, the method’s capabilities are extended further to the assimilation of Reynolds stresses where PINNs successfully interpolate the data to complete the velocity as well as the stresses fields and gain insight into the pressure field of the investigated flow.

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

Abstract: Abstract

Cited by 16 publications

References 68 publications

Augmented state estimation of urban settings using intrusive sequential Data Assimilation

Augmented state estimation of urban settings using intrusive sequential Data Assimilation

Dense velocity, pressure and Eulerian acceleration fields from single-instant scattered velocities through Navier–Stokes-based data assimilation

Mean flow reconstruction of unsteady flows using physics-informed neural networks

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