2021
DOI: 10.1017/jfm.2021.488
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Linear and nonlinear sensor placement strategies for mean-flow reconstruction via data assimilation

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Cited by 20 publications
(13 citation 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%
“…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%
“…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%
“…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%