Unraveling Flow Patterns through Nonlinear Manifold Learning

Tauro, Flavia; Grimaldi, Salvatore; Porfiri, Maurizio

doi:10.1371/journal.pone.0091131

Cited by 22 publications

(15 citation statements)

References 54 publications

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“…Nonlinear manifold learning techniques, such as the isometric feature mapping (Isomap) method, have been shown to be useful in estimating Reynolds number from raw video footage of cylinder wakes. 27 Similar success has been demonstrated through a combined use of POD, compressive sensing, and supervised learning to determine the Reynolds number associated with numerically simulated cylinder flows from a limited set of surface pressure signals. 28,29 Related techniques have been successful in the context of separated flows for discriminating between actuated and unactuated flows from image data.…”

Section: Introductionmentioning

confidence: 73%

Learning Wake Regimes from Snapshot Data

Hemati¹

2016

46th AIAA Fluid Dynamics Conference

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Fluid wakes are often categorized by visual inspection according to the number and grouping of vortices shed per cycle (e.g., 2S, 2P, P+S). While such categorizations have proven useful for describing and comparing wakes, the criterion excludes features that are essential to a wake's evolution (i.e., the relative positions and strengths of the shed vortices). For example, not all 2P wakes exhibit the same dynamics; thus, the evolution of wake patterns among 2P wakes can be markedly distinct. Here, we explore the notion of labeling wakes according their dynamics based on empirical snapshot data. Snapshots of the velocity field are reduced to a representative feature vector, which is then processed using machine learning techniques tailored to the task of wake regime learning. The wake regime learning framework is evaluated on an idealized 2P wake model, which can be configured to simulate different (known) dynamical regimes. A simple version of the wake regime learning framework successfully discriminates between dynamically distinct 2P wakes. The results presented here suggest that the wake regime learning perspective may facilitate the development of a new dynamics-based wake labeling convention in the future.

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Section: Introductionmentioning

confidence: 73%

Learning Wake Regimes from Snapshot Data

Hemati¹

2016

46th AIAA Fluid Dynamics Conference

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“…The dependence of LSPIV measurements on the presence of homogeneously distributed tracers may pose a significant technical challenge to practical implementations of the proposed approach. To overcome this issue, we envision the development of machine learning techniques to automatically de-noise pictures [44] and to emphasize floaters' contrast against the image background. Alternative image-based flow velocimetry algorithms that yield the trajectory of individual floaters, such as particle tracking velocimetry, should be explored [45].…”

Section: Airborne Flow Velocimetrymentioning

confidence: 99%

Surface flow measurements from drones

Tauro

Porfiri

Grimaldi

2016

Journal of Hydrology

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112

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Drones are transforming the way we sense and interact with the environment. However, despite their increased capabilities, the use of drones in geophysical sciences usually focuses on image acquisition for generating high-resolution maps. Motivated by the increasing demand for innovative and high performance geophysical observational methodologies, we posit the integration of drone technology and optical sensing toward a quantitative characterization of surface flow phenomena. We demonstrate that a recreational drone can be used to yield accurate surface flow maps of sub-meter water bodies. Specifically, drone's vibrations do not hinder surface flow observations, and velocity measurements are in agreement with traditional techniques. This first instance of quantitative water flow sensing from a flying drone paves the way to novel observations of the environment.

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“…In the future, we plan on testing video data recorded at the gauge-cam station through an array of image-based algorithms, spanning from LSPIV to particle tracking velocimetry (Tang et al, 2008), long-term tracking (Pervez and Solomon, 1994), and optical flow (Quénot et al, 1998). In addition, classification of such a large database will leverage the application of unsupervised machine learning procedures (Tauro et al, 2014a).…”

Section: Research Objectivesmentioning

confidence: 99%

A novel permanent gauge-cam station for surface-flow observations on the Tiber River

Tauro

Petroselli

Porfiri

et al. 2016

Geosci. Instrum. Method. Data Syst.

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Abstract. Flow monitoring of riverine environments is crucial for hydrology and hydraulic engineering practice. Besides few experimental implementations, flow gauging relies on local water level and surface-flow velocity measurements through ultrasonic meters and radars. In this paper, we describe a novel permanent gauge-cam station for large-scale and continuous observation of surface flows, based on remote acquisition and calibration of video data. Located on the Tiber River, in the center of Rome, Italy, the station captures 1 min videos every 10 min over an area oriented along the river cross section of up to 20.6 × 15.5 m 2 . In a feasibility study, we demonstrate that accurate surface-flow velocity estimations can be obtained by analyzing experimental images via particle tracking velocimetry (PTV). In medium illumination conditions (70-75 lux), PTV leads to velocity estimations in close agreement with radar records and is less affected by uneven lighting than large-scale particle image velocimetry. Future efforts will be devoted to the development of a comprehensive test bed infrastructure for investigating the potential of multiple optics-based approaches for surface hydrology.

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Unraveling Flow Patterns through Nonlinear Manifold Learning

Cited by 22 publications

References 54 publications

Learning Wake Regimes from Snapshot Data

Learning Wake Regimes from Snapshot Data

Surface flow measurements from drones

A novel permanent gauge-cam station for surface-flow observations on the Tiber River

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