Evidence map›Paper›PMID 35161703›Full record

ArticleSensors (Basel, Switzerland)2022

About 3D Incompressible Flow Reconstruction from 2D Flow Field Measurements.

Laura Fabbiano, Paolo Oresta, Aimé Lay-Ekuakille, Gaetano Vacca

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Laura FabbianoDepartment of Mechanics, Mathematics and Management, Polytechnic University of Bari, Via E. Orabona, 4, 70125 Bari, Italy.
Paolo OrestaDepartment of Mechanics, Mathematics and Management, Polytechnic University of Bari, Via E. Orabona, 4, 70125 Bari, Italy.ORCID 0000-0002-1383-8449
Aimé Lay-EkuakilleDepartment of Innovation Engineering, University of Salento, Via per, Monteroni, 73100 Lecce, Italy.ORCID 0000-0002-1762-419X
Gaetano VaccaDepartment of Mechanics, Mathematics and Management, Polytechnic University of Bari, Via E. Orabona, 4, 70125 Bari, Italy.ORCID 0000-0002-9125-9936

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, an assessment of the uncertainty affecting a hybrid procedure (experimental/numerical) is carried out to validate it for industrial applications, at the least. The procedure in question serves to depict 3D incompressible flow fields by using 2D measurements of it and computing the third velocity component by means of the continuity equation. A quasi-3D test case of an incompressible flow has been inspected in the wake of a NACA 0012 airfoil immersed in a forced flow of water running in a rectangular open channel. Specifically, starting from a 2D measurement data in planes orthogonal to the stream-wise direction, the computational approach can predict the third flow velocity component. A 3D ADV instrument has been utilized to measure the flow field, but only two velocity components have been considered as measured quantities, while the third one has been considered as reference with which to compare the computed component from the continuity equation to check the accuracy and validity of the hybrid procedure. At this aim, the uncertainties of the quantities have been evaluated, according to the GUM, to assess the agreement between experiments and predictions, in addition to other metrics. This aspect of uncertainty is not a technical sophistication but a substantial way to bring to the use of a 1D and 2D measurement system in lieu of a 3D one, which is costly in terms of maintenance, calibration, and economic issues. Moreover, the magnitude of the most relevant flow indicators by means of experimental data and predictions have been estimated and compared, for further confirmation by means of a supervised learning classification. Further, the sensed data have been processed, by means of a machine learning algorithm, to express them in a 3D way along with accuracy and epoch metrics. Two additional metrics have been included in the effort to show paramount interest, which are a geostatistical estimator and Sobol sensitivity. The statements of this paper can be used to design and test several devices for industrial purposes more easily.

Indexed as

3D flow velocity reconstructionfluid dynamics measurementshybrid procedureKrigingmachine learningsensor signal processingSobol sensitivityuncertainty evaluation

Identifiers

PMID35161703
PMCPMC8840076

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.