Evidence map›Paper›PMID 32674278›Full record

ReviewSensors (Basel, Switzerland)2020

A Review of the Real-Time Monitoring of Fluid-Properties in Tubular Architectures for Industrial Applications.

Maha A Nour, Muhammad M Hussain

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
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

2 authors.

Maha A Nourmmh Labs, Electrical Engineering, Computer Electrical Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.
Muhammad M Hussainmmh Labs, Electrical Engineering, Computer Electrical Mathematical Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia.

Funding

King Abdullah University of Science and Technology BAS/1/1619-01-01Saudi Aramco N/A
6 · The paper itself

Abstract

The real-time monitoring of fluid properties in tubular systems, such as viscosity and flow rate, is essential for industries utilizing liquid mediums. Nowadays, most studies of the fluid characteristics are performed off-line using laboratory facilities that can provide accurate results, yet they do not match the demanded industrial pace. Off-line measurements are ineffective and time-consuming. The available real-time monitoring sensors for fluid properties are generally destructive methods that produce significant and persistent damage to the tubular systems during the installation process. Others use huge and bulky invasive instrument methods that generate considerable pressure reduction and energy loss in tubular systems. For these drawbacks, industries centered their attention on non-invasive and non-destructive testing (NDT) methodologies, which are installed on the outer tubular surface to avoid flow disturbance and desist shutting down systems for installations. Although these sensors showed excellent achievement for monitoring and inspecting pipe health conditions, the performance was not convincing for monitoring the properties of fluids. This review paper presents an overview of the real-time monitoring of fluid properties in tubular systems for industrial applications, particularly for pipe monitoring sensors, viscosity, and flow measurements. Additionally, the different available sensing mechanisms and their advantages, drawbacks, and potentials are discussed.

Indexed as

flowmeterfluid sensorspipestubesviscosity monitor

Identifiers

PMID32674278
PMCPMC7412341

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.