Evidence map›Paper›PMID 39211418›Full record

ArticleFrontiers in robotics and AI2024

Concepts for drone based pipeline leak detection.

Lutz Bretschneider, Sven Bollmann, Deborah Houssin-Agbomson, Jacob Shaw, Neil Howes, Linh Nguyen, Rod Robinson, Jon Helmore, Michael Lichtenstern, Javis Nwaboh and 3 more

Abstract read
In one paragraph

Article in Frontiers in robotics and AI, 2024. 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

13 authors.

Lutz BretschneiderTU Braunschweig, Institute of Flight Guidance, Braunschweig, Germany.
Sven BollmannTU Braunschweig, Institute of Flight Guidance, Braunschweig, Germany.
Deborah Houssin-AgbomsonAir Liquide, Campus Innovation Paris, Paris, France.
Jacob ShawNational Physical Laboratory (NPL), Teddington, United Kingdom.
Neil HowesNational Physical Laboratory (NPL), Teddington, United Kingdom.
Linh NguyenNational Physical Laboratory (NPL), Teddington, United Kingdom.
Rod RobinsonNational Physical Laboratory (NPL), Teddington, United Kingdom.
Jon HelmoreNational Physical Laboratory (NPL), Teddington, United Kingdom.
Michael LichtensternInstitut für Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt, Oberpfaffenhofen, Germany.
Javis NwabohPhysikalisch-Technische Bundesanstalt (PTB), Braunschweig, Germany.
Andrea PoganyPhysikalisch-Technische Bundesanstalt (PTB), Braunschweig, Germany.
Volker EbertPhysikalisch-Technische Bundesanstalt (PTB), Braunschweig, Germany.
Astrid LampertTU Braunschweig, Institute of Flight Guidance, Braunschweig, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The quickly developing drone technology can be used efficiently in the field of pipeline leak detection. The aim of this article is to provide drone mission concepts for detecting releases from pipelines. It provides an overview of the current applications of natural gas pipeline surveys, it considers environmental conditions by plume modelling, it discusses suitable commercially available sensors, and develops concepts for routine monitoring of pipelines and short term missions for localising and identifying a known leakage. Suitable platforms depend on the particular mission and requirements concerning sensors and legislation. As an illustration, a feasibility study during a release experiment is introduced. The main challenge of this study was the variability of wind direction on a time scale of minutes, which produces considerable differences to the plume simulations. Nevertheless, the leakage rates derived from the observations are in the same order of magnitude as the emission rates. Finally the results from the modeling, the release experiment and possible drone scenarios are combined and requirements for future application derived.

Indexed as

droneleak detectionmethanepipelineplume modelingrelease experimentSDG13

Identifiers

PMID39211418
PMCPMC11357903

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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.