Evidence map›Paper›PMID 32182939›Full record

ArticleSensors (Basel, Switzerland)2020

Flow Field Perception of a Moving Carrier Based on an Artificial Lateral Line System.

Guijie Liu, Huanhuan Hao, Tingting Yang, Shuikuan Liu, Mengmeng Wang, Atilla Incecik, Zhixiong Li

Abstract read
In one paragraph

Article 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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Guijie LiuDepartment of Mechanical and Electrical Engineering & Key Laboratory of Ocean Engineering of Shang Dong Province, Ocean University of China, Qingdao 266100, China.ORCID 0000-0002-3353-117X
Huanhuan HaoDepartment of Mechanical and Electrical Engineering & Key Laboratory of Ocean Engineering of Shang Dong Province, Ocean University of China, Qingdao 266100, China.
Tingting YangDepartment of Mechanical and Electrical Engineering & Key Laboratory of Ocean Engineering of Shang Dong Province, Ocean University of China, Qingdao 266100, China.
Shuikuan LiuDepartment of Mechanical and Electrical Engineering & Key Laboratory of Ocean Engineering of Shang Dong Province, Ocean University of China, Qingdao 266100, China.
Mengmeng WangDepartment of Mechanical and Electrical Engineering & Key Laboratory of Ocean Engineering of Shang Dong Province, Ocean University of China, Qingdao 266100, China.ORCID 0000-0003-4629-633X
Atilla IncecikDepartment of Naval Architecture, Ocean and Marine Engineering, University of Strathclyde, Glasgow G1 1XQ, UK.
Zhixiong LiSchool of Mechanical, Materials, Mechatronic and Biomedical Engineering, University of Wollongong, Wollongong, NSW 2522, Australia.

Funding

ARC DECRA DE190100931National Aerospace Science Foundation of China 61540010
6 · The paper itself

Abstract

At present, autonomous underwater vehicles (AUVs) cannot perceive local environments in complex marine environments, where fish can obtain hydrodynamic information about the surrounding environment through a lateral line. Inspired by this biological function, an artificial lateral line system (ALLS) was built on a moving bionic carrier using the pressure sensor in this paper. When the carrier operated with different speeds in the flow field, the pressure distribution characteristics surrounding the carrier were analyzed by numerical simulation, where the effect of the flow angle between the fluid velocity direction and the carrier navigation direction was considered. The flume experiment was carried out in accordance with the simulation conditions, and the analysis results of the experiment were consistent with those in the simulation. The relationship between pressure and fluid velocity was established by a fitting method. Subsequently, the pressure difference method was investigated to establish a relationship model between the pressure difference on both sides of the carrier and the flow angle. Finally, a back propagation neural network model was used to predict the fluid velocity, flow angle, and carrier speed successfully in the unknown fluid environment. The local fluid environment perception by moving carrier carrying ALLS was studied which may promote the engineering application of the artificial lateral line in the local perception, positioning, and navigation on AUVs.

Indexed as

artificial lateral line systemcarrier speedflow anglefluid velocityneural network

Identifiers

PMID32182939
PMCPMC7085528

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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.