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Clustering-Based Component Fraction Estimation in Solid–Liquid Two-Phase Flow in Dredging Engineering

Component fraction (CF) is one of the most important parameters in multiple-phase flow. Due to the complexity of the solid–liquid two-phase flow, the CF estimation remains unsolved both in scientific research and industrial application for a long time. Electrical resistance tomography (ERT) is an ad...

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Autores principales: Sun, Chang, Yue, Shihong, Li, Qi, Wang, Huaxiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583740/
https://www.ncbi.nlm.nih.gov/pubmed/33036261
http://dx.doi.org/10.3390/s20195697
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author Sun, Chang
Yue, Shihong
Li, Qi
Wang, Huaxiang
author_facet Sun, Chang
Yue, Shihong
Li, Qi
Wang, Huaxiang
author_sort Sun, Chang
collection PubMed
description Component fraction (CF) is one of the most important parameters in multiple-phase flow. Due to the complexity of the solid–liquid two-phase flow, the CF estimation remains unsolved both in scientific research and industrial application for a long time. Electrical resistance tomography (ERT) is an advanced type of conductivity detection technique due to its low-cost, fast-response, non-invasive, and non-radiation characteristics. However, when the existing ERT method is used to measure the CF value in solid–liquid two-phase flow in dredging engineering, there are at least three problems: (1) the dependence of reference distribution whose CF value is zero; (2) the size of the detected objects may be too small to be found by ERT; and (3) there is no efficient way to estimate the effect of artifacts in ERT. In this paper, we proposed a method based on the clustering technique, where a fast-fuzzy clustering algorithm is used to partition the ERT image to three clusters that respond to liquid, solid phases, and their mixtures and artifacts, respectively. The clustering algorithm does not need any reference distribution in the CF estimation. In the case of small solid objects or artifacts, the CF value remains effectively computed by prior information. To validate the new method, a group of typical CF estimations in dredging engineering were implemented. Results show that the new method can effectively overcome the limitations of the existing method, and can provide a practical and more accurate way for CF estimation.
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spelling pubmed-75837402020-10-28 Clustering-Based Component Fraction Estimation in Solid–Liquid Two-Phase Flow in Dredging Engineering Sun, Chang Yue, Shihong Li, Qi Wang, Huaxiang Sensors (Basel) Article Component fraction (CF) is one of the most important parameters in multiple-phase flow. Due to the complexity of the solid–liquid two-phase flow, the CF estimation remains unsolved both in scientific research and industrial application for a long time. Electrical resistance tomography (ERT) is an advanced type of conductivity detection technique due to its low-cost, fast-response, non-invasive, and non-radiation characteristics. However, when the existing ERT method is used to measure the CF value in solid–liquid two-phase flow in dredging engineering, there are at least three problems: (1) the dependence of reference distribution whose CF value is zero; (2) the size of the detected objects may be too small to be found by ERT; and (3) there is no efficient way to estimate the effect of artifacts in ERT. In this paper, we proposed a method based on the clustering technique, where a fast-fuzzy clustering algorithm is used to partition the ERT image to three clusters that respond to liquid, solid phases, and their mixtures and artifacts, respectively. The clustering algorithm does not need any reference distribution in the CF estimation. In the case of small solid objects or artifacts, the CF value remains effectively computed by prior information. To validate the new method, a group of typical CF estimations in dredging engineering were implemented. Results show that the new method can effectively overcome the limitations of the existing method, and can provide a practical and more accurate way for CF estimation. MDPI 2020-10-06 /pmc/articles/PMC7583740/ /pubmed/33036261 http://dx.doi.org/10.3390/s20195697 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sun, Chang
Yue, Shihong
Li, Qi
Wang, Huaxiang
Clustering-Based Component Fraction Estimation in Solid–Liquid Two-Phase Flow in Dredging Engineering
title Clustering-Based Component Fraction Estimation in Solid–Liquid Two-Phase Flow in Dredging Engineering
title_full Clustering-Based Component Fraction Estimation in Solid–Liquid Two-Phase Flow in Dredging Engineering
title_fullStr Clustering-Based Component Fraction Estimation in Solid–Liquid Two-Phase Flow in Dredging Engineering
title_full_unstemmed Clustering-Based Component Fraction Estimation in Solid–Liquid Two-Phase Flow in Dredging Engineering
title_short Clustering-Based Component Fraction Estimation in Solid–Liquid Two-Phase Flow in Dredging Engineering
title_sort clustering-based component fraction estimation in solid–liquid two-phase flow in dredging engineering
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7583740/
https://www.ncbi.nlm.nih.gov/pubmed/33036261
http://dx.doi.org/10.3390/s20195697
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