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Selected papers from ISMTMF-2019
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Selected papers from ISMTMF-2019

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (30 September 2020) | Viewed by 7312

Special Issue Editors

Prof. Dr. Hiroshige Kikura

E-Mail Website
Chief Guest Editor
Tokyo Institute of Technology, Tokyo, Japan
Interests: nuclear safety; thermal hydrodynamics; process instrumentation; transportation of radioactive materials; vitrified waste storage; deep geological repository; solar energy system; solar power system; robotic measurement
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Chao Tan

E-Mail Website
Co-Guest Editor
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
Interests: process measurement and instrumentation; process tomography
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Measurement of multiphase flow has been a challenging topic in the process industries and flow mechanism research, such as chemical engineering, nuclear engineering, food engineering, and biomedical engineering. Development of new sensing techniques is significant to monitor, model, and then control such a complex process. It is thus considered one of the ultimate challenges in industrial measurement and instrumentation.

Following the successful event of International Symposium on Measurement Techniques for Multiphase Flows (ISMTMF-2019), this Special Issue will select a set of papers of high quality while also closely related to the topic of sensors from conference proceedings, but after a thorough technical extension. All submitted papers will undertake the same review procedure as ordinary submissions but by the reviewers in the most focused field. We hope this Special Issue will present the latest development and the state-of-art sensing technologies of multiphase flow in various fields.

Prof. Dr. Chao Tan
Prof. Dr. Hiroshige Kikura
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Multiphase flow
  • Process tomography and measurement
  • Flow measurement and instrumentation
  • Complex process diagnosis

Published Papers (3 papers)

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Research

13 pages, 14508 KiB  
Article
Clustering-Based Component Fraction Estimation in Solid–Liquid Two-Phase Flow in Dredging Engineering
by Chang Sun, Shihong Yue, Qi Li and Huaxiang Wang
Sensors 2020, 20(19), 5697; https://doi.org/10.3390/s20195697 - 06 Oct 2020
Cited by 5 | Viewed by 1598
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Selected papers from ISMTMF-2019)
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13 pages, 3023 KiB  
Article
Solid Concentration Estimation by Kalman Filter
by Yongguang Tan and Shihong Yue
Sensors 2020, 20(9), 2657; https://doi.org/10.3390/s20092657 - 06 May 2020
Cited by 2 | Viewed by 1796
Abstract
One of the major tasks in process industry is solid concentration (SC) estimation in solid–liquid two-phase flow in any pipeline. The γ-ray sensor provides the most used and direct measurement to SC, but it may be inaccurate due to very local measurements [...] Read more.
One of the major tasks in process industry is solid concentration (SC) estimation in solid–liquid two-phase flow in any pipeline. The γ-ray sensor provides the most used and direct measurement to SC, but it may be inaccurate due to very local measurements and inaccurate density baseline. Alternatively, under various conditions there are a tremendous amount of indirect measurements from other sensors that can be used to adjust the accuracy of SC estimation. Consequently, there is complementarity between them, and integrating direct and indirect measurements is helpful to improve the accuracy of SC estimation. In this paper, after recovering the interrelation of these measurements, we proposed a new SC estimation method according to Kalman filter fusion. Focusing on dredging engineering fields, SCs of representative flow pattern were tested. The results show that our proposed methods outperform the fused two types of measurements in real solid–liquid two-phase flow conditions. Additionally, the proposed method has potential to be applied to other fields as well as dredging engineering. Full article
(This article belongs to the Special Issue Selected papers from ISMTMF-2019)
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16 pages, 4768 KiB  
Article
A New Logging-While-Drilling Method for Resistivity Measurement in Oil-Based Mud
by Yongkang Wu, Baoping Lu, Wei Zhang, Yandan Jiang, Baoliang Wang and Zhiyao Huang
Sensors 2020, 20(4), 1075; https://doi.org/10.3390/s20041075 - 16 Feb 2020
Cited by 2 | Viewed by 3450
Abstract
Resistivity logging is an important technique for identifying and estimating reservoirs. Oil-based mud (OBM) can improve drilling efficiency and decrease operation risks, and has been widely used in the well logging field. However, the non-conductive OBM makes the traditional logging-while-drilling (LWD) method with [...] Read more.
Resistivity logging is an important technique for identifying and estimating reservoirs. Oil-based mud (OBM) can improve drilling efficiency and decrease operation risks, and has been widely used in the well logging field. However, the non-conductive OBM makes the traditional logging-while-drilling (LWD) method with low frequency ineffective. In this work, a new oil-based LWD method is proposed by combining the capacitively coupled contactless conductivity detection (C4D) technique and the inductive coupling principle. The C4D technique is to overcome the electrical insulation problem of the OBM and construct an effective alternating current (AC) measurement path. Based on the inductive coupling principle, an induced voltage can be formed to be the indirect excitation voltage of the AC measurement path. Based on the proposed method, a corresponding logging instrument is developed. Numerical simulation was carried out and results show that the logging instrument has good measurement accuracy, deep detection depth and high vertical resolution. Practical experiments were also carried out, including the resistance box experiment and the well logging experiment. The results of the resistance box experiment show that the logging instrument has good resistance measurement accuracy. Lastly, the results of the well logging experiment indicate that the logging instrument can accurately reflect the positions of different patterns on the wellbore of the experimental well. Both numerical simulation and practical experiments verify the feasibility and effectiveness of the new method. Full article
(This article belongs to the Special Issue Selected papers from ISMTMF-2019)
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