Analysis of the Influence of the Vortex Shedder Shape on the Metrological Properties of the Vortex Flow Meter

Rząsa, Mariusz; Czapla-Niełacna, B.

doi:10.3390/s21144697

Cited by 12 publications

(6 citation statements)

References 18 publications

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“…It should be noted that the method quality indicator for the LSTM classifier was the RMSE. Impedance tomography is a potential field of science for identifying inclusions inside industrial reactors, containers, and pipes [52,53]. The presented research findings substantiate the preceding assertion due to the complexity of the model transferring the measurement data into the output image and the requirement to solve the incorrectly stated inverse problem, developing an effective and universal technique.…”

Section: Comparison Of the Reconstructions Based On Simulation Datasupporting

confidence: 79%

Comparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical Tomography

et al. 2021

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Electrical tomography is a non-invasive method of monitoring the interior of objects, which is used in various industries. In particular, it is possible to monitor industrial processes inside reactors and tanks using tomography. Tomography enables real-time observation of crystals or gas bubbles growing in a liquid. However, obtaining high-resolution tomographic images is problematic because it involves solving the so-called ill-posed inverse problem. Noisy input data cause problems, too. Therefore, the use of appropriate hardware solutions to eliminate this phenomenon is necessary. An important cause of obtaining accurate tomographic images may also be the incorrect selection of algorithmic methods used to convert the measurements into the output images. In a dynamically changing environment of a tank reactor, selecting the optimal algorithmic method used to create a tomographic image becomes an optimization problem. This article presents the machine learning method’s original concept of intelligent selection depending on the reconstructed case. The long short-term memory network was used to classify the methods to choose one of the five homogenous methods—elastic net, linear regression with the least-squares learner, linear regression with support vector machine learner, support vector machine model, or artificial neural networks. In the presented research, tomographic images of selected measurement cases, reconstructed using five methods, were compared. Then, the selection methods’ accuracy was verified thanks to the long short-term memory network used as a classifier. The results proved that the new concept of long short-term memory classification ensures better tomographic reconstructions efficiency than imaging all measurement cases with single homogeneous methods.

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Section: Comparison Of the Reconstructions Based On Simulation Datasupporting

confidence: 79%

Comparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical Tomography

et al. 2021

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“…Many deterministic, statistical, or computational intelligence methods solve optimisation tasks [42][43][44][45]. In this paper, the authors concentrate on the use of machine learning.…”

Section: Algorithms and Methodsmentioning

confidence: 99%

Application of Electrical Tomography Imaging Using Machine Learning Methods for the Monitoring of Flood Embankments Leaks

et al. 2021

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This paper presents an application for the monitoring of leaks in flood embankments by reconstructing images in electrical tomography using logistic regression machine learning methods with elastic net regularisation, PCA and wave preprocessing. The main advantage of this solution is to obtain a more accurate spatial conductivity distribution inside the studied object. The described method assumes a learning system consisting of multiple equations working in parallel, where each equation creates a single point in the output image. This enables the efficient reconstruction of spatial images. The research focused on preparing, developing, and comparing algorithms and models for data analysis and reconstruction using a proprietary electrical tomography solution. A reliable measurement solution with sensors and machine learning methods makes it possible to analyse damage and leaks, leading to effective information and the eventual prevention of risks. The applied methods enable the improved resolution of the reconstructed images and the possibility to obtain them in real-time, which is their distinguishing feature compared to other methods. The use of electrical tomography in combination with specific methods for image reconstruction allows for an accurate spatial assessment of leaks and damage to dikes.

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“…For example, the calorimetric flow meters use the physical effects that amount of flows are capable of absorbing different amounts of heat from a heat source on a measuring tip [2,3]. In the vortex flow meters, piezoelectric or capacitancetype sensors are implemented to detect pressure oscillation due to vortices of fluids around a bluff body [4]. The resulting frequency is an indication of the flow rate level.…”

Section: Introductionmentioning

confidence: 99%

Experimental Investigation of Novel Liquid Flow Measurement by Using Thermoelectric Module

Oncha-ek,

Prasongsil,

Saitong

et al. 2023

J. Phys.: Conf. Ser.

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This paper presents an experimental investigation of liquid flow measurement by using a thermoelectric module. The measuring principle is based on the Seebeck effect of the thermoelectric module and the energy balance of convective heat transfer between the liquid flow and the thermoelectric module during implementation. The experimental studies confirm the viability of the thermoelectric module in determining the flow rates of water. In experiments, the results show that the determined flow rates by using the thermoelectric module are close to the actual flow rates of water with a coefficient of determination R2 of 0.9965. In addition to the straightforward measuring principle, low-cost material, no limitation of fluid types as well as directions of flows in a pipe, and no requirement of electrical power source make the thermoelectric module noteworthy as a sensor material in developing liquid flow meters.

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Analysis of the Influence of the Vortex Shedder Shape on the Metrological Properties of the Vortex Flow Meter

Cited by 12 publications

References 18 publications

Comparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical Tomography

Comparison of Machine Learning Methods for Image Reconstruction Using the LSTM Classifier in Industrial Electrical Tomography

Application of Electrical Tomography Imaging Using Machine Learning Methods for the Monitoring of Flood Embankments Leaks

Experimental Investigation of Novel Liquid Flow Measurement by Using Thermoelectric Module

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