Vibration-based multiphase-flow pattern classification via machine learning techniques

Sestito, Guilherme Serpa; Álvarez-Briceño, Ricardo; Ribatski, Gherhardt; Silva, Maíra Martins da; Oliveira, L.

doi:10.1016/j.flowmeasinst.2022.102290

Cited by 10 publications

(2 citation statements)

References 26 publications

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“…Machine learning techniques, such as decision trees, gradient boosting, random forests, fuzzy adaptive neural inference systems, and artificial neural networks, have gained acclaim for their robust predictive capabilities. They have become potent tools for crafting highly accurate models that estimate the thermophysical and heat transport properties of nanofluids [9], [10]. Among these, Artificial Neural Networks stand out as a soft computing paradigm that employs regression models to express targets as nonlinear functions derived from relevant features [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

A Study on Machine Learning Models’ Capability as an Alternative for CFD in Modeling Heat Transfer

Godasiaei

2024

Preprint

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This research assesses the viability of utilizing machine learning models as alternatives to computational fluid dynamics for heat transfer modeling. Driven by a heightened interest in evaluating the benefits of machine learning for precise predictions in forced convection heat transfer, the study investigates the potential of artificial neural networks, super-gradient boosting, and random forests as alternatives to traditional methods. Employing artificial intelligence algorithms and implemented through Python software, the methodology conducts a meticulous analysis of a dataset comprising 210 data points. The dataset includes critical heat transfer parameters such as nanoparticle characteristics, size, Reynolds number, Nusselt number, and volume fraction. The selected machine learning algorithms are systematically applied to predict forced convection heat transfer outcomes, and their accuracy is rigorously assessed through comparisons using machine learning R-Squared, Mean Absolute Error, and Root Mean Squared Error values. The results demonstrate promising predictive capabilities, with super-gradient boosting, random forest, and artificial neural network models achieving accuracies of 91%, 90%, and 86%, respectively. The corresponding mean squared error values of 1.07, 1.65, and 16.1 underscore the high accuracy and predictive prowess of these machine learning models in simulating forced convection heat transfer processes.

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Section: Introductionmentioning

confidence: 99%

A Study on Machine Learning Models’ Capability as an Alternative for CFD in Modeling Heat Transfer

Godasiaei

2024

Preprint

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“…For instance, Zhang et al studied the identification of oil–gas two-phase flow patterns based on machine learning and electrical capacitance tomography [ 23 ]. Sestito et al classified two-phase flow patterns based on frequency-domain features by machine learning-based classifiers [ 24 ]. Amirsoleymani et al explored two-phase flow-pattern identification in compressed air energy storage systems via dimensional analysis coupled with machine learning [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Identifying Flow Patterns in a Narrow Channel via Feature Extraction of Conductivity Measurements with a Support Vector Machine

Yang

Wang

et al. 2023

Sensors

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In this work, a visualization experiment for rectangular channels was carried out to explore gas–liquid two-phase flow characteristics. Typical flow patterns, including bubble, elastic and mixed flows, were captured by direct imaging technology and the corresponding measurements with fluctuation characteristics were recorded by using an electrical conductivity sensor. Time-domain and frequency-domain characteristics of the corresponding electrical conductivity measurements of each flow pattern were analyzed with a probability density function and a power spectral density curve. The results showed that the feature vectors can be constructed to reflect the time–frequency characteristics of conductivity measurements successfully by introducing the quantized characteristic parameters, including the maximum power of the frequency, the standard deviation of the power spectral density, and the range of the power distribution. Furthermore, the overall recognition rate of the four flow patterns measured by the method was 93.33% based on the support vector machine, and the intelligent two-phase flow-pattern identification method can provide a new technical support for the online recognition of gas–liquid two-phase flow patterns in rectangular channels. It may thus be concluded that this method should be of great significance to ensure the safe and efficient operation of relevant industrial production systems.

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Application of selected methods of computational intelligence to recognition of the liquid–gas flow regime in pipeline by use gamma absorption and frequency domain feature extraction

Hanus,

Zych,

Kusy

et al. 2024

Measurement

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Vibration-based multiphase-flow pattern classification via machine learning techniques

Cited by 10 publications

References 26 publications

A Study on Machine Learning Models’ Capability as an Alternative for CFD in Modeling Heat Transfer

A Study on Machine Learning Models’ Capability as an Alternative for CFD in Modeling Heat Transfer

Identifying Flow Patterns in a Narrow Channel via Feature Extraction of Conductivity Measurements with a Support Vector Machine

Application of selected methods of computational intelligence to recognition of the liquid–gas flow regime in pipeline by use gamma absorption and frequency domain feature extraction

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