Next Article in Journal
Glass-Ceramics Processed by Spark Plasma Sintering (SPS) for Optical Applications
Next Article in Special Issue
Effect of Liquid Viscosity and Flow Orientation on Initial Waves in Annular Gas–Liquid Flow
Previous Article in Journal
Application of Box-Behnken Design and Desirability Function for Green Prospection of Bioactive Compounds from Isochrysis galbana
Previous Article in Special Issue
Temperature Fields of the Droplets and Gases Mixture
Open AccessArticle

Boiling Flow Pattern Identification Using a Self-Organizing Map

Department of Mechanics and Applied Computer Science, Faculty of Mechanical Engineering Bialystok University of Technology, Wiejska 45 C, 15-351 Bialystok, Poland
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(8), 2792;
Received: 21 March 2020 / Revised: 8 April 2020 / Accepted: 14 April 2020 / Published: 17 April 2020
(This article belongs to the Special Issue Heat and Mass Transfer in Intense Liquid Evaporation)
In the paper, a self-organizing map combined with the recurrence quantification analysis was used to identify flow boiling patterns in a circular horizontal minichannel with an inner diameter of 1 mm. The dynamics of the pressure drop during density-wave oscillations in a single pressure drop oscillations cycle were considered. It has been shown that the proposed algorithm allows us to distinguish five types of non-stationary two-phase flow patterns, such as bubble flow, confined bubble flow, wavy annular flow, liquid flow, and slug flow. The flow pattern identification was confirmed by images obtained using a high-speed camera. Taking into consideration the oscillations between identified two-phase flow patterns, the four boiling regimes during a single cycle of the long-period pressure drop oscillations are classified. The obtained results show that the proposed combination of recurrence quantification analysis (RQA) and a self-organizing map (SOM) in the paper can be used to analyze changes in flow patterns in non-stationary boiling. It seems that the use of more complex algorithms of neural networks and their learning process can lead to the automation of the process of identifying boiling regimes in minichannel heat exchangers. View Full-Text
Keywords: boiling flow patterns; two-phase flow instability; flow boiling; self-organizing map; recurrence quantification analysis boiling flow patterns; two-phase flow instability; flow boiling; self-organizing map; recurrence quantification analysis
Show Figures

Figure 1

MDPI and ACS Style

Zaborowska, I.; Grzybowski, H.; Mosdorf, R. Boiling Flow Pattern Identification Using a Self-Organizing Map. Appl. Sci. 2020, 10, 2792.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Search more from Scilit
Back to TopTop