Soft-ANN based correlation for air-water two-phase flow pressure drop estimation in a vertical mini-channel

In this paper, an Artificial Neural Network soft matrix correlation to estimate the pressure drop of air-water two-phase flow is developed. The applicability of the model is extended by using dimensionless physical numbers as inputs (Air-Reynolds number, Water-Reynolds number, and the ratio of Air I...

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Bibliographic Details
Published in:Proceedings of the Institution of Mechanical Engineers. Part C, Journal of mechanical engineering science Journal of mechanical engineering science, 2022-02, Vol.236 (3), p.1430-1442
Main Authors: Manuel, Barroso-Maldonado Juan, Manuel, Riesco-Ávila José, Martín, Picón-Núñez, Manuel, Belman-Flores Juan
Format: Article
Language:English
Subjects:
Artificial neural networks
Correlation
Density
Diameters
Dimensionless numbers
Fluid flow
Laminar flow
Pressure drop
Reynolds number
Two phase flow
Viscosity
Water drops
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Summary:In this paper, an Artificial Neural Network soft matrix correlation to estimate the pressure drop of air-water two-phase flow is developed. The applicability of the model is extended by using dimensionless physical numbers as inputs (Air-Reynolds number, Water-Reynolds number, and the ratio of Air Inertial Forces to Water Inertial Forces), so the model can be implemented for vertical pipes with the proper combination of diameter-velocity-density-viscosity allowing estimations of dimensional numbers within the range of: Air-Reynolds numbers (430–6100), Water-Reynolds number (2400–7200), and Air-Water-Inertial forces ratio (1.6–1834), including the diameter range from 3 to 28 mm. Experimental measurements of frictional pressure drop of water-air mixtures are determined at different conditions. A search of the most suitable density, viscosity, and friction models was conducted and used in the model. The performance of the proposed ANN correlation is compared against published expressions showing good approximation to experimental data; results indicate that the most used correlations are within a mean relative error (mre) of 23.9–30.7%, while the proposed ANN has a mre = 0.9%. Two additional features are discussed: i) the applicability and generality of the ANN using untrained data, ii) the applicability in laminar, transitional, and turbulent flow regimen. To take the approach beyond a robust performance mapping, the methodology to translate the ANN into a programmable equation is presented.
ISSN:0954-4062
2041-2983
DOI:10.1177/09544062211020329