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Artificial Neural Network (ANN) modelling of scale dependent dynamic capillary pressure effects in two-phase flow in porous media
journal contribution
posted on 2014-12-02, 14:53 authored by Luqman K. Abidoye, Diganta DasDiganta DasA number of numerical simulations and experimental investigations have reported the impact of specific domain size on the dynamic capillary pressure which is one of the forces that govern two-phase flow in porous media. These investigations are often achieved with time-consuming experiments and/or costly/complex computational methods. In view of this, a computationally efficient and simple alternative platform for the prediction of the domain scale dependence of the dynamic capillary pressure effects, defined in terms of a coefficient named as dynamic coefficient ( ), is developed using artificial neural network (ANN). The input parameters consist of the phase saturation, media permeability, capillary entry pressure, viscosity ratio, density ratio, temperature, pore size distribution index, porosity and domain volume with corresponding output obtained at different domain scales. Good generalization of the model was achieved by acquiring data from independent sources comprising experiments and numerical simulations. Different ANN configurations as well as linear and non-linear multivariate regression models were tested using a number of performance criteria. Findings in this work showed that the ANN structures with two hidden layers perform better than those with single hidden layer. In particular, the ANN configuration with 13 and 15 neurons in the first and second hidden layers, respectively, performed the best. Using this best-performing ANN, effects of increased domain size were predicted for three separate experimental results obtained from literature and our laboratory with different domain scales. Results showed increased magnitude of as the domain size increases for all the independent experimental data considered. This work shows the applicability and techniques of using ANN in the prediction of scale dependence of two-phase flow parameters.
History
School
- Aeronautical, Automotive, Chemical and Materials Engineering
Department
- Chemical Engineering
Published in
Journal of HydroinformaticsVolume
0Pages
0 - ?Citation
ABIDOYE, L.K. and DAS, D.B., 2015. Artificial Neural Network (ANN) modelling of scale dependent dynamic capillary pressure effects in two-phase flow in porous media. Journal of Hydroinformatics, 17 (3), pp. 446–461.Publisher
© IWA PublishingVersion
- AM (Accepted Manuscript)
Publisher statement
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/Publication date
2015Notes
© IWA Publishing [2015]. The definitive peer-reviewed and edited version of this article is published in Journal of Hydroinformatics, 17 (3), pp. 446–461 doi: 10.2166/hydro.2014.079 and is available at www.iwapublishing.comISSN
1464-7141Publisher version
Language
- en