Loughborough University
Leicestershire, UK
LE11 3TU
+44 (0)1509 263171
Loughborough University

Loughborough University Institutional Repository

Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/14352

Title: Artificial neural network (ANN) modeling of dynamic effects on two-phase flow in homogenous porous media
Authors: Hanspal, Navraj S.
Allison, Babatunde A.
Deka, Lipika
Das, Diganta Bhusan
Keywords: Artificial neural network (ANN)
Dynamic coefficient
Porous media
Regression models
Two-phase flow
Issue Date: 2013
Publisher: © IWA Publishing
Citation: HANSPAL, N.S. ... et al, 2013. Artificial neural network (ANN) modeling of dynamic effects on two-phase flow in homogenous porous media. Journal of Hydroinformatics, 15 (2), pp. 540 - 554.
Abstract: The dynamic effect in two-phase flow in porous media indicated by a dynamic coefficient τ depends on a number of factors (e.g. medium and fluid properties). Varying these parameters parametrically in mathematical models to compute τ incurs significant time and computational costs. To circumvent this issue, we present an artificial neural network (ANN)-based technique for predicting τ over a range of physical parameters of porous media and fluid that affect the flow. The data employed for training the ANN algorithm have been acquired from previous modeling studies. It is observed that ANN modeling can appropriately characterize the relationship between the changes in the media and fluid properties, thereby ensuring a reliable prediction of the dynamic coefficient as a function of water saturation. Our results indicate that a double-hidden-layer ANN network performs better in comparison to the single-hidden-layer ANN models for the majority of the performance tests carried out. While single-hidden-layer ANN models can reliably predict complex dynamic coefficients (e.g. water saturation relationships) at high water saturation content, the double-hidden-layer neural network model outperforms at low water saturation content. In all the cases, the single- and double-hidden-layer ANN models are better predictors in comparison to the regression models attempted in this work.
Description: The definitive peer-reviewed and edited version of this article is published in Journal of Hydroinformatics, 15 (2), pp. 540–554, 2013, 10.2166/hydro.2012.119 [© IWA Publishing] and is available at www.iwapublishing.com
Sponsor: We acknowledge the financial support from the EPSRC, UK for funding the project GR/S94315/01, Micro-Heterogeneity Effects on Dynamic Capillary Pressure–Saturation Relationships in Porous Figure 13 Figure 14 Media, which enabled us to conduct the CFD modeling studies to generate the reference data used for the ANN simulations in this work.
Version: Accepted for publication
DOI: 10.2166/hydro.2012.119
URI: https://dspace.lboro.ac.uk/2134/14352
Publisher Link: http://dx.doi.org/10.2166/hydro.2012.119
ISSN: 1464-7141
Appears in Collections:Published Articles (Civil and Building Engineering)
Published Articles (Chemical Engineering)

Files associated with this item:

File Description SizeFormat
Hanspal_ANN_August_13_2012_Unmarked_accepted.pdfAccepted version670.26 kBAdobe PDFView/Open


SFX Query

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.