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Engineering with Computers
Erschienen in: Engineering with Computers 6/2023

06.04.2023 | Original Article

Comparative study of modelling flows in porous media for engineering applications using finite volume and artificial neural network methods

verfasst von: Pijus Makauskas, Mayur Pal, Vismay Kulkarni, Abhishek Singh Kashyap, Himanshu Tyagi

Erschienen in: Engineering with Computers | Ausgabe 6/2023

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Abstract

A neural solution methodology, using a feed-forward and a convolutional neural networks, is presented for general tensor elliptic pressure equation with discontinuous coefficients. The methodology is applicable for solving single-phase flow in porous medium, which is traditionally solved using numerical schemes like finite-volume methods. The neural solution to elliptic pressure equation is based on machine learning algorithms and could serve as a more effective alternative to finite volume schemes like two-point or multi-point discretization schemes (TPFA or MPFA) for faster and more accurate solution of elliptic pressure equation. Series of 1D and 2D test cases, where the results of Neural solutions are compared to numerical solutions obtained using two-point schemes with range of heterogeneities, are also presented to demonstrate general applicability and accuracy of the Neural solution method.
Vorheriger Artikel The 30th anniversary of the UK Association for Computational Mechanics
Nächster Artikel Meshing using neural networks for improving the efficiency of computer modelling

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Fußnoten
1
A grid is called orthogonal if all grid lines intersect at a right angle.
 
Literatur
1.
Durlofsky LJ (1993) A triangle based mixed finite element finite volume technique for modeling two phase flow through porous media. J Comput Phys 1:252–266CrossRef
2.
Edwards MG (2002) Unstructured, control-volume distributed, full-tensor finite volume schemes with flow based grids. Comput Geo 6:433–452MathSciNetCrossRef
3.
Eigestad GT, Klausen RA (2005) On convergence of multi-point flux approximation o-method; numerical experiment for discontinuous permeability. (2nd edn). Submitted to Numer Meth Part Diff Eqs
4.
5.
Hagan MT, Demuth HB, Beale MH (1996) Neural Network Design. PWS Publishing, Boston
6.
7.
8.
Recktenwald G (2014) The control-volume finite-difference approximation to the diffusion equation
9.
Aavatsmark I (2002) Introduction to multipoint flux approximation for quadrilateral grids. Comput Geo No 6:405–432MathSciNetCrossRef
10.
Kirill Zubov et al. (2021) NeuralPDE: automating physics-informed neural networks (PINNs) with error approximations. http://​arxiv.​org/​2107.​09443arXiv:https://arxiv.org/abs/2107.09443
11.
12.
Pal M, Edwards MG (2006) Effective upscaling using a family of flux-continuous, finite-volume schemes for the pressure equation. In Proceedings, ACME 06 Conference, Queens University Belfast, Northern Ireland-UK, pages 127–130
13.
Pal M, Edwards MG, Lamb AR (2006) Convergence study of a family of flux-continuous, Finite-volume schemes for the general tensor pressure equation. Numer Method Fluids 51(9–10):1177–1203MathSciNetCrossRef
14.
Pal M (2007) Families of control-volume distributed cvd(mpfa) finite volume schemes for the porous medium pressure equation on structured and unstructured grids. PhD Thesis, University of Wales, Swansea-UK
15.
Pal M, Edwards MG (2008) The competing effects of discretization and upscaling - A study using the q-family of CVD-MPFA. ECMOR 2008 - 11th European Conference on the Mathematics of Oil Recovery
16.
Pal M (2010) The effects of control-volume distributed multi-point flux approximation (CVD-MPFA) on upscaling-A study using the CVD-MPFA schemes. Int J Numer Methods Fluids 68:1MathSciNet
17.
Pal M, Edwards MG (2011) Non-linear flux-splitting schemes with imposed discrete maximum principle for elliptic equations with highly anisotropic coefficients. Int J Numer Method Fluids 66(3):299–323MathSciNetCrossRef
18.
19.
20.
Zhang Wenjuan, Kobaisi Al, Mohammed, (2022) On the monotonicity and positivity of physics-informed neural networks for highly anisotropic diffusion equations. MDPI Energ 15:6823
21.
Ahmad S Abushaika (2013) Numerical methods for modelling fluid flow in highly heterogeneous and fractured reservoirs
22.
Pal M, Lamine S, Lie K-A, Krogstad S (2015) Validation of multiscale mixed finite-element method. Int J Numer Methods Fluids 77:223MathSciNetCrossRef
23.
Pal M (2021) On application of machine learning method for history matching and forecasting of times series data from hydrocarbon recovery process using water flooding. Petrol Sci Technol 39:15–16CrossRef
24.
Pal M, Makauskas P, Saxena P, Patil P (2022) The neural upscaling method for single-phase flow in porous medium. Paper Presented at EAGE-ECMOR 2022 Conference
25.
26.
Ricky TQ, Chen Yulia Rubanova, Jesse Bettencourt, David Duvenaud (2018) Neural ordinary differential equations. 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Canada
27.
28.
Wu J-L, XioaH Paterson EG (2018) Physics-informed machine learning approach for augmenting turbulence models: A comprehensive framework. Phys Rev Fluids 2:073602
29.
Russel TF, Wheeler MF (1983) Finite element and finite difference methods for continuous flows in porous media. Chapter 2, in the Mathematics of Reservoir Simulation, R.E. Ewing ed. Front Appl Math SIAM pp 35–106
30.
Raissi M, Perdikaris P, Karniadakis GE (2019) Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. J Comput Phys 378:686–707MathSciNetCrossRef
31.
Zhao J, Zhao W, Ma Z, Yong WA, Dong B (2022) Finding models of heat conduction via machine learning. Int J Heat Mass Transfer 185:122396CrossRef
32.
Huang S, Tao B, Li J, Yin Z (2019) On-line heat flux estimation of a nonlinear heat conduction system with complex geometry using a sequential inverse method and artificial neural network. Int J Heat Mass Transfer 143:118491CrossRef
33.
Wang Y, Zhang S, Ma Z, Yang Q (2020) Artificial neural network model development for prediction of nonlinear flow in porous media. Powder Technol 373:274–288CrossRef
34.
Abad JMN, Alizadeh R, Fattahi A, Doranehgard MH, Alhajri E, Karimi N (2020) Analysis of transport processes in a reacting flow of hybrid nanofluid around a bluff-body embedded in porous media using artificial neural network and particle swarm optimization. J Mol Liquids 313:113492CrossRef
Metadaten
Titel
Comparative study of modelling flows in porous media for engineering applications using finite volume and artificial neural network methods
verfasst von
Pijus Makauskas
Mayur Pal
Vismay Kulkarni
Abhishek Singh Kashyap
Himanshu Tyagi
Publikationsdatum
06.04.2023
Verlag
Springer London
Erschienen in
Engineering with Computers / Ausgabe 6/2023
Print ISSN: 0177-0667
Elektronische ISSN: 1435-5663
DOI
https://doi.org/10.1007/s00366-023-01814-x

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