Verification and Validation of a Network Algorithm for Single-phase Flow Modeling using Microfluidic Experiments

Document Type : Research Paper

Authors

1 Siberian Federal University, 79 Svobodny pr., Krasnoyarsk, 660041, Russian Federation

2 Kutateladze Institute of Thermophysics, SB RAS, Novosibirsk, 630090, Russian Federation

Abstract

This paper presents the results of the full-scale verification and validation of the mathematical model and numerical algorithm for the network computation of a single-phase flow in a highly branched pipeline chain. The essential difference of this work from others is that highly branched hydraulic networks with homogeneous and non-uniform permeability, containing more than 70 thousand branches, are considered. Such branched networks are important in many applications. Therefore, the development of algorithms for calculating flows in such networks is very important. The network model is based on hydraulic theory, and the numerical algorithm relies on the network analogue of the well-known control volume method. At the same time, obtaining reliable experimental data for testing models for calculating very branched hydraulic networks is very difficult. In this work, microfluidic technologies are used to solve this problem. Data of laboratory experiments, obtained using microfluidic models of branched networks with homogeneous and heterogeneous permeability, containing several tens of thousands of branches, as well as CFD simulation results in full 3D formulation employing the fine computational grids were used to validate the model. The Reynolds number ranged from 0.81 to 13. Conducted validation has shown a good qualitative and quantitative concordance of the results of network and hydrodynamic simulation, as well as the data of the microfluidic experiments. The error in determining the total pressure drop in the branched hydraulic network with heterogeneous permeability, containing 37,855 nodes and 74,900 branches, did not exceed 5%. It has been demonstrated that the speed of solving a single-phase flow problem in a highly branched chain using network simulation techniques is 60 times more of magnitude higher as compared to CFD simulation at virtually the same accuracy.

Keywords

Main Subjects

Publisher’s Note Shahid Chamran University of Ahvaz remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

[1] Merenkov, A.P., Khasilev, V.Y., Theory of Hydraulic Circuits, Nauka, Moscow, 1985.
[2] Novitsky, N.N., Tevyashev, A.D., Pipeline systems of power engineering: Methodical and applied problems of simulation, Nauka, Novosibirsk, 2015.
[3] Todini, E., Santopietro, S., Gargano, R., Rossman, L.A., Pressure Flow–Based Algorithms for Pressure-Driven Analysis of Water Distribution Networks, ASCE Journal of Water Resources Planning and Management, 147(8), 2021, DOI: 10.1061/(ASCE)WR.1943-5452.0001401.
[4] Wilson, R., Introduction to Graph Theory, Mir, Moscow, 1977
[5] Singh, M., Mohanty, K.K., Dynamic modeling of drainage through three-dimensional porous materials, Chemical Engineering Science, 58(1), 2003, 1-18.
[6] Regaieg, M., McDougall, S.R., Bondino, I., Hamon, G., Finger Thickening during Extra-Heavy Oil Waterflooding: Simulation and Interpretation Using Pore-Scale Modelling, PLoS One, 12(1), 2017, e0169727.
[7] Aghaei, A., Piri, M., Direct pore-to-core up-scaling of displacement processes: Dynamic pore network modeling and experimentation, Journal of Hydrology, 522, 2015, 488-509.
[8] Piri, M., Blunt, M.J., Three-dimensional mixed-wet random pore-scale network modeling of two- and three-phase flow in porous media. I. Model description, Physical Review E, 71(2), 2005, 026301.
[9] Joekar-Niasar, V., Hassanizadeh, S.M., Analysis of Fundamentals of Two-Phase Flow in Porous Media Using Dynamic Pore-Network Models: A Review, Critical Reviews in Environmental Science and Technology, 42(18), 2012, 1895-1976.
[10] Filimonov, S.A., Pryazhnikov, M.I., Pryazhnikov, A.I., Minakov, A.V., Development and Testing of a Mathematical Model for Dynamic Network Simulation of the Oil Displacement Process, Fluids, 7(9), 2022, 311.
[11] Lin, D., Hu, L., Bradford, S.A., Pore-network modeling of colloid transport and retention considering surface deposition, hydrodynamic bridging, and straining, Journal of Hydrology, 603(B), 2021, 127020.
[12] Li, Z., Yang, H., Sun, Z., Espinoza, D.N., Balhoff, M.T., A Probability-Based Pore Network Model of Particle Jamming in Porous Media, Transport in Porous Media, 139(2), 2021, 419-445.
[13] Filimonov, S.A., Dekterev A.A., Sentyabov, A.V., Minakov, A.V., Simulation of conjugate heat transfer in a microchannel system by a hybrid algorithm, Journal of Applied and Industrial Mathematics, 9(4), 2015, 469-479.
[14] Weishaupt, K., Helmig, R., A Dynamic and Fully Implicit Non‐Isothermal, Two‐Phase, Two‐Component Pore‐Network Model Coupled to Single‐Phase Free Flow for the Pore‐Scale Description of Evaporation Processes, Water Resources Research, 57(4), 2021, e2020WR028772.
[15] Zhao, J., Qin, F., Kang, Q., Derome, D., Carmeliet, J., Pore-scale simulation of drying in porous media using a hybrid lattice Boltzmann: pore network model, Drying Technology An International Journal, 40(4), 2022, 719-734.
[16] Agaesse, T., Lamibrac, A., Buchi, F.N., Pauchet, J., Prat, M., Validation of pore network simulations of ex-situ water distributions in a gas diffusion layer of proton exchange membrane fuel cells with X-ray tomographic images, Journal of Power Sources, 331, 2016, 462-474.
[17] Fani, M., Pourrafshary, P., Mostaghimi, P., Mosavat, N., Application of microfluidics in chemical enhanced oil recovery: A review, Fuel, 315, 2022, 123225.
[18] Pryazhnikov, M.I., Minakov, A.V., Guzei, D.V., Yakimov, A.S., Flow Regimes Characteristics of Water-crude Oil in a Rectangular Y-microchannel, Journal of Applied and Computational Mechanics, 8(2), 2022, 655-670.
[19] Lei, W., Liu, T., Xie, C., Yang, H., Wu, T., Wang, M., Enhanced oil recovery mechanism and recovery performance of micro-gel particle suspensions by microfluidic experiments, Energy Science and Engineering, 8, 2020, 986–998.
[20] Al-Kharusi, A.S., Blunt, M.J., Network extraction from sandstone and carbonate pore space images, Journal of Petroleum Science and Engineering, 56(4), 2007, 219–231.
[21] Raeini, A.Q., Bijeljic, B., Blunt, M.J., Generalized network modeling: Network extraction as a coarse-scale discretization of the void space of porous media, Physical Review E, 96(1), 2017, 013312.
[22] Liu, Y., Gong, W., Zhao, Y., Jin, X., Wang, M., A pore-throat segmentation method based on local hydraulic resistance equivalence for pore-network modeling, Water Resources Research, 58, 2020, e2022WR033142.
[23] Patankar, S.V., Numerical heat transfer and fluid flow, Series in computational methods in mechanics and thermal sciences, Hemisphere publishing corporation, Washington, 1980.
[24] Filimonov, S.A., Neobyavlyayushchiy, P.A., Mikhienkova, E.I., An application of hybrid simulation algorithm for a research of the disposal system of noxious gases in aluminium production, Vestnik Tomskogo Gosudarstvennogo Universiteta. Matematika i Mekhanika, 44(6), 2016, 64-79.
[25] Mihienkova, E.I., Filimonov, S.A., Neobyavlyayuschiy, P.A., Boykov, D.V., Calculation of Industrial Enterprise Ventilation System by Network Integral Method, MATEC Web of Conferences, 72, 2016, 01070.