Si Suo , Sajjad Foroughi , Martin J. Blunt , Catherine O’Sullivan
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引用次数: 0
Abstract
Non-Newtonian fluid flows in porous media are critical in various subsurface and geotechnical engineering applications. However, accurately predicting such flows remains challenging due to the complex fluid rheology and intricate pore structures. This study focuses on polymer fluids with shear-thinning rheology with the motivation of advancing understanding of polymer support fluids for ground engineering applications. To address the limitations of existing models, we derive a theoretical conductance model for polymer flow in a capillary tube, based on which a customized pore-network method is developed. Our simulations reveal three distinct flow regimes, highlighting the impact of the rheology on flow dynamics. Notably, flow heterogeneity amplifies as the shear-thinning feature directs more flow through wider pores, where the effective viscosity decreases more significantly compared to narrower ones. A generalized Darcy’s law is formulated for non-Newtonian fluids, validated through pore-network modeling on 60 distinct sphere packings. The proposed framework is adaptable to a broad range of non-Newtonian fluids, offering valuable insights for scaling up to field-scale applications.
期刊介绍:
The use of computers is firmly established in geotechnical engineering and continues to grow rapidly in both engineering practice and academe. The development of advanced numerical techniques and constitutive modeling, in conjunction with rapid developments in computer hardware, enables problems to be tackled that were unthinkable even a few years ago. Computers and Geotechnics provides an up-to-date reference for engineers and researchers engaged in computer aided analysis and research in geotechnical engineering. The journal is intended for an expeditious dissemination of advanced computer applications across a broad range of geotechnical topics. Contributions on advances in numerical algorithms, computer implementation of new constitutive models and probabilistic methods are especially encouraged.