Sub-particle-scale investigation of seepage in sands

Taylor, Howard F., O'Sullivan, Catherine, Sim, Way Way and Carr, Simon ORCID logo ORCID: https://orcid.org/0000-0003-4487-3551 (2017) Sub-particle-scale investigation of seepage in sands. Soils and Foundations, 57 (3). pp. 439-452.

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Official URL: https://doi.org/10.1016/j.sandf.2017.05.010

Abstract

While seepage poses significant challenges to many geotechnical projects and hydraulic conductivity is a key soil property, the fundamental pore-scale understanding of the water flow in soil is poor. The seepage velocities considered in geotechnical engineering are area-averaged flow rates and their relation to the actual fluid velocity is unclear. Some of the predictive formulae for sand currently used in engineering practice were developed using simplified particle-scale analytical models whose validity is not well-established. Recent advances in modelling and imaging enable these uncertainties associated with seepage to be addressed and this paper proposes a first principles simulation approach in which the flow in the void space is modelled by applying Computational Fluid Dynamics (CFD) to void geometries obtained using X-ray micro-Computed Tomography (microCT). The model was verified by comparing it to hydraulic conductivity data from laboratory permeameter tests on the same materials. The generated data provide significant sub-particle-scale insight into fluid velocities and head loss. The results are used to show that the existing models for predicting hydraulic conductivity struggle to account for the full range of particle variables and fail to explain the true governing variables, which relate to the micro-scale properties of the void space.

Item Type: Article
Journal / Publication Title: Soils and Foundations
Publisher: Elsevier / Japanese Geotechnical Society
ISSN: 0038-0806
Departments: Academic Departments > Science, Natural Resources & Outdoor Studies (SNROS) > STEM
Additional Information: This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Awarded “Best Paper of 2017” by the journal Editorial board.
Depositing User: Anna Lupton
Date Deposited: 15 Mar 2019 13:53
Last Modified: 12 Jan 2024 17:31
URI: https://insight.cumbria.ac.uk/id/eprint/4555

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