A capillary-tube model for two-phase transient flow through bentonite materials

2007 ◽  
Vol 44 (12) ◽  
pp. 1446-1461 ◽  
Author(s):  
Greg Siemens ◽  
James A. Blatz ◽  
Douglas Ruth
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Unsaturated Soils ◽  
Transient Flow ◽  
Capillary Tube ◽  
High Plasticity ◽  
Tube Model ◽  
Capillary Tube Model ◽  
Flow Through

Swelling mechanisms occurring on the pore scale or at the molecular level of high plasticity, unsaturated soils often control macroscopic behaviour. In this paper, a new capillary tube model is proposed. The model is used to represent laboratory-scale infiltration tests on a bentonite-rich soil. The goal is to develop a greater understanding of bulk behaviour by closely examining microscopic behaviour. A decrease in hydraulic conductivity with increasing water content has been observed in laboratory studies on flow through shrink–swell materials. The proposed mechanism causing a decrease in conductivity is a change in pore-size distribution. The unique feature of the new capillary-tube model is that, as water flows down the tube, the tube’s cross-sectional area contracts to restrict flow, thus representing the change in pore-size distribution observed in the physical tests. Flow data from the capillary tube are used to model the laboratory results, and new insight is gained into bulk flow behaviour. Finally, a comparison with a three-dimensional network model for bentonite-coated sand mixtures is presented.

Two sides of the same inverse problem, in identifying the pore size distribution based on experiments with non-Newtonian fluids

2021 ◽  
Author(s):  
Martin Lanzendörfer
Keyword(s):  
Inverse Problem ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Forward Problem ◽  
Size Distributions ◽  
Pore Sizes ◽  
Pore Size Distributions ◽  
Flow Through ◽  
Effective Pore Size

<p>Following the capillary bundle concept, i.e. idealizing the flow in a saturated porous media in a given direction as the Hagen-Poiseuille flow through a number of tubular capillaries, one can very easily solve what we would call the <em>forward problem</em>: Given the number and geometry of the capillaries (in particular, given the pore size distribution), the rheology of the fluid and the hydraulic gradient, to determine the resulting flux. With a Newtonian fluid, the flux would follow the linear Darcy law and the porous media would then be represented by one constant only (the permeability), while materials with very different pore size distributions can have identical permeability. With a non-Newtonian fluid, however, the flux resulting from the forward problem (while still easy to solve) depends in a more complicated nonlinear way upon the pore sizes. This has allowed researchers to try to solve the much more complicated <em>inverse problem</em>: Given the fluxes corresponding to a set of non-Newtonian rheologies and/or hydraulic gradients, to identify the geometry of the capillaries (say, the effective pore size distribution).</p><p>The potential applications are many. However, the inverse problem is, as they usually are, much more complicated. We will try to comment on some of the challenges that hinder our way forward. Some sets of experimental data may not reveal any information about the pore sizes. Some data may lead to numerically ill-posed problems. Different effective pore size distributions correspond to the same data set. Some resulting pore sizes may be misleading. We do not know how the measurement error affects the inverse problem results. How to plan an optimal set of experiments? Not speaking about the important question, how are the observed effective pore sizes related to other notions of pore size distribution.</p><p>All of the above issues can be addressed (at least initially) with artificial data, obtained e.g. by solving the forward problem numerically or by computing the flow through other idealized pore geometries. Apart from illustrating the above issues, we focus on <em>two distinct aspects of the inverse problem</em>, that should be regarded separately. First: given the forward problem with <em>N</em> distinct pore sizes, how do different algorithms and/or different sets of experiments perform in identifying them? Second: given the forward problem with a smooth continuous pore size distribution (or, with the number of pore sizes greater than <em>N</em>), how should an optimal representation by <em>N</em> effective pore sizes be defined, regardless of the method necessary to find them?</p>

Unsaturated hydraulic conductivity of vineyard soils with high rock fragment content in the Mosel area, Germany

2021 ◽  
Author(s):  
Selina Walle ◽  
Thomas Iserloh ◽  
Manuel Seeger
Keyword(s):  
Hydraulic Conductivity ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Unsaturated Soils ◽  
Hydrological Cycle ◽  
Calculated Data ◽  
Rock Fragment ◽  
Unsaturated Hydraulic Conductivity ◽  
Significant Difference

<p>The study deals with the unsaturated hydraulic conductivity of soils within the scope of the Diverfarming-Project, funded by the EU commission (Horizon 2020 grant agreement no 728003). For this reason, the field work took place in the examined vineyard of the Wawerner Jesuitenberg near Kanzem in the Saar-Mosel valley (Rhineland-Palatinate, Germany). The mentioned parameter is one of the most important specific factors of the hydrological cycle to characterize soil hydraulic properties in the unsaturated soil zone. A mini disc infiltrometer was used to measure the conductivity values at different suctions. The purpose of this study is to determine the plausibility of the fundamentals and the analytical expression of the unsaturated conductivity models in a nearly skeletal soil of schist. In this regard, the mathematical expressions of Mualem (1976), van Genuchten (1980) and Zhang (1997) are focused on calculating the unsaturated hydraulic conductivity. The two variables α and n are analysed in order to better compare between literature specifications and the explicit calculated data of the vineyard’s soil. As a result, the various developments of α are similar thus the significant difference is based on the value of n. Nevertheless, in consideration of these frame conditions the models represent a suitable mathematical expression of the unsaturated hydraulic conductivity. Furthermore, a range of parameters affecting this conductivity is analysed, particularly with regard to the applied variable soil and cultivation management under the grapevines in the vineyard. Also, the rock fragment cover and the pore size distribution are taken into account. In this context the soil compaction and modified pore size distribution in the wheel tracks stand out due to salient unsaturated hydraulic conductivities at higher tensions. In particular, the stone cover of the contact surface influence the characteristics of the analysed conductivity. Additionally, the connection of stone cover, management and pore size distribution creates a mixture of affected parameters of the unsaturated hydraulic conductivity.</p><p> </p><p>Mualem, Y.: A new model for predicting the hydraulic conductivity of unsaturated porous media, Water Resour. Res, 12, 513–522, https://doi.org/10.1029/WR012i003p00513, 1976.</p><p>Van Genuchten, M.T.: A Closed-form Equation for Predicting the Hydraulic Conductivity of Unsaturated Soils, Soil Sci. Soc. Am. J., 44, 892–898, https://doi.org/10.2136/sssaj1980.03615995004400050002x, 1980.</p><p>Zhang, R.: Determination of Soil Sorptivity and Hydraulic Conductivity from the Disk Infiltrometer, Soil Sci. Soc. Am. J., 61, 1024–1030, https://doi.org/10.2136/sssaj1997.03615995006100040005x, 1997.</p>

Prediction of Hydraulic Conductivity as Related to Pore Size Distribution in Unsaturated Soils

Soil Science ◽  
2009 ◽  
Vol 174 (9) ◽  
pp. 508-515 ◽  
Author(s):  
Abdel-Monem M. Amer ◽  
Sally D. Logsdon ◽  
Dedrick Davis
Keyword(s):  
Hydraulic Conductivity ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Unsaturated Soils

pyGAPS: A Python-Based Framework for Adsorption Isotherm Processing and Material Characterisation

2019 ◽  
Author(s):  
Paul Iacomi ◽  
Philip L. Llewellyn
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Surface Area ◽  
Isosteric Heat ◽  
Material Characterisation ◽  
Mixture Adsorption ◽  
Surface Energetics ◽  
Adsorption Processing ◽  
User Friendly

Material characterisation through adsorption is a widely-used laboratory technique. The isotherms obtained through volumetric or gravimetric experiments impart insight through their features but can also be analysed to determine material characteristics such as specific surface area, pore size distribution, surface energetics, or used for predicting mixture adsorption. The pyGAPS (python General Adsorption Processing Suite) framework was developed to address the need for high-throughput processing of such adsorption data, independent of the origin, while also being capable of presenting individual results in a user-friendly manner. It contains many common characterisation methods such as: BET and Langmuir surface area, t and α plots, pore size distribution calculations (BJH, Dollimore-Heal, Horvath-Kawazoe, DFT/NLDFT kernel fitting), isosteric heat calculations, IAST calculations, isotherm modelling and more, as well as the ability to import and store data from Excel, CSV, JSON and sqlite databases. In this work, a description of the capabilities of pyGAPS is presented. The code is then be used in two case studies: a routine characterisation of a UiO-66(Zr) sample and in the processing of an adsorption dataset of a commercial carbon (Takeda 5A) for applications in gas separation.

Sign in / Sign up

Export Citation Format

Share Document