Estimating soil freezing characteristic curve based on pore-size distribution

2017 ◽  
Vol 124 ◽  
pp. 1049-1060 ◽  
Author(s):  
Chong Wang ◽  
Yuanming Lai ◽  
Mingyi Zhang
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Characteristic Curve ◽  
Soil Freezing

Equations for the soil-water characteristic curve

1994 ◽  
Vol 31 (4) ◽  
pp. 521-532 ◽  
Author(s):  
D.G. Fredlund ◽  
Anqing Xing
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Soil Water ◽  
Characteristic Curve ◽  
Nonlinear Least Squares ◽  
Soil Water Characteristic Curve ◽  
Nonlinear Curve Fitting ◽  
Soil Behaviour ◽  
Nonlinear Curve

The soil-water characteristic curve can be used to estimate various parameters used to describe unsaturated soil behaviour. A general equation for the soil-water characteristic curve is proposed. A nonlinear, least-squares computer program is used to determine the best-fit parameters for experimental data presented in the literature. The equation is based on the assumption that the shape of the soil-water characteristic curve is dependent upon the pore-size distribution of the soil (i.e., the desaturation is a function of the pore-size distribution). The equation has the form of an integrated frequency distribution curve. The equation provides a good fit for sand, silt, and clay soils over the entire suction range from 0 to 106 kPa. Key words : soil-water characteristic curve, pore-size distribution, nonlinear curve fitting, soil suction, water content.

scholarly journals A Fractal Model of Hydraulic Conductivity for Saturated Frozen Soil

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 369 ◽  
Author(s):  
Lei Chen ◽  
Dongqing Li ◽  
Feng Ming ◽  
Xiangyang Shi ◽  
Xin Chen
Keyword(s):  
Hydraulic Conductivity ◽  
Pore Structure ◽  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Fractal Theory ◽  
Frozen Soil ◽  
Soil Freezing ◽  
Size Dimension ◽  
Maximum Pore Size

In cold regions, hydraulic conductivity is a critical parameter for determining the water flow in frozen soil. Previous studies have shown that hydraulic conductivity hinges on the pore structure, which is often depicted as the pore size and porosity. However, these two parameters do not sufficiently represent the pore structure. To enhance the characterization ability of the pore structure, this study introduced fractal theory to investigate the influence of pore structure on hydraulic conductivity. In this study, the pores were conceptualized as a bundle of tortuous capillaries with different radii and the cumulative pore size distribution of the capillaries was considered to satisfy the fractal law. Using the Hagen-Poiseuille equation, a fractal capillary bundle model of hydraulic conductivity for saturated frozen soil was developed. The model validity was evaluated using experimental data and by comparison with previous models. The results showed that the model performed well for frozen soil. The model showed that hydraulic conductivity was related to the maximum pore size, pore size dimension, porosity and tortuosity. Of all these parameters, pore size played a key role in affecting hydraulic conductivity. The pore size dimension was found to decrease linearly with temperature, the maximum pore size decreased with temperature and the tortuosity increased with temperature. The model could be used to predict the hydraulic conductivity of frozen soil, revealing the mechanism of change in hydraulic conductivity with temperature. In addition, the pore size distribution was approximately estimated using the soil freezing curve, making this method could be an alternative to the mercury intrusion test, which has difficult maneuverability and high costs. Darcy’s law is valid in saturated frozen silt, clayed silt and clay, but may not be valid in saturated frozen sand and unsaturated frozen soil.

scholarly journals A simple approach for predicting soil water characteristic curve of clayey soils using pore size distribution data

2021 ◽  
Vol 337 ◽  
pp. 02012
Author(s):  
Wei Yan ◽  
Emanuel Birle ◽  
Roberto Cudmani
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Soil Water ◽  
Characteristic Curve ◽  
Simple Approach ◽  
Distribution Data ◽  
Clayey Soils ◽  
Soil Water Characteristic Curve ◽  
Good Consistency

The soil water characteristic curve (SWCC) of soils can be derived from the measured pore size distribution (PSD) data by applying capillary models. This method is limited for clayey soils due to the PSD changes during SWCC testing. In this study, a suction-dependent multimodal PSD model based on probability theory is developed and used to derive SWCC. The model is validated by simulating the drying branches of SWCCs of four compacted Lias Clay samples with different initial states. A good consistency between the measured and predicted SWCC is shown.

Estimating the water characteristic curve for soil containing residual plastic film based on an improved pore-size distribution

Geoderma ◽  
2020 ◽  
Vol 370 ◽  
pp. 114341 ◽  
Author(s):  
Zhichao Wang ◽  
Xianyue Li ◽  
Haibin Shi ◽  
Weiping Li ◽  
Wenhuan Yang ◽  
...  
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Characteristic Curve ◽  
Plastic Film

scholarly journals EFFECT OF SAMPLE RE-SATURATION ON SOIL-WATER CHARACTERISTIC CURVE

2018 ◽  
Vol 31 (2) ◽  
pp. 446-454 ◽  
Author(s):  
ÍCARO VASCONCELOS DO NASCIMENTO ◽  
THIAGO LEITE DE ALENCAR ◽  
CARLOS LEVI ANASTÁCIO DOS SANTOS ◽  
RAIMUNDO NONATO DE ASSIS JÚNIOR ◽  
JAEDSON CLÁUDIO ANUNCIATO MOTA
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Soil Water ◽  
Characteristic Curve ◽  
Soil Samples ◽  
Similar Process ◽  
Undisturbed Soil ◽  
Saturation Process ◽  
Soil Water Characteristic Curve

ABSTRACT Soil-water characteristic curve (SWCC) is an important tool for water management in irrigated agriculture. However, factors such as texture and structure of soils influence SWCC behavior. According to the literature, wetting and drying cycles alter SWCC. A similar process of re-saturation and drying occurs during SWCC obtainment under laboratory conditions. Based on the hypothesis that re-saturation process alters SWCC due to clay loss in the sample, this study aimed to obtain the SWCC, S index, and pore size distribution from samples submitted to re-saturation cycles, as well as from not re-saturated samples but under higher matric potentials (-2, -4, -6, -8, and -10 kPa). For this, disturbed and undisturbed soil samples, collected from the A (sandy texture) and Btg (sandy clay loam texture) horizons of a Argissolo Acizentado, were used. After obtaining SWCC, each air-dried soil sample was submitted to particle size and clay dispersed in water analyses to verify whether the soil lost clay. The experimental design was a completely randomized design with two methods of SWCC constructing (with and without re-saturation) and eight replications. The re-saturation process generates a loss of clay in the sample, not causing significant changes in SWCC considering the assessed textural soil classes. In addition, sandy soil samples are more sensitive to changes in pore size distribution when submitted to re-saturation.

Measurement and estimation of pore shrinkage and pore distribution in a clayey till during soil-water characteristic curve tests

2001 ◽  
Vol 38 (4) ◽  
pp. 741-754 ◽  
Author(s):  
Paul H Simms ◽  
Ernest K Yanful
Keyword(s):  
Pore Size Distribution ◽  
Pore Size ◽  
Size Distribution ◽  
Soil Water ◽  
Mine Waste ◽  
Characteristic Curve ◽  
Landfill Liners ◽  
Soil Water Characteristic Curve ◽  
Fine Grained ◽  
In Series

The soil-water characteristic curve (SWCC) of fine-grained soils is usually determined experimentally. In many applications, such as design of mine waste covers and landfill liners, the unsaturated permeability function, k(h), is often derived theoretically from the measured SWCC. Implicit in these derivations is the transformation of the SWCC to a pore-size distribution (PSD), typically assumed to be constant and mono-modal. PSDs of a clayey till compacted at various water contents were measured after compaction, after flexible-wall permeability testing, and during and after SWCC tests. The measurements show that the PSD changes significantly during permeability and SWCC testing. A method is advanced for predicting the observed changes in PSD during SWCC testing. PSDs are determined for soil samples subjected to the highest and lowest suctions applied during the SWCC test. The measured PSDs are transformed to account for pore trapping; the transform assumes that flow occurs through two sets of randomly distributed pores in series. To model pore shrinkage, the pores are idealized as elastic cylinders. PSDs measured after different suction applications in the SWCC tests are compared with predictions of the shrinkage model. The method can also be used to predict the SWCC. Measured and predicted values are compared.Key words: landfill liners, mine waste covers, soil-water characteristic curve, pore-size distribution.

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.

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