scholarly journals On the influence of the spatial distribution of fine content in the hydraulic conductivity of sand-clay mixtures

2018 ◽  
Vol 22 (4) ◽  
pp. 239-249 ◽  
Author(s):  
William Mario Fuentes ◽  
Carolina Hurtado ◽  
Carlos Lascarro
Keyword(s):  
Spatial Distribution ◽  
Hydraulic Conductivity ◽  
Numerical Model ◽  
Soil Sample ◽  
Gaussian Distribution ◽  
Geotechnical Engineering ◽  
Empirical Relation ◽  
Fine Content ◽  
The Mean

Sand-clay mixtures are one of the most usual types of soils in geotechnical engineering. These soils present a hydraulic conductivity which highly depends on the fine content. In this work, it will be shown, that not only the mean fine content of a soil sample affects its hydraulic conductivity, but also its spatial distribution within the sample. For this purpose, a set of hydraulic conductivity tests with sand-clay mixtures have been conducted to propose an empirical relation of the hydraulic conductivity depending on the fine content. Then, a numerical model of a large scaled hydraulic conductivity test is constructed. In this model, the heterogeneity of the fine content is simulated following a Gaussian distribution. The equivalent hydraulic conductivity resulting of the whole model is then computed and the influence of the spatial distribution of the fine content is evaluated. The results indicate that the hydraulic conductivity is not only related to the mean fine content, but also on its heterogeneity.

scholarly journals Spatial variability of saturated hydraulic conductivity and its links with other soil properties at the regional scale

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Boguslaw Usowicz ◽  
Jerzy Lipiec
Keyword(s):  
Spatial Distribution ◽  
Hydraulic Conductivity ◽  
Organic Carbon ◽  
Spatial Variability ◽  
Soil Properties ◽  
Regional Scale ◽  
Total Porosity ◽  
The Other ◽  
Study Region ◽  
The Mean

AbstractSaturated hydraulic conductivity (K) is a key property for evaluating soil water movement and quality. Most studies on spatial variability of K have been performed soil at a field or smaller scale. Therefore, the aim of this work was to assess (quantify) the spatial distribution of K at the larger regional scale in south-eastern Poland and its relationship with other soil properties, including intrinsic sand, silt, and clay contents, relatively stable organic carbon, cation exchange capacity (CEC) and temporally variable water content (WC), total porosity (FI), and dry bulk density (BD) in the surface layer (0–20 cm). The spatial relationships were assessed using a semivariogram and a cross-semivariogram. The studied region (140 km2) with predominantly permeable sandy soils with low fertility and productivity is located in the south-eastern part of Poland (Podlasie region). The mean sand and organic carbon contents are 74 and 0.86 and their ranges (in %) are 45–95 and 0.002–3.75, respectively. The number of individual samples varied from 216 to 228 (for K, WC, BD, FI) to 691 for the other soil properties. The best fitting models were adjusted to the empirical semivariogram (exponential) and the cross-semivariogram (exponential, Gaussian, or linear) used to draw maps with kriging. The results showed that, among the soil properties studied, K was most variable (coefficient of variation 77.3%) and significantly (p < 0.05) positively correlated with total porosity (r = 0.300) and negatively correlated with soil bulk density (r = – 0.283). The normal or close to the normal distribution was obtained by natural logarithmic and root square transformations. The mean K was 2.597 m day−1 and ranged from 0.01 up to 11.54 m day−1. The spatial autocorrelation (range) of K in the single (direct) semivariograms was 0.081° (8.1 km), while it favourably increased up to 0.149°–0.81° (14.9–81 km) in the cross-semivariograms using the OC contents, textural fractions, and CEC as auxiliary variables. The generated spatial maps allowed outlining two sub-areas with predominantly high K above 3.0 m day−1 in the northern sandier (sand content > 74%) and less silty (silt content < 22%) part and, with lower K in the southern part of the study region. Generally, the spatial distribution of the K values in the study region depended on the share of individual intrinsic textural fractions. On the other hand, the ranges of the spatial relationship between K and the intrinsic and relatively stable soil properties were much larger (from ~ 15 to 81 km) than between K and the temporally variable soil properties (0.3–0.9 km). This knowledge is supportive for making decisions related to land management aimed at alteration of hydraulic conductivity to improve soil water resources and crop productivity and reduce chemical leaching.

scholarly journals Spatial Variability of Saturated Hydraulic Conductivity and its Links with other Soil Properties at the Commune Scale

2021 ◽  
Author(s):  
Boguslaw Usowicz ◽  
Jerzy Lipiec
Keyword(s):  
Spatial Distribution ◽  
Hydraulic Conductivity ◽  
Organic Carbon ◽  
Spatial Variability ◽  
Soil Properties ◽  
Bulk Density ◽  
Soil Water ◽  
Total Porosity ◽  
The Other ◽  
The Mean

Abstract Saturated hydraulic conductivity (SHC) is a key property for evaluating soil water movement and quality. Most studies on spatial variability of SHC have been performed soil at a field or smaller scale. Therefore, the aim of this work was to assess (quantify) the spatial distribution of SHC at the commune scale and its relationship with other soil properties, including intrinsic sand, silt, and clay contents, relatively stable organic carbon, cation exchange capacity (CEC), dynamic water content (WC), total porosity (FI), and dry bulk density (BD) in the surface layer (0–20 cm). The spatial relationships were assessed using a semivariogram and a cross-semivariogram. The studied commune (140 km2) with predominantly permeable sandy soils with low fertility and productivity is located in the south-eastern part of Poland (Podlasie region). The mean sand and organic carbon contents are 74 andobablyctknąć, czy o to chodzid mniej znacznie mniejszed? ? 0.86 and their ranges (in %) are 45-95 and 0.002-3.75, respectively. The number of individual samples varied from 216–228 (for SHC, WC, BD, FI) to 691 for the other soil properties. The best fitting models were adjusted to the empirical semivariogram (exponential) and the cross-semivariogram (exponential, Gaussian, or linear) used to draw maps with kriging. The results showed that, among the soil properties studied, SHC was most variable (coefficient of variation 77.3%) and significantly (p <0.05) positively correlated with total porosity (r = 0.300) and negatively correlated with soil bulk density (r = –0.283). The mean SHC was 2.597 m day–1 and ranged from 0.01 up to 11.54 m day–1. The spatial autocorrelation (range) of SHC in the single (direct) semivariograms was 0.081° (8.1 km), while it favourably increased up to 0.149–0.81° (14.9–81 km) in the cross-semivariograms using the OC contents, textural fractions, and CEC as auxiliary variables. The generated spatial maps allowed outlining two sub-areas with predominantly high SHC above 3.0 m day–1 in the northern sandier (sand content >74%) and less silty (silt content <22%) part and, with lower SHC in the southern part of the commune. Generally, the spatial distribution of the SHC values in the commune area depended on the share of individual intrinsic textural fractions. On the other hand, the ranges of the spatial relationship between SHC and the intrinsic and relatively stable soil properties were much larger (from ~15 to 81 km) than between SHC and the dynamic soil properties (0.3-0.9 km). This knowledge is supportive for making decisions related to land management aimed at reduction of hydraulic conductivity and chemical leaching and improvement of soil water resources and crop productivity.

Limit tolerances for sums of measurement errors

2018 ◽  
Vol 934 (4) ◽  
pp. 59-62
Author(s):  
V.I. Salnikov
Keyword(s):  
Normal Distribution ◽  
Mean Square Error ◽  
Gaussian Distribution ◽  
Measurement Errors ◽  
Theory And Practice ◽  
Mean Square ◽  
The Mean ◽  
Limiting Values ◽  
Limiting Value

The question of calculating the limiting values of residuals in geodesic constructions is considered in the case when the limiting value for measurement errors is assumed equal to 3m, ie ∆рred = 3m, where m is the mean square error of the measurement. Larger errors are rejected. At present, the limiting value for the residual is calculated by the formula 3m√n, where n is the number of measurements. The article draws attention to two contradictions between theory and practice arising from the use of this formula. First, the formula is derived from the classical law of the normal Gaussian distribution, and it is applied to the truncated law of the normal distribution. And, secondly, as shown in [1], when ∆рred = 2m, the sums of errors naturally take the value equal to ?pred, after which the number of errors in the sum starts anew. This article establishes its validity for ∆рred = 3m. A table of comparative values of the tolerances valid and recommended for more stringent ones is given. The article gives a graph of applied and recommended tolerances for ∆рred = 3m.

Experiments on the flow of heat in liquid helium below 0.7 °K

1958 ◽  
Vol 246 (1246) ◽  
pp. 390-405 ◽  
Keyword(s):  
Thermal Conductivity ◽  
Liquid Helium ◽  
Free Path ◽  
Empirical Relation ◽  
Mean Free Path ◽  
Series Of Experiments ◽  
The Mean ◽  
Set Up ◽  
Low Pressures ◽  
Measured Thermal Conductivity

A series of experiments has been performed to study the steady flow of heat in liquid helium in tubes of diameter 0.05 to 1.0 cm at temperatures between 0.25 and 0.7 °K. The results are interpreted in terms of the flow of a gas of phonons, in which the mean free path λ varies with temperature, and may be either greater or less than the diameter of the tube d . When λ ≫ d the flow is limited by the scattering of the phonons at the walls, and the effect of the surface has been studied, but when λ ≪ d viscous flow is set up in which the measured thermal conductivity is increased above that for wall scattering. This behaviour is very similar to that observed in the flow of gases at low pressures, and by applying kinetic theory to the problem it can be shown that the mean free path of the phonons characterizing viscosity can be expressed by the empirical relation λ = 3.8 x 10 -3 T -4.3 cm. This result is inconsistent with the temperature dependence of λ as T -9 predicted theoretically by Landau & Khalatnikov (1949).

Evaluation of Single-Hole Hydraulic Tests in Fractured Crystalline Rock by Steady-State and Transient Methods: A Comparison

1985 ◽  
Vol 50 ◽  
Author(s):  
J-E. Andersson ◽  
O. Persson
Keyword(s):  
Steady State ◽  
Hydraulic Conductivity ◽  
Flow Pattern ◽  
Transient Analysis ◽  
Crystalline Rock ◽  
Steady State Analysis ◽  
Single Hole ◽  
The Mean ◽  
Transient Methods ◽  
State Analysis

AbstractThe results from a large number of single-hole packer tests in crystalline rock from three test sites in Sweden have been analysed statistically. Average hydraulic conductivity values for 25 m long test intervals along boreholes with a maximal length of about 700 m are used in this study. A comparison between steady state and transient analysis of the same test data has been performed.The mean vaule of the hydraulic conductivity determined from steady state analysis was found to be about two to three times higher compared to transient analysis. However, in some cases the steady state analysis resulted in 10 to 20 times higher values compared to the transient analysis. Such divergence between the two analysis methods may be caused by deviations from the assumed flow pattern, borehole skin effects and influence of hydraulic boundaries.

scholarly journals RHYTHMICITY IN THE PROTOPLASMIC STREAMING OF A SLIME MOLD, PHYSARUM POLYCEPHALUM

1958 ◽  
Vol 41 (6) ◽  
pp. 1205-1222 ◽  
Author(s):  
Uichiro Kishimoto
Keyword(s):  
Gaussian Distribution ◽  
Electric Potential ◽  
Distribution Curve ◽  
Slime Mold ◽  
Protoplasmic Streaming ◽  
Arbitrary Phase ◽  
Wave Forms ◽  
The Mean ◽  
Phase Angles ◽  
Constant Period

The electric potential difference (1 to 15 mv.) between two loci of the slime mold connected with a strand of protoplasm changes rhythmically with the same period (60 to 180 seconds) as that of the back and forth protoplasmic streaming along the strand. Generally some phase difference is observed between them. Periods of the electric potential rhythm show a Gaussian distribution. Amplitudes give a somewhat different distribution curve. Wave forms are not always simple harmonic ones, but are distorted more or less. However, auto-correlation analysis proves that there is a dominant rhythm of a nearly constant period which coincides with the mean period of the Gaussian distribution curve. Calculations made on an assumption that the electric potential rhythm is the result of many elementary rhythms (i.e., same periodicity, arbitrary phase angles) distributed throughout the plasmodium, give a satisfactory coincidence with the observed distribution for the amplitude. The predominance of a rhythm of a nearly constant periodicity suggests the existence of well organized interactions among components of a contractile protein network, the rhythmic deformation of which is supposed to be responsible for the protoplasmic streaming and for the electric potential rhythm.

scholarly journals Elasto-plastic-adhesive DEM model for simulating hillslope debris flows: cross comparison with field experiments

2018 ◽  
Author(s):  
Adel Albaba ◽  
Massimiliano Schwarz ◽  
Corinna Wendeler ◽  
Bernard Loup ◽  
Luuk Dorren
Keyword(s):  
Numerical Model ◽  
Flow Velocity ◽  
Debris Flows ◽  
Field Experiments ◽  
Initial Conditions ◽  
Experimental Tests ◽  
Height Velocity ◽  
Model Parameters ◽  
Fine Content ◽  
Adhesive Model

Abstract. This paper presents a Discrete Element-based elasto-plastic-adhesive model which is adapted and tested for producing hillslope debris flows. The numerical model produces three phases of particle contacts: elastic, plastic and adhesion. The model capabilities of simulating different types of cohesive granular flows were tested with different ranges of flow velocities and heights. The basic model parameters, being the basal friction (&amp;varphi;b) and normal restitution coefficient (&amp;varepsilon;n), were calibrated using field experiments of hillslope debris flows impacting two sensors. Simulations of 50 m3 of material were carried out on a channelized surface that is 41 m long and 8 m wide. The calibration process was based on measurements of flow height, flow velocity and the pressure applied to a sensor. Results of the numerical model matched well those of the field data in terms of pressure and flow velocity while less agreement was observed for flow height. Those discrepancies in results were due in part to the deposition of material in the field test which are not reproducible in the model. A parametric study was conducted to further investigate that effect of model parameters and inclination angle on flow height, velocity and pressure. Results of best-fit model parameters against selected experimental tests suggested that a link might exist between the model parameters &amp;varphi;b and &amp;varepsilon;n and the initial conditions of the tested granular material (bulk density and water and fine contents). The good performance of the model against the full-scale field experiments encourages further investigation by conducting lab-scale experiments with detailed variation of water and fine content to better understand their link to the model's parameters.

scholarly journals Finding the pattern: online extraction of spatial structure during virtual navigation

2019 ◽  
Author(s):  
Kathryn Nicole Graves ◽  
James Antony ◽  
Nicholas Turk-Browne
Keyword(s):  
Spatial Distribution ◽  
Animal Behavior ◽  
Virtual Water ◽  
Single Session ◽  
Statistical Knowledge ◽  
Navigation Patterns ◽  
The Mean ◽  
Online Extraction ◽  
Human Participants ◽  
The Individual

While navigating the world, we pick up on patterns of where things tend to appear. According to theories of memory and studies of animal behavior, knowledge of these patterns emerges gradually over days or weeks, via consolidation of individual navigation episodes. Here we discover that navigation patterns can also be extracted online, prior to the opportunity for offline consolidation, as a result of rapid statistical learning. Human participants navigated a virtual water maze in which platform locations were drawn from a spatial distribution. Within a single session, participants increasingly navigated through the mean of the distribution. This behavior was better simulated by random walks from a model with only an explicit representation of the current mean, compared to a model with only memory for the individual platform locations. These results suggest that participants rapidly summarized the underlying spatial distribution and used this statistical knowledge to guide future navigation.

Chemical Concentration and Spatial Uniformity of a Premixing In-Line Injection System Attached to a Variable-Rate Orchard Sprayer

2021 ◽  
Vol 64 (6) ◽  
pp. 1977-1987
Author(s):  
Zhihong Zhang ◽  
Heping Zhu ◽  
Zhiming Wei ◽  
Ramon Salcedo
Keyword(s):  
Spatial Distribution ◽  
Duty Cycle ◽  
Spray Deposition ◽  
Injection System ◽  
List Type ◽  
Variable Rate ◽  
Chemical Concentration ◽  
Fluorescent Tracer ◽  
Distribution Uniformity ◽  
The Mean

HighlightsA newly developed premixing in-line injection system attached to a variable-rate orchard sprayer was evaluated.Tests were conducted to verify the in-line injection system performance using a vertical spray patternator.Concentration accuracy and spatial distribution uniformity were determined with a fluorescent tracer.Uniform spray mixtures were obtained for different spray viscosities and duty cycle combinations.Abstract. Pesticide spray application efficiency is highly dependent on the chemical concentration accuracy and spatial distribution uniformity. In this study, the performance of a newly developed premixing in-line injection system was evaluated when it was attached to a laser-guided, pulse width modulated (PWM), variable-rate orchard sprayer. The chemical concentration accuracy was determined with respect to spray deposition with a fluorescent tracer, and the spatial distribution uniformity was determined with spray deposits at different heights on a vertical spray patternator. Outdoor tests were conducted with 27 combinations of target chemical concentration (1.0%, 1.5%, and 2.0%), viscosity of the simulated pesticide (1.0, 12.0, and 24.0 mPa·s), and various spray outputs manipulated with PWM duty cycles. For each injection loop, the amounts of the chemical concentrate and water discharged into the mixing line were measured separately in response to preset target concentrations. The results showed that the measured concentrations were consistent across the patternator heights, spray viscosities, and duty cycle combinations. For all treatments, the mean absolute percentage error (MAPE) of the measured concentration was 6.96%, indicating that the concentration accuracy of the system was acceptable. The mean coefficient of variation was 3.35%, indicating that the spatial distribution uniformity of the system was in the desirable range. In addition, there was little variation in chemical concentration for spray mixtures collected at different heights on the patternator. Thus, the premixing in-line injection system could adequately dispense chemical concentrate and water to produce accurate concentrations and uniform spray mixtures for variable-rate nozzles to discharge to targets. Keywords: Environment protection, Precision pesticide application, Laser-guided sprayer, Tank mixture disposal, Specialty crop.

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