scholarly journals Estimation of Permeability Coefficient Using Fractal Dimension of Particle Size Distribution Curve in Granular Soils

2006 ◽  
Vol 48 (4) ◽  
pp. 41-49
Author(s):  
Jae-Seong Park ◽  
Pyoung-Wuck Chang ◽  
Young-Hwan Son ◽  
Seong-Pil Kim
Keyword(s):  
Particle Size ◽  
Fractal Dimension ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Permeability Coefficient ◽  
Distribution Curve ◽  
Granular Soils ◽  
Particle Size Distribution Curve

scholarly journals The apparent density as a function of the specific surface of copper powder and the shape of the particle size distribution curve

2003 ◽  
Vol 68 (11) ◽  
pp. 903-907 ◽  
Author(s):  
Konstantin Popov ◽  
Predrag Zivkovic ◽  
Snezana Krstic
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Specific Surface ◽  
Size Distribution ◽  
Apparent Density ◽  
Copper Powder ◽  
Distribution Curve ◽  
Particle Size Distribution Curve ◽  
Sulfate Solutions ◽  
Copper Powders

The relation between the specific surface and apparent density of copper powders electrodeposited from acid copper sulfate solutions is established. It is shown that the apparent density is inversely proportional to the specific surface of copper powder. The shape of the particle size distribution curve is also discussed.

Effect of Vibro-Milling Time on Phase Formation and Particle Size of ZnNbO6 Nano-Powders

2009 ◽  
Vol 421-422 ◽  
pp. 550-553
Author(s):  
Athipong Ngamjarurojana ◽  
Rattikorn Yimnirun ◽  
Supon Ananta
Keyword(s):  
Crystal Structure ◽  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Phase Formation ◽  
Milling Time ◽  
Distribution Curve ◽  
Laser Diffraction ◽  
Narrow Particle Size Distribution ◽  
Particle Size Distribution Curve

Zinc niobate, ZnNb2O6, nanopowders was synthesized by a solid-state reaction via a rapid vibro-milling technique. The effect of milling time on the phase formation and particle size of ZnNb2O6 powder was investigated. The formation of the ZnNb2O6 phase investigated as a function of calcination conditions by DTA and XRD. The particle size distribution of the calcined powders was determined by laser diffraction technique, while morphology, crystal structure and phase composition were determined via a SEM techniques. In addition, by employing an appropriate choice of milling time, a narrow particle size distribution curve was also observed.

Prediction of CEC Using Fractal Parameters by Artificial Neural Networks

2014 ◽  
Vol 28 (2) ◽  
pp. 143-152 ◽  
Author(s):  
Hossein Bayat ◽  
Naser Davatgar ◽  
Mohsen Jalali
Keyword(s):  
Particle Size ◽  
Particle Size Distribution ◽  
Size Distribution ◽  
Distribution Curve ◽  
Exchange Capacity ◽  
Pedotransfer Functions ◽  
Model Parameters ◽  
Particle Size Distribution Curve ◽  
Fractal Parameters ◽  
Curve Model

Abstract The prediction of cation exchange capacity from readily available soil properties remains a challenge. In this study, firstly, we extended the entire particle size distribution curve from limited soil texture data and, at the second step, calculated the fractal parameters from the particle size distribution curve. Three pedotransfer functions were developed based on soil properties, parameters of particle size distribution curve model and fractal parameters of particle size distribution curve fractal model using the artificial neural networks technique. 1 662 soil samples were collected and separated into eight groups. Particle size distribution curve model parameters were estimated from limited soil texture data by the Skaggs method and fractal parameters were calculated by Bird model. Using particle size distribution curve model parameters and fractal parameters in the pedotransfer functions resulted in improvements of cation exchange capacity predictions. The pedotransfer functions that used fractal parameters as predictors performed better than the those which used particle size distribution curve model parameters. This can be related to the non-linear relationship between cation exchange capacity and fractal parameters. Partitioning the soil samples significantly increased the accuracy and reliability of the pedotransfer functions. Substantial improvement was achieved by utilising fractal parameters in the clusters.

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