scholarly journals Reconfiguration Strategies with Composite Data Physicalizations

2021 ◽  
Author(s):  
Kim Sauvé ◽  
David Verweij ◽  
Jason Alexander ◽  
Steven Houben
Keyword(s):  
Composite Data

VISCOSITY OF NATURAL RUBBER SOLUTIONS AT VERY LOW RATES OF SHEAR

1957 ◽  
Vol 35 (4) ◽  
pp. 381-387 ◽  
Author(s):  
Morton A. Golub
Keyword(s):  
Experimental Data ◽  
Natural Rubber ◽  
Pressure Head ◽  
Viscosity Data ◽  
Theoretical Relation ◽  
Newtonian Flow ◽  
Shear Rates ◽  
Reduced Viscosity ◽  
Wide Range ◽  
Composite Data

The shear dependence of viscosity of benzene solutions of natural rubber was studied at rates of shear from about 500 down to less than 1 sec.−1. Measurements involved following the change of pressure head with time of the various solutions flowing in a capillary, U-tube viscometer. Curvature in the plots of the logarithm of pressure head versus time indicated non-Newtonian flow. From such curves, reduced viscosity data over the above-mentioned shear range were readily derived. As a check, data over the range 100–500 sec.−1 were also obtained with a five-bulb viscometer of the Krigbaum–Flory type, and these data overlapped those obtained with the U tube. The reduced viscosity increased very sharply with decrease in gradient, making extrapolation to the viscosity axis quite unreliable. However, a theoretical relation proposed by Bueche fitted the composite data rather well. This work furnished a nice technique for determining the zero shear reduced viscosity (ηap/c)0 without the necessity of performing an uncertain extrapolation: evaluate the parameters of the Bueche formula which best satisfies the experimental data over a fairly wide range of shear rates, and then calculate (ηap/c)0 directly.

Commissioning and dosimetric characteristics of TrueBeam system: Composite data of three TrueBeam machines

2012 ◽  
Vol 39 (11) ◽  
pp. 6981-7018 ◽  
Author(s):  
Zheng Chang ◽  
Qiuwen Wu ◽  
Justus Adamson ◽  
Lei Ren ◽  
James Bowsher ◽  
...  
Keyword(s):  
Composite Data

Aerosol Optical Depth for Atmospheric Correction of AVHRR Composite Data

2000 ◽  
Vol 26 (4) ◽  
pp. 273-284 ◽  
Author(s):  
G. Fedosejevs ◽  
N.T. O'Neill ◽  
A. Royer ◽  
P.M. Teillet ◽  
A.I. Bokoye ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Atmospheric Correction ◽  
Composite Data

Composite Data Types

1993 ◽  
pp. 245-263
Author(s):  
André Heck
Keyword(s):  
Data Types ◽  
Composite Data

Composite Data Types

1996 ◽  
pp. 293-323
Author(s):  
André Heck
Keyword(s):  
Data Types ◽  
Composite Data

scholarly journals Scale Characterization and Correction of Diurnal Cycle Errors in MAPLE

2017 ◽  
Vol 56 (9) ◽  
pp. 2561-2575 ◽  
Author(s):  
Aitor Atencia ◽  
Isztar Zawadzki ◽  
Marc Berenguer
Keyword(s):  
Diurnal Cycle ◽  
Spatial Scales ◽  
Time Of Day ◽  
Precipitation Forecast ◽  
Systematic Bias ◽  
Correction Scheme ◽  
Average Rainfall ◽  
The Mean ◽  
Scale Characterization ◽  
Composite Data

AbstractThe most widely used technique for nowcasting of quantitative precipitation in operational and research centers is the Lagrangian extrapolation of the latest radar observations. However, this technique has a limited forecast skill because of the assumption made on its formulation, such as the fact that the motion vectors do not change and, even more important for convective events, neglect any growth or decay in the precipitation field. In this work, the McGill Algorithm for Precipitation Nowcasting by Lagrangian Extrapolation (MAPLE) errors have been computed for 10 yr of radar composite data over the continental United States. The study of these errors shows systematic bias depending on the time of day. This effect is related to the solar cycle, whose heating energy results in an increase in the average rainfall in the afternoon. This external forcing interacts with the atmospheric system, creating local initiation and dissipation of convection depending on orography, land use, cloud coverage, etc. The signal of the diurnal cycle in MAPLE precipitation forecast has been studied in different locations and spatial scales as a function of lead time in order to recognize where, when, and for which spatial scales the signal is significant. This information has been used in the development of a scaling correction scheme where the mean errors due to the diurnal cycle are adjusted. The results show that the developed methodology improves the forecast for the spatial scales and locations where the diurnal cycle signal is significant.

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