Simulation of the supercooling process and frazil evolution in turbulent flows

2004 ◽  
Vol 31 (6) ◽  
pp. 915-926 ◽  
Author(s):  
Shi Qiang Ye ◽  
Jay Doering
Keyword(s):  
Turbulent Flows ◽  
Heat Balance ◽  
Laboratory Experiments ◽  
Distribution Model ◽  
Secondary Nucleation ◽  
Frazil Ice ◽  
Rotating Flume ◽  
Flow Turbulence ◽  
Log Normal ◽  
Log Normal Distribution

A model to simulate the supercooling process and frazil ice evolution in a counter-rotating flume is developed based on a series of laboratory experiments. The characteristics of the supercooling process were found to be related to air temperature and flow turbulence. Frazil ice growth was observed to follow a log-normal distribution model. The model avoids the need to simulate seeding, secondary nucleation, flocculation–breakup, and gravitational removal. Only the overall heat balance is considered during the entire process. The simulations show good agreements with experimental time–temperature curves and frazil evolution.Key words: supercooling, frazil ice, size distribution, concentration, turbulence, simulation.

A Log-Normal Distribution Model for the Molecular Weight of Aquatic Fulvic Acids

2000 ◽  
Vol 34 (6) ◽  
pp. 1103-1109 ◽  
Author(s):  
Stephen E. Cabaniss ◽  
Qunhui Zhou ◽  
Patricia A. Maurice ◽  
Yu-Ping Chin ◽  
George R. Aiken
Keyword(s):  
Molecular Weight ◽  
Normal Distribution ◽  
Fulvic Acids ◽  
Distribution Model ◽  
Log Normal ◽  
Log Normal Distribution ◽  
Normal Distribution Model

scholarly journals The Statistical Distributions of PM2.5 in Rayong and Chonburi Provinces, Thailand

2020 ◽  
Vol 8 (3) ◽  
Author(s):  
Vanida Pongsakchat ◽  
Pattaraporn Kidpholjaroen
Keyword(s):  
Fine Particulate Matter ◽  
Statistical Distribution ◽  
Distribution Model ◽  
Statistical Distributions ◽  
Average Data ◽  
Fine Particulate ◽  
Pearson Type ◽  
Anderson Darling ◽  
Log Normal ◽  
Log Normal Distribution

The fine particulate matter (PM2.5) concentrations is one of the most important issues that are often discussed since it has a greater impact on human health. Statistical distribution modeling plays an important role in predicting PM2.5 concentrations. This research aims to find the optimum statistical distribution model of PM2.5 in Rayong Province and Chonburi Province. The daily average data from 2014 – 2019 for Rayong and from 2015 – 2019 for Chonburi were using. Five statistical distributions were compared. A proper statistical distribution that represents PM2.5 concentrations has been chosen based on three criteria include Anderson-Darling statistic and RMSE. The results show that Pearson type VI distribution performs better compared to other distributions for PM2.5 concentrations in Rayong. For Chonburi, the proper statistical distribution is Log normal distribution.  

Comprehensive Source Identification and Apportionment Analysis of five Heavy Metals in Soils in Wenzhou City, China

2020 ◽  
Author(s):  
Shuai Shao ◽  
Bifeng Hu ◽  
Yin Zhou ◽  
Zhou Shi
Keyword(s):  
Heavy Metals ◽  
Normal Distribution ◽  
Source Identification ◽  
Mixture Distribution ◽  
Anthropogenic Sources ◽  
Distribution Model ◽  
Auxiliary Variables ◽  
Industrial Emissions ◽  
Log Normal ◽  
Log Normal Distribution

<p>Source identification and apportionment of heavy metals (HMs) has been a vital issue of soil contamination restoration. In this study, qualitive approaches (Finite mixture distribution model (FMDM) and raster based principal components analysis (RB-PCA)) as well as quantitative approach (positive matrix factorization (PMF)) were composed to identify and apportion sources of five HMs (Cd, Hg, As, Pb, Cr) with the help of several crucial auxiliary variables in Wenzhou City, China. The result of FMDM showed Cd, and Pb fitted for single log-normal distribution, while Hg fitted for double log-normal mixed distribution, and As, Cr presented triple log-normal distribution. Each element was identified and separated from natural or anthropogenic sources. An improved score interpolation map of PCA attached with corresponded auxiliary variables analysis suggested three main contribution sources including parental materials, mines exploiting and industrial emissions contributes most in the whole study area. Each element was further discussed for quantitative contributions through PMF model. Parental materials contributed to all elements (Cd, Hg, As, Pb, Cr) as 89.22%, 84.81%, 7.31%, 35.84%, 27.42%. Industrial emissions had a contribution as 2.94%, 80.77%, 15.93%, 4.79%, 25.63% for each element respectively. While Mine exploiting mixed with fertilizers inputs has dedicated for such five HMs as 7.84%,11.92%, 48.23%, 10.40% and 46.95%. Such results could efficiently be devoted to scientific decisions and strategies making regarding HMs pollution regulation in soils.</p>

Development of a mathematical model of frazil ice evolution based on laboratory tests using a counter-rotating flume

2007 ◽  
Vol 34 (2) ◽  
pp. 210-218 ◽  
Author(s):  
Shuang Ming Wang ◽  
J C Doering
Keyword(s):  
Mathematical Model ◽  
Numerical Models ◽  
Frazil Ice ◽  
Thermal Growth ◽  
Mean Size ◽  
Rotating Flume ◽  
The Mean ◽  
Flow Turbulence ◽  
Turbulence Parameters ◽  
Good Agreement

A mathematical model to simulate the supercooling process and frazil ice evolution in a counter-rotating flume was developed. It considers the effect of frazil ice thermal growth while neglecting several complicated physical processes such as secondary nucleation, flocculation, and (or) break up. The supercooling process, vertical distribution of flow turbulence parameters, and frazil ice number concentration were simulated, as well the variation of the mean size of frazil ice during the supercooling process was modeled. The simulation results from this model show good agreement with experimental data.Key words: frazil ice, turbulence, numerical models, counter-rotating flume, supercooling.

scholarly journals Estimation on Reliability Models of Bearing Failure Data

2018 ◽  
Vol 2018 ◽  
pp. 1-21 ◽  
Author(s):  
Xia Xintao ◽  
Chang Zhen ◽  
Zhang Lijun ◽  
Yang Xiaowei
Keyword(s):  
Probability Distribution ◽  
Weibull Distribution ◽  
Normal Distribution ◽  
Distribution Model ◽  
Bearing Failure ◽  
Analysis Model ◽  
Failure Data ◽  
Log Normal ◽  
Log Normal Distribution ◽  
Two Parameter

The failure data of bearing products is random and discrete and shows evident uncertainty. Is it accurate and reliable to use Weibull distribution to represent the failure model of product? The Weibull distribution, log-normal distribution, and an improved maximum entropy probability distribution were compared and analyzed to find an optimum and precise reliability analysis model. By utilizing computer simulation technology and k-s hypothesis testing, the feasibility of three models was verified, and the reliability of different models obtained via practical bearing failure data was compared and analyzed. The research indicates that the reliability model of two-parameter Weibull distribution does not apply to all situations, and sometimes, two-parameter log-normal distribution model is more precise and feasible; compared to three-parameter log-normal distribution model, the three-parameter Weibull distribution manifests better accuracy but still does not apply to all cases, while the novel proposed model of improved maximum entropy probability distribution fits not only all kinds of known distributions but also poor information issues with unknown probability distribution, prior information, or trends, so it is an ideal reliability analysis model with least error at present.

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