Physical Modelling for the Precise Representation of Flow Phenomena Based on Simultaneous Similitude of Multiple Dimensionless Numbers

Author(s):  
Yuichi Tsukaguchi ◽  
Kodai Fujita ◽  
Hideki Murakami ◽  
Roderick I. L. Guthrie
2021 ◽  
Vol 61 (12) ◽  
pp. 2897-2903
Author(s):  
Yuichi Tsukaguchi ◽  
Kodai Fujita ◽  
Hideki Murakami ◽  
Roderick I. L. Guthrie

2011 ◽  
Vol 32 (2) ◽  
pp. 91-100 ◽  
Author(s):  
Paweł Mirek

Scaling of flow phenomena in circulating fluidized bed boilers The paper presents an overview of scaling models used for determining hydrodynamic parameters of Circulating Fluidized Bed boilers. The governing equations and the corresponding dimensionless numbers are derived and presented for three different approaches to the scaling law of fluidized beds: classical dimensional analysis, differential equations and integrated solutions and experimental correlations. Some results obtained with these equations are presented. Finally, the capabilities and limitations of scaling experiments are discussed.


1995 ◽  
Vol 46 (1) ◽  
pp. 197 ◽  
Author(s):  
A Worman

A coupled hydrological and biogeochemical model for aqueous contaminant transport has been developed on the basis of field data on phosphorus transport in minor drainage brooks through agricultural areas in Sweden. Large scattering exists in constitutive parameters reflecting mass transfer due to solute/particle sorption and retention in bed sediments. To cope with these uncertainties, a physical/modelling framework was developed that, in a deterministic way, takes into account the most essential mechanisms controlling the transport on a regional scale and also includes randomness in process behaviour. Constitutive parameters of the governing system are conceived as stochastic, continuous fields in space and can be evaluated from field data by means of geostatistics. This modelling approach enables one to conduct analyses of uncertainty/error propagation and effects of system heterogeneity on both expected predictions and confidence intervals. Depending on the governing dimensionless numbers of the problem, changes in the covariance structure of parameter fields may cause severe deviations between statistically expected predictions associated with a stochastic parameter field and predictions based on the average value of the parameter.


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