--> Abstract: Turbidity Currents Physical Modelling from Three Different Scales: Data Parameterization Through Dimensional Analysis and Multiple Regression Models, by Eduardo Puhl, Ana Luiza O. Borges, and Rogério D. Maestri; #90078 (2008)

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Turbidity Currents Physical Modelling from Three Different Scales: Data Parameterization Through Dimensional Analysis and Multiple Regression Models

Eduardo Puhl, Ana Luiza O. Borges, and Rogério D. Maestri
NECOD/IPH/UFRGS, Porto Alegre, Brazil

This work aims to associate the data of turbidity currents laboratory experiments, which had been carried through three distinct scales of simulation (from small-scale to large-scale tanks) and diverse setups (e.g. flow type and mechanism of ignition), in order to identify possible trends and convergence points between them. At total, it was examined the data of 122 experiments simulated in three different physical models: a confined small tank with 3,0 m long x 0,12 m wide and 0,2 m deep; a confined tank with 7,0 m x 0,4 m x 1,0 m; and a unconfined three-dimensional large tank with more than 13,0 m long. The data were processed and correlated using dimensional analysis and multiple regression models of monomial power law. The employ of the dimensional analysis established 32 non-dimensional groups, including Reynolds, Richardson, Keulegan, Stokes and Archimedes numbers. The relationship amongst all groups resulted in general trends with small dispersions. However the correlation also shows significantly differences concerning flow-type (conservative and non-conservative flows), lock-exchange vs. continuous flow injection, sediment properties and model scale. The multiple regressions models were employed to correlate the head velocity of flow and their geometric properties (dependent parameters) with flow rate, sediment and mixture properties (independent parameters) in order to indicate what parameters are more significant. Based on that, the results demonstrate that dependent parameters are more susceptible to flow rate, volumetric concentration and viscosity of the mixture parameters. The parameterization of all experimental data and the regression laws determined in this study can be useful to predict turbidity currents behaviour (improving numerical and experiments simulations) and implicate new directions of turbidity currents comprehension.

 

AAPG Search and Discovery Article #90078©2008 AAPG Annual Convention, San Antonio, Texas