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The results have verified the theoretical predictions presented in Part 1.
Again, the simulation results are in line with theoretical predictions presented in Figures 4 and 5 (see results for high σ and L =1).
The comparison between the experimental drainage and evaporation patterns confirms numerical predictions presented in a previous study.
This is confirmed by the time series of the E-W gradient residuals (observed gradient estimates minus their model predictions), presented in Fig. 3 for the scalar gradient and for the three vector gradient components, considering non-polar dark data.
Such predictions, presented in terms of non-dimensional system parameters, are based on structural analysis and energy threshholds: an ideal balance of gravity potential energy and elastic spring energy.
Since the experimental data does not provide all tensor components to fully assess RANS quality, and LES results can be considered as a reliable source, they are employed in the full characterization of turbulence and in the assessment of the several RANS models predictions presented in this paper.
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Such predictions present in two distant regions often form a gain/loss pattern.
The field example is consistent with the theoretical prediction presented in Rung-Arunwan et al.
Neural networks-based techniques are presented extensively in cognitive radio networks [18,51 55], with application on spectrum prediction presented in [56,57].
The falling velocity of a spherical swarm of particles, which was called a drop of suspension, was measured and compared with a new theoretical prediction presented in this paper.
This analysis is in accordance with the structure prediction presented in PlasmoDB [ 41].
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