SearcharxivSearch

arXiv subjects

A. Willis

Publications and source records attributed to A. Willis.

2 recordsLinked to original sources

Destabilizing turbulence in pipe flow

Turbulence is the major cause of friction losses in transport processes and it is responsible for a drastic drag increase in flows over bounding surfaces. While much effort is invested into developing ways to control and reduce turbulence intensities, so far no methods exist to altogether eliminate turbulence if velocities are sufficiently large. We demonstrate for pipe flow that appropriate distortions to the velocity profile lead to a complete collapse of turbulence and subsequently friction losses are reduced by as much as 95%. Counterintuitively, the return to laminar motion is accomplished by initially increasing turbulence intensities or by transiently amplifying wall shear. The usual measures of turbulence levels, such as the Reynolds number (Re) or shear stresses, do not account for the subsequent relaminarization. Instead an amplification mechanism measuring the interaction between eddies and the mean shear is found to set a threshold below which turbulence is suppressed beyond recovery.

physics.flu-dyn

Estimating Diversity via Frequency Ratios

We wish to estimate the total number of classes in a population based on sample counts, especially in the presence of high latent diversity. Drawing on probability theory that characterizes distributions on the integers by ratios of consecutive probabilities, we construct a nonlinear regression model for the ratios of consecutive frequency counts. This allows us to predict the unobserved count and hence estimate the total diversity. We believe that this is the first approach to depart from the classical mixed Poisson model in this problem. Our method is geometrically intuitive and yields good fits to data with reasonable standard errors. It is especially well-suited to analyzing high diversity datasets derived from next-generation sequencing in microbial ecology. We demonstrate the method's performance in this context and via simulation, and we present a dataset for which our method outperforms all competitors.

stat.ME