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13. High Reynolds Number Wind Turbine Wake Data

Author(s):
Pique, Alexander
Abstract:
This dataset is affiliated with the publication https://doi.org/10.1007/s00348-022-03455-0. All of the data provided is necessary to reproduce the results with the aforementioned publication. The data in this repository is for the wake of a wind turbine at high Reynolds numbers. The data is mainly used for reproducing the statistics (deficit and variance profiles) and the phase averaged results.
Type:
Dataset
Issue Date:
16 June 2022

14. Data set for "Film drop production over a wide range of liquid conditions"

Author(s):
Shaw, Daniel
Abstract:
Data set for "Film drop production over a wide range of liquid conditions." One .csv file is provided that contains data about the number of film drops produced by bursting bubbles of multiple sizes in various liquid conditions.
Type:
Dataset
Issue Date:
5 January 2024

15. Data set for "Ocean emission of microplastic by bursting bubble jet drops"

Author(s):
Shaw, Daniel B.; Deike, Luc
Abstract:
Data set for "Ocean emission of microplastic by bursting bubble jet drops." Two .csv files are provided: one for the size of a jet drop carrying microplastic, and another for the amount of microplastic captured by a jet drop.
Type:
Dataset
Issue Date:
21 December 2022

17. Bubble pinch-off in turbulence

Abstract:
Experimental data, simulation code, and Python scripts to reproduce the data presented in "Bubble pinch-off in turbulence"
Type:
Dataset and Software

20. Instability of shear flows in spatially periodic domains

Abstract:
Force-driven parallel shear flow in a spatially periodic domain is shown to be linearly unstable with respect to both the Reynolds number and the domain aspect ratio. This finding is confirmed by computer simulations, and a simple expression is derived to determine stable flow conditions. Periodic extensions of Couette and Poiseuille flows are unstable at Reynolds numbers two orders of magnitude smaller than their aperiodic equivalents because the periodic boundaries impose fundamentally different constraints. This instability has important implications for designing computational models of nonlinear dynamic processes with periodicity.
Type:
Dataset