READ ME File For 'Bubble Collapse and Jet Formation in Corner Geometries'

ReadMe Author: Ivo R. Peters, University of Southampton

This dataset supports the publication:
'Bubble Collapse and Jet Formation in Corner Geometries' by Yoshiyuki Tagawa and Ivo R. Peters, Phys. Rev. Fluids (2018)


Contents
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*** data.csv ***
The main data file is 'data.csv', which contains experimental data for all experiments that were used in the paperIt consists of the following fields:
Movie: a number corresponding to the movie file name
Date: date that the experiment was performed (yyyymmdd)
series ID: number assigned to a specific series of experiments
Geometry ID: number assigned to a specific geometry (5: 60 degrees angle, 6: 90 degrees angle)
Laser power: laser power setting (0-100)
Attenuator: laser attenuator setting (0-100)
Laser Energy (V): measured voltage by energy meter
FPS: high speed camera recording frame rate (frames/second)
scale (m/pixels): scaling of the image
scale_e (m/pixel): uncertainty of the scaling
x_ref (pixels): x-position of the reference point of the geometry (the point where the two walls meet)
y_ref (pixels): y-position of the reference point of the geometry (the point where the two walls meet)
Laser energy (J): laser energy calculated from the measured voltage
x_bub_0_im (pixels): x-position of the bubble in the image at first maximum radius
y_bub_0_im (pixels): y-position of the bubble in the image at first maximum radius
bub_area (pixels): projected area of the bubble in the image at first maximum radius
x_bub_1_im (pixels): x-position of the bubble in the image at second maximum radius
y_bub_1_im (pixels): y-position of the bubble in the image at second maximum radius

*** figure03.py ***
Python script that reads data from data.csv to produce figure03.pdf.
The script also calculates the corresponding model predictions.
The figure corresponds to Figure 3 in the paper.

*** figure04.py ***
Python script that generates model predictions for a larger range of corner opening angles.
It also reads in the same data as figure03.py, but plots the normalized data.
The figure corresponds to Figure 4 in the paper.

*** movies ***
Folder that contains the compressed version of the high-speed images that were used in the paper. The files are numbered, corresponding to the Movie number given in the main file 'data.csv'.



Geographic location of data collection: University of Southampton, U.K.


Dataset available under a CC BY 4.0 licence


Publisher: University of Southampton, U.K.


Date: July 2018