Published September 22, 2023 | Version v1
Dataset Open

Drop count and size of 38 numerical simulation of a flat fan spray

  • 1. The Alan Turing Institute
  • 2. University of Birmingham, School of Chemical Engineering
  • 3. Imperial College London, Department of Chemical Engineering
  • 4. Imperial College London, Department of Aeronautics

Description

Data refer to the drop size distribution used in the paper "Data-driven modelling for drop size distributions"

by T. Traverso, T. Abadie, O. K. Matar, and L. Magri (arXiv link: https://arxiv.org/abs/2305.18049)

Each of the 38 .csv file in this folder is associated with a different working condition of the nozzle.

Specifically, the name 'alpha##_Re##_We##.csv' contains the working condition of the nozzle as

- alpha## (## is the spray angle)

- Re## (## is the Reynolds number)

- We## (## is the Weber number)

Each file contains as many raws as the number of drops.

In the i-th raw,

1) the first element is the Volume of the i-th drop;

2) the second element is the estimated surface of the i-th drop with the method in equation (18) of [1];

3) the third element is the equivalent diameter of the i-th drop (i.e., as if it was spherical - computed from the volume)

The value of the Weber number found in the Arxiv paper is half of that reported here. The correct one is the one in this database. The paper will be corrected in due time.

 

Files

alpha10_Re20_We18.csv

Files (25.4 MB)

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Additional details

Funding

European Commission
PhyCo - Physics-constrained adaptive learning for multi-physics optimization 949388
UK Research and Innovation
PREdictive Modelling with QuantIfication of UncERtainty for MultiphasE Systems (PREMIERE) EP/T000414/1

References

  • Traverso, T., Abadie, T., Matar, O. K., & Magri, L. (2023). Data-driven modelling for drop size distributions. arXiv preprint arXiv:2305.18049.