QoI - Droplets

DOI

This dataset consists of a database of regular and distorted droplets with droplet clusters extracted from high-resolution shadowgraphy images in technical sprays. It serves as a subclass for generating synthetic training data via domain randomisation, which is subsequently used to train Deep Learning (DL) based image segmentation models.

A more detailed description is provided in the corresponding paper: https://doi.org/10.1016/j.ijmultiphaseflow.2023.104702

Data Samples

Regular droplets:

Distorted droplets:

Identifier
DOI https://doi.org/10.18419/DARUS-5200
Related Identifier IsSupplementTo https://doi.org/10.1016/j.ijmultiphaseflow.2023.104702
Metadata Access https://darus.uni-stuttgart.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.18419/DARUS-5200
Provenance
Creator Jose, Basil ORCID logo; Hampp, Fabian ORCID logo
Publisher DaRUS
Contributor Jose, Basil; Hampp, Fabian
Publication Year 2025
Funding Reference DFG 456687251
Rights CC BY 4.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/licenses/by/4.0
OpenAccess true
Contact Jose, Basil (University of Stuttgart); Hampp, Fabian (University of Stuttgart)
Representation
Resource Type Dataset
Format application/zip; image/png; text/markdown
Size 13446006; 9435; 5447; 653; 9374291
Version 2.0
Discipline Chemistry; Construction Engineering and Architecture; Engineering; Engineering Sciences; Fluid Mechanics; Heat Energy Technology, Thermal Machines, Fluid Mechanics; Mechanical and industrial Engineering; Mechanics; Mechanics and Constructive Mechanical Engineering; Natural Sciences; Thermal Engineering/Process Engineering