Data · dataset · 2017
A Lagrangian approach to elastic turbulence in a curvilinear microfluidic channel
Listed in DataCite
A Lagrangian approach to elastic turbulence in a curvilinear microfluidic channel: Particle dispersion in a dissipative chaotic flow Elastic turbulence, a random in time flow which can drive efficient mixing in microfluidics, serves as a laboratory for non-linear and out-of-equilibrium classical physics.
Description
The literature to date shows that previous measurements of elastic turbulence have been limited to two-dimensions. By means of a direct three-dimensional Lagrangian particle tracking technique [1], we have established an experimental database of about 10 7 trajectories derived from passive tracers in elastic turbulence, generated in a curvilinear microfluidic tube.
This dataset has allowed us to study the dispersion of pairs in the chaotic flow. It has revealed that the smoothness assumption, which is at the basis of the theoretical frameworks commonly employed in the study of elastic turbulence, breaks at scales far smaller than previously believed, and that ballistic pair dispersion holds over much longer distances than expected. [2] The experimental dataset is made available here as we are certain it would prove useful once applied further analysis, not only as reference to compare with new theoretical, numerical and experimental results, but also in addressing novel basic questions in the field.
Read the rest (3 more)
[1] Afik, E. Robust and highly performant ring detection algorithm for 3d particle tracking using 2d microscope imaging. Sci. Rep. 5, 13584; doi: 10.1038/srep13584 (2015).
[2] Afik, E. & Steinberg V. On the role of initial velocities in pair dispersion in a microfluidic chaotic flow. Nat. Commun. 8:468; doi: 10.1038/s41467-017-00389-8 (2017). ************************************************ The original file was split using: split --number=l/15 ET_Lagrangian_smoothing_splines_all_20130219-20130227.h5 ET_Lagrangian_smoothing_splines_20130219-20130227.h5. to merge back can use: [UNIX] cat ET_Lagrangian_smoothing_splines_20130219-20130227.h5.a{a..o} > ET_Lagrangian_smoothing_splines_20130219-20130227.h5 [MS Windows] copy /b ET_Lagrangian_smoothing_splines_20130219-20130227.h5.a* ET_Lagrangian_smoothing_splines_20130219-20130227.h5 To confirm integrity of the output file the MD5 checksum value is provided.
The attached Jupyter notebook `A Lagrangian approach to elastic turbulence -- example notebook.ipynb` is meant to provide a simple example of how to access information in the dataset; ViTables (by PyTables) is another convenient way to explore the data.
Links
Where it is published
- Repository landing page figshare.com/articles/dataset/A_Lagrangian_approach_to_elastic_turbulence_i… ↗
landing page · from DataCite
- DOI doi.org/10.6084/m9.figshare.5112991 ↗
DOI / persistent id · from DataCite
Documentation and papers
- Creative Commons Zero v1.0 Universal creativecommons.org/publicdomain/zero/1.0/legalcode ↗
license · from DataCite
Catalogue records · 2
- DataCite API api.datacite.org/dois/10.6084/m9.figshare.5112991 ↗
metadata API · from DataCite
- DataCite Commons commons.datacite.org/doi.org/10.6084/m9.figshare.5112991 ↗
catalogue entry · from DataCite
Topics
- Stated by source
- Mechanical engineering · Physical sciences
- Inferred from text
- Imaging 75%
Provenance · 1 source records, 11 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| DataCite | 10.6084/m9.figshare.5112991 | 11 d ago | JSON v1 |
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| byte_size | source · DataCite | connector:datacite@1.0.0 | |
| concepts[field].fos:mechanical-engineering | source · DataCite | connector:datacite@1.0.0 | |
| concepts[field].fos:physical-sciences | source · DataCite | connector:datacite@1.0.0 | |
| concepts[modality].local:modality:imaging | enrichment · DataCite | keyword-concept-rules@1.0.0 | title+description (75%) |
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