Published July 23, 2024
| Version v1
Data paper
Open
Actuation manifold from snapshots data
Authors/Creators
-
1.
Universidad Carlos III de Madrid
- 2. Harbin Institute of Technology Shenzhen
-
3.
Harbin Institute of Technology
Description
This dataset contains the results of the article titled Actuation manifold from snapshots data
Description: In the paper, a data-driven methodology to learn a low-dimensional actuation manifold of controlled flows is proposed. The starting point is resolving snapshot flow data for a representative ensemble of actuations. Key enablers for the actuation manifold are isometric mapping as encoder and 饾憳-nearest neighbour regression as a decoder. This methodology is tested for the fluidic pinball for Reynolds number Re = 30. he proposed methodology yields a five-dimensional manifold describing a wide range of dynamics with small representation error. The manifold is shown to be a key enabler for control-oriented flow estimation.
Code repository: https://github.com/Lmarra1/Actuation-manifold-from-snapshot-data.git
Funding: This work is supported by the National Science Foundation of China (NSFC) through grants 12172109, 12172111 and 12302293, by the Guangdong Basic and Applied Basic Research Foundation under grant 2022A1515011492, by the Guangdong Provincial Key Laboratory of Intelligent Morphing Mechanisms and Adaptive Robotics, by the Shenzhen Science and Technology Program under grant JCYJ20220531095605012, by the projects PITUFLOW-CM-UC3M and PREDATOR-CM-UC3M, funded by the call ``Estímulo a la Investigación de Jóvenes Doctores/as'' within the frame of the Convenio Plurianual CM-UC3M and the V PRICIT (V Regional Plan for Scientific Research and Technological Innovation) and by the project ARTURO, ref. PID2019-109717RB-I00/AEI/10.13039/501100011033, funded by the Spanish State Research Agency.
DOI: ...
Files
README.txt
Files
(19.0 GB)
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