READ ME File For 'Dataset for Determination of unsteady wing loading using tuft visualization'

Dataset DOI: 10.5258/SOTON/D3232

Date that the file was created: Sept, 2024

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GENERAL INFORMATION
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ReadMe Author: Francis De Voogt, University of Southampton ORCID ID 0000-0002-7229-7160

Date of data collection: 2020-2022

Information about geographic location of data collection: - CFD data obtained with Ansys and the UoS Iridis HPC cluster
- Experimental data obtained in the 7x5 wind tunnel at UoS



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SHARING/ACCESS INFORMATION
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Licenses/restrictions placed on the data, or limitations of reuse:CC-BY

Recommended citation for data

Francis De Voogt, Bharathram Ganapathisubramani(2024). Dataset for Determination of unsteady wing loading using tuft visualization. University of Southampton. Dataset. doi:10.5258/SOTON/D3232


This dataset supports the publication:
AUTHORS:Francis De Voogt, Bharathram Ganapathisubramani
TITLE:Determination of unsteady wing loading using tuft visualization
JOURNAL:Experiments in Fluids
PAPER DOI IF KNOWN:10.1007/s00348-024-03882-1

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DATA & FILE OVERVIEW
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This dataset contains:

- tuft_analysis_CFD.ipynb : jupyter notebook for creating the model based on CFD data
- tuft_analysis_experimental.ipynb : jupyter notebook for creating the models based on experimental data
> lift_nn_model : folder containing the files that make up the lift model for experimental data
> moment_nn_model : folder containing the files that make up the moment model for experimental data
> lift_nn_model_cfd : folder containing the files that make up the lift model for CFD data
> CFD_data : folder containing the suction surface parameters on the wing obtained with CFD
> experimental_data : folder contain the data obtained from experiments in the wind tunnel and prepared for analysis
	> images : 3 sample images for a single angle of attack and velocity
	> ML_data: for each angle of attack the full tuft locations (fixed and free end), lift and moment.

By utilising the jupyter notebooks the .npy (numpy arrays) with data are automatically loaded and used to create new models based on the data. It is also possible to load the included models. Running the full notebooks will produce the equivalent figures as shown in the paper.

Relationship between files, if important for context: 
Preserve the file structure after unpacking and run the notebooks in the directory they are located in, this ensures the relative file location references work without changes. 


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METHODOLOGICAL INFORMATION
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Description of methods used for collection/generation of data: 

Full details on the data acquisition process are given in the paper for both the CFD and experimental data. Additionally the details for the processing of the data are given in the paper and the jupyter notebooks.