This repository hold comparative techniques associated with machine learning to examine CFD and aerodynamics of a bunch of airfoils in the UIUC airfoil datasite. The codes were initially released in the short course of KSCFE 2022. and have been modified and added for creating a technical report by researchers @ GIST (Prof. Seongim Choi, Wontae Hwang, Suhun Cho). Please check them in the AI-CFD-Technical-Report repository (https://github.com/Jameshin/AI-CFD-Technical-Report).
- Comparative study of POD, DNN, and their Mixed in the Case of the Eppler387 Airfoil
- Appication of CNN to the 1550 UIUC airfoils
- Data repository : https://dataon.kisti.re.kr/search/view.do?mode=view&svcId=9ea6683a346f59a9e3391ad6473f67d6
- Shin, J. H. and Sa, J. H, "UIUC airfoil dataset, Grid, aerodynamics, computational fluid dynamics, Simulation, dataon, http://doi.org/10.22711/idr/952, 2022.
- Related Literature
- Shin, J. H., Utilizing Data/AI Libraries for CFD: A Case of Airfoil Aerodynamics, 12th CFD Short Course of Korean Society for Computational Fluid Engineering, 2022. (in Korean)
- Shin, J.-H., Cho, K.-W. Comparative study on reduced models of unsteady aerodynamics using proper orthogonal decomposition and deep neural network. Journal of Mechanical Science and Technology 36 (9) (2022) 4491~4499. http://doi.org/10.1007/s12206-022-0813-3