Prediction of Airfoil Efficiency by Artificial Neural Network
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-7633-1_1
Reference20 articles.
1. Ahmed S, Kamal K (2022) Aerodynamic analyses of airfoils using machine learning as an alternative to RANS simulation. National University of Sciences and Technology, Islamabad 44000, Pakistan. https://doi.org/10.3390/app12105194
2. Bhatnagar S, Afshar Y (2019) Prediction of aerodynamic flow fields using convolutional neural networks. Comput Mech 64:525–545. https://doi.org/10.1007/s00466-019-01740-0
3. Chen H, He L, Qian W, Wang S (2020) Multiple aerodynamic coefficient prediction of airfoils using a convolution neural network. Computational Aerodynamics Research Institute, China Aerodynamics Research and Development Center, Mianyang 621000, China. https://doi.org/10.3390/sym12040544
4. Duru C, Alemdar H, Baran U (2021) CNNFOIL: convolutional encoder decoder modeling for pressure fields around airfoils. Neural Comput Appl. https://doi.org/10.1007/s00521-020-05461-x
5. Guo X, Li W, Iorio F (2016) Convolutional neural networks for steady flow approximation. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining. https://doi.org/10.1145/2939672.2939738
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