Deep Learning-Based Prediction of Aerodynamic Performance for Airfoils in Transonic Regime-Reference-Cited by-同舟云学术

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Deep Learning-Based Prediction of Aerodynamic Performance for Airfoils in Transonic Regime

Published:2023-10-21 Issue: Volume: Page:
ISSN:
Container-title:2023 5th Novel Intelligent and Leading Emerging Sciences Conference (NILES)
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Author:

Ayman Tarek¹,Elrefaie Mayar A.¹,Sayed Eman¹,Elrefaie Mohamed²,Ayyad Mahmoud³,Hamada Ahmed A.⁴,Abdelrahman Mohamed M.¹

Affiliation:

1. Cairo University,Aerospace Engineering Department,Giza,Egypt,12613

2. Technical University of Munich,Department of Aerospace and Geodesy,Munich,Germany,80333

3. Stevens Institute of Technology,Davidson Lab,CEOE Department,Hoboken,NJ,USA,07030

4. Texas A&M University,Department of Ocean Engineering,College State,TX,USA,77843

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10296538/10296540/10296587.pdf?arnumber=10296587

Reference13 articles.

1. Linear Reduced-order Model Based on PIV Data of Flow Field around Airfoil

2. Proper orthogonal decomposition assisted inverse design optimisation method for the compressor cascade airfoil

3. Lift coefficient prediction at high angle of attack using recurrent neural network

4. A Convolutional Neural Network Approach to Training Predictors for Airfoil Performance

5. Airfoil’s Aerodynamic Coefficients Prediction using Artificial Neural Network

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Surrogate Modeling of the Aerodynamic Performance for Airfoils in Transonic Regime;AIAA SCITECH 2024 Forum;2024-01-04

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