Inverse Design of Airfoils Using Artificial Neural Network

Chol Myong Ri; Kwang Hyok Ri; Myong Chol Ri

doi:doi:10.11648/j.ajesa.20251101.12

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Inverse Design of Airfoils Using Artificial Neural Network

Chol Myong Ri, Kwang Hyok Ri, Myong Chol Ri^*

Published in American Journal of Embedded Systems and Applications (Volume 11, Issue 1)

Received: 12 August 2025 Accepted: 9 September 2025 Published: 26 November 2025

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Abstract

With the development of science and technology, application of UAV is increasing in the world. In order to improve the flight performance of UAV, it is important to inversely design the airfoil that generates lift. Inverse design of airfoils has been a challenging task and subject of investigation for a long time, date back to the beginning of airfoil research. In this paper, an inverse design method of airfoil using ANN and profile database is proposed. In preliminary design phase of the UAV, airfoil database and ANN are constructed and validated to design a profile that satisfied the aerodynamic character of the airfoil for a given flight performance and availability are verified. Many types of airfoils are parameterized using the PARSEC method. For the NASA SC, RAE, HQ, and NACA series of airfoil, the aerodynamic characteristics at the specified Reynolds number and angle of attack are calculated and database are created. These aerodynamic characters are trained by ANN to establish the inverse design process of the airfoil. For the airfoil obtained through the reverse design process, the lift and drag values obtained using CFD calculations are in relatively good agreement with the setting target values.

Published in	American Journal of Embedded Systems and Applications (Volume 11, Issue 1)
DOI	10.11648/j.ajesa.20251101.12
Page(s)	8-15
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Airfoil, Artificial Neural Network, Inverse Design

References

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[2]	L. F. Gonzalez, “Robust design optimisation using multi-objective evolutionary algorithms”, Computers & Fluids 37 (2008) 565–583. https://doi.org/10.1016/j.compfluid.2007.07.011
[3]	Laura Mainini and Paolo Maggiore, “Multidisciplinary Integrated Framework for the Optimal Design of a Jet Aircraft Wing”, International Journal of Aerospace Engineering (2012), https://doi.org/10.1155/2012/750642
[4]	Hu Tianyuan and Yu Xiongqing, “Aerodynamic/Stealthy/Structural Multidisciplinary Design Optimization of Unmanned Combat Air Vehicle”, Chinese Journal of Aeronautics 22(2009) 380-386. https://doi.org/10.1016/S1000-9361(08)60114-4
[5]	Khayyam Masood and Zhang Wei, “Robust Multidisciplinary Optimization for Wing of a Low Subsonic UAV”, Global Journal of Technology and Optimization (2018), https://doi.org/10.4172/2332-2543.1000226
[6]	Yasushi Ito, “Multidisciplinary Design Optimization of Wing Shape for a Small Jet Aircraft Using Kriging Model”, AIAA 2006-932.
[7]	Marija Samard and Lamine Rebhi, “UAV aerodynamic design involving genetic algorithm and artificial neural network for wing preliminary computation”, Aerospace Science and Technology (2018), https://doi.org/10.1016/j.ast.2018.09.043
[8]	Hassan Moin and Hafiz Zeeshan, “Airfoil’s Aerodynamic Coefficients Prediction using Artificial Neural Network”, Institute of Space Technology, 2021.
[9]	Pierluigi Della Vecchia and Elia Daniele, “An airfoil shape optimization technique coupling PARSEC parameterization and evolutionary algorithm”, Aerospace Science and Technology 32 (2014) 103–110, https://doi.org/10.1016/j.ast.2013.11.006
[10]	Kensley Balla and Ruben Sevilla, “An application of neural networks to the prediction of aerodynamic coefficients of aerofoils and wings”, Applied Mathematical Modelling (2021).
[11]	Jing WANG and Haixin CHEN, “An inverse design method for supercritical airfoil based on conditional generative models”, Chinese Journal of Aeronautics, (2022), 35(3): 62–74. https://doi.org/10.1016/j.cja.2021.03.006
[12]	Ruiwu Lei and Junqiang Bai, “Deep learning based multistage method for inverse design of supercritical airfoil”, Aerospace Science and Technology 119 (2021) 107101. https://doi.org/10.1016/j.ast.2021.107101
[13]	Zhi-Hua Chen and Nadine Aubry, “Fast Prediction of Flow Field around Airfoils Based on Deep Convolutional Neural Network”, Applied sciences (2022). https://doi.org/10.3390/app122312075
[14]	Andrew Glaws and Ganesh Vijayakumar, “Invertible Neural Networks for Airfoil Design”, AIAA JOURNAL (2022). https://doi.org/10.2514/1.J060866
[15]	Joshua D and Gregory W. Reich, “Robust Optimal Design and Control of a Maneuvering Morphing Airfoil”, JOURNAL OF AIRCRAFT (2022), Vol. 59, No. 4. https://doi.org/10.2514/1.C036440
[16]	Maryam Ghodrat and Milad Heidari, “Numerical and experimental investigation to design a novel morphing airfoil for performance optimization”, Propulsion and Power Research 2023; 12(1): 83-103. https://doi.org/10.1016/j.jppr.2023.02.004
[17]	Feng Deng and Cheng Xue, “Parameterizing Airfoil Shape Using Aerodynamic Performance Parameters”, AIAA JOURNAL (2022). https://doi.org/10.2514/1.J061464

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APA Style

Ri, C. M., Ri, K. H., Ri, M. C. (2025). Inverse Design of Airfoils Using Artificial Neural Network. American Journal of Embedded Systems and Applications, 11(1), 8-15. https://doi.org/10.11648/j.ajesa.20251101.12

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ACS Style

Ri, C. M.; Ri, K. H.; Ri, M. C. Inverse Design of Airfoils Using Artificial Neural Network. Am. J. Embed. Syst. Appl. 2025, 11(1), 8-15. doi: 10.11648/j.ajesa.20251101.12

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AMA Style

Ri CM, Ri KH, Ri MC. Inverse Design of Airfoils Using Artificial Neural Network. Am J Embed Syst Appl. 2025;11(1):8-15. doi: 10.11648/j.ajesa.20251101.12

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@article{10.11648/j.ajesa.20251101.12,
  author = {Chol Myong Ri and Kwang Hyok Ri and Myong Chol Ri},
  title = {Inverse Design of Airfoils Using Artificial Neural Network
},
  journal = {American Journal of Embedded Systems and Applications},
  volume = {11},
  number = {1},
  pages = {8-15},
  doi = {10.11648/j.ajesa.20251101.12},
  url = {https://doi.org/10.11648/j.ajesa.20251101.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajesa.20251101.12},
  abstract = {With the development of science and technology, application of UAV is increasing in the world. In order to improve the flight performance of UAV, it is important to inversely design the airfoil that generates lift. Inverse design of airfoils has been a challenging task and subject of investigation for a long time, date back to the beginning of airfoil research. In this paper, an inverse design method of airfoil using ANN and profile database is proposed. In preliminary design phase of the UAV, airfoil database and ANN are constructed and validated to design a profile that satisfied the aerodynamic character of the airfoil for a given flight performance and availability are verified. Many types of airfoils are parameterized using the PARSEC method. For the NASA SC, RAE, HQ, and NACA series of airfoil, the aerodynamic characteristics at the specified Reynolds number and angle of attack are calculated and database are created. These aerodynamic characters are trained by ANN to establish the inverse design process of the airfoil. For the airfoil obtained through the reverse design process, the lift and drag values obtained using CFD calculations are in relatively good agreement with the setting target values.
},
 year = {2025}
}

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TY  - JOUR
T1  - Inverse Design of Airfoils Using Artificial Neural Network

AU  - Chol Myong Ri
AU  - Kwang Hyok Ri
AU  - Myong Chol Ri
Y1  - 2025/11/26
PY  - 2025
N1  - https://doi.org/10.11648/j.ajesa.20251101.12
DO  - 10.11648/j.ajesa.20251101.12
T2  - American Journal of Embedded Systems and Applications
JF  - American Journal of Embedded Systems and Applications
JO  - American Journal of Embedded Systems and Applications
SP  - 8
EP  - 15
PB  - Science Publishing Group
SN  - 2376-6085
UR  - https://doi.org/10.11648/j.ajesa.20251101.12
AB  - With the development of science and technology, application of UAV is increasing in the world. In order to improve the flight performance of UAV, it is important to inversely design the airfoil that generates lift. Inverse design of airfoils has been a challenging task and subject of investigation for a long time, date back to the beginning of airfoil research. In this paper, an inverse design method of airfoil using ANN and profile database is proposed. In preliminary design phase of the UAV, airfoil database and ANN are constructed and validated to design a profile that satisfied the aerodynamic character of the airfoil for a given flight performance and availability are verified. Many types of airfoils are parameterized using the PARSEC method. For the NASA SC, RAE, HQ, and NACA series of airfoil, the aerodynamic characteristics at the specified Reynolds number and angle of attack are calculated and database are created. These aerodynamic characters are trained by ANN to establish the inverse design process of the airfoil. For the airfoil obtained through the reverse design process, the lift and drag values obtained using CFD calculations are in relatively good agreement with the setting target values.

VL  - 11
IS  - 1
ER  -

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Author Information

Chol Myong Ri

Faculty of Physical Engineering, Kim Chaek University of Technology, Pyongyang, Democratic People’s Republic of Korea
Kwang Hyok Ri

Faculty of Physical Engineering, Kim Chaek University of Technology, Pyongyang, Democratic People’s Republic of Korea
Myong Chol Ri

Faculty of Physical Engineering, Kim Chaek University of Technology, Pyongyang, Democratic People’s Republic of Korea

Contact Email

http://orcid.org/0009-0007-5835-904X

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APA Style

Ri, C. M., Ri, K. H., Ri, M. C. (2025). Inverse Design of Airfoils Using Artificial Neural Network. American Journal of Embedded Systems and Applications, 11(1), 8-15. https://doi.org/10.11648/j.ajesa.20251101.12

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ACS Style

Ri, C. M.; Ri, K. H.; Ri, M. C. Inverse Design of Airfoils Using Artificial Neural Network. Am. J. Embed. Syst. Appl. 2025, 11(1), 8-15. doi: 10.11648/j.ajesa.20251101.12

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AMA Style

Ri CM, Ri KH, Ri MC. Inverse Design of Airfoils Using Artificial Neural Network. Am J Embed Syst Appl. 2025;11(1):8-15. doi: 10.11648/j.ajesa.20251101.12

Copy | Download

@article{10.11648/j.ajesa.20251101.12,
  author = {Chol Myong Ri and Kwang Hyok Ri and Myong Chol Ri},
  title = {Inverse Design of Airfoils Using Artificial Neural Network
},
  journal = {American Journal of Embedded Systems and Applications},
  volume = {11},
  number = {1},
  pages = {8-15},
  doi = {10.11648/j.ajesa.20251101.12},
  url = {https://doi.org/10.11648/j.ajesa.20251101.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajesa.20251101.12},
  abstract = {With the development of science and technology, application of UAV is increasing in the world. In order to improve the flight performance of UAV, it is important to inversely design the airfoil that generates lift. Inverse design of airfoils has been a challenging task and subject of investigation for a long time, date back to the beginning of airfoil research. In this paper, an inverse design method of airfoil using ANN and profile database is proposed. In preliminary design phase of the UAV, airfoil database and ANN are constructed and validated to design a profile that satisfied the aerodynamic character of the airfoil for a given flight performance and availability are verified. Many types of airfoils are parameterized using the PARSEC method. For the NASA SC, RAE, HQ, and NACA series of airfoil, the aerodynamic characteristics at the specified Reynolds number and angle of attack are calculated and database are created. These aerodynamic characters are trained by ANN to establish the inverse design process of the airfoil. For the airfoil obtained through the reverse design process, the lift and drag values obtained using CFD calculations are in relatively good agreement with the setting target values.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Inverse Design of Airfoils Using Artificial Neural Network

AU  - Chol Myong Ri
AU  - Kwang Hyok Ri
AU  - Myong Chol Ri
Y1  - 2025/11/26
PY  - 2025
N1  - https://doi.org/10.11648/j.ajesa.20251101.12
DO  - 10.11648/j.ajesa.20251101.12
T2  - American Journal of Embedded Systems and Applications
JF  - American Journal of Embedded Systems and Applications
JO  - American Journal of Embedded Systems and Applications
SP  - 8
EP  - 15
PB  - Science Publishing Group
SN  - 2376-6085
UR  - https://doi.org/10.11648/j.ajesa.20251101.12
AB  - With the development of science and technology, application of UAV is increasing in the world. In order to improve the flight performance of UAV, it is important to inversely design the airfoil that generates lift. Inverse design of airfoils has been a challenging task and subject of investigation for a long time, date back to the beginning of airfoil research. In this paper, an inverse design method of airfoil using ANN and profile database is proposed. In preliminary design phase of the UAV, airfoil database and ANN are constructed and validated to design a profile that satisfied the aerodynamic character of the airfoil for a given flight performance and availability are verified. Many types of airfoils are parameterized using the PARSEC method. For the NASA SC, RAE, HQ, and NACA series of airfoil, the aerodynamic characteristics at the specified Reynolds number and angle of attack are calculated and database are created. These aerodynamic characters are trained by ANN to establish the inverse design process of the airfoil. For the airfoil obtained through the reverse design process, the lift and drag values obtained using CFD calculations are in relatively good agreement with the setting target values.

VL  - 11
IS  - 1
ER  -

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