Deep Learning-Based Aerodynamic Analysis for Diverse Aircraft Configurations

Lukyanov, Oleg; Hoang, Van Hung; Guerra Guerra, Damian Josue; Quijada Pioquinto, Jose Gabriel; Kurkin, Evgenii; Nikonorov, Artem

doi:10.3390/technologies13110529

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Open AccessArticle

Deep Learning-Based Aerodynamic Analysis for Diverse Aircraft Configurations

by

Oleg Lukyanov

^1,*

,

Van Hung Hoang

²

,

Damian Josue Guerra Guerra

¹,

Jose Gabriel Quijada Pioquinto

¹

,

Evgenii Kurkin

¹

and

Artem Nikonorov

¹

Samara National Research University, 34 Moskovskoe Shosse, Samara 443086, Russia

²

Department of Aeronautical Engineering, Air Defence and Air Force Academy, Doaiphuong, Hanoi 10000, Vietnam

^*

Author to whom correspondence should be addressed.

Technologies 2025, 13(11), 529; https://doi.org/10.3390/technologies13110529 (registering DOI)

Submission received: 19 October 2025 / Revised: 7 November 2025 / Accepted: 9 November 2025 / Published: 15 November 2025

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Abstract

In this study, a neural network was developed to predict the aerodynamic characteristics of fixed-wing aircraft with two lifting surfaces of various aerodynamic configurations. The proposed neural network model can incorporate 23 parameters to describe the aerodynamic configuration of an aircraft. A methodology for discrete geometric parameterization of aerodynamic configurations is introduced, enabling coverage of various combinations of relative positions of aircraft components. This study presents an approach to database construction and automated sample generation for neural network training. Furthermore, a procedure is provided for data preprocessing and correlation analysis of the input variables. The optimization process of the hyperparameters of the multilayer perceptron (MLP) architecture is described. The neural network models are validated through comparison with numerical simulations. Finally, several aerodynamic design problems are addressed, and the key advantages of the developed MLP-based surrogate aerodynamic models are demonstrated.

Keywords: surrogate models; aerodynamic modeling; aircraft; neural network; aerodynamic configuration; discrete parameterization; database

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MDPI and ACS Style

Lukyanov, O.; Hoang, V.H.; Guerra Guerra, D.J.; Quijada Pioquinto, J.G.; Kurkin, E.; Nikonorov, A. Deep Learning-Based Aerodynamic Analysis for Diverse Aircraft Configurations. Technologies 2025, 13, 529. https://doi.org/10.3390/technologies13110529

AMA Style

Lukyanov O, Hoang VH, Guerra Guerra DJ, Quijada Pioquinto JG, Kurkin E, Nikonorov A. Deep Learning-Based Aerodynamic Analysis for Diverse Aircraft Configurations. Technologies. 2025; 13(11):529. https://doi.org/10.3390/technologies13110529

Chicago/Turabian Style

Lukyanov, Oleg, Van Hung Hoang, Damian Josue Guerra Guerra, Jose Gabriel Quijada Pioquinto, Evgenii Kurkin, and Artem Nikonorov. 2025. "Deep Learning-Based Aerodynamic Analysis for Diverse Aircraft Configurations" Technologies 13, no. 11: 529. https://doi.org/10.3390/technologies13110529

APA Style

Lukyanov, O., Hoang, V. H., Guerra Guerra, D. J., Quijada Pioquinto, J. G., Kurkin, E., & Nikonorov, A. (2025). Deep Learning-Based Aerodynamic Analysis for Diverse Aircraft Configurations. Technologies, 13(11), 529. https://doi.org/10.3390/technologies13110529

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Deep Learning-Based Aerodynamic Analysis for Diverse Aircraft Configurations

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