Version 1
: Received: 23 July 2024 / Approved: 24 July 2024 / Online: 25 July 2024 (12:58:42 CEST)
How to cite:
Quijada Pioquinto, J. G.; Shakhov, V.; Minaev, E.; Kurkin, E.; Lukyanov, O. Optimization of Airfoils for the Design of Long Endurance Aircrafts Using Deep Learning Models and Metaheuristics Algorithms. Preprints2024, 2024071992. https://doi.org/10.20944/preprints202407.1992.v1
Quijada Pioquinto, J. G.; Shakhov, V.; Minaev, E.; Kurkin, E.; Lukyanov, O. Optimization of Airfoils for the Design of Long Endurance Aircrafts Using Deep Learning Models and Metaheuristics Algorithms. Preprints 2024, 2024071992. https://doi.org/10.20944/preprints202407.1992.v1
Quijada Pioquinto, J. G.; Shakhov, V.; Minaev, E.; Kurkin, E.; Lukyanov, O. Optimization of Airfoils for the Design of Long Endurance Aircrafts Using Deep Learning Models and Metaheuristics Algorithms. Preprints2024, 2024071992. https://doi.org/10.20944/preprints202407.1992.v1
APA Style
Quijada Pioquinto, J. G., Shakhov, V., Minaev, E., Kurkin, E., & Lukyanov, O. (2024). Optimization of Airfoils for the Design of Long Endurance Aircrafts Using Deep Learning Models and Metaheuristics Algorithms. Preprints. https://doi.org/10.20944/preprints202407.1992.v1
Chicago/Turabian Style
Quijada Pioquinto, J. G., Evgenii Kurkin and Oleg Lukyanov. 2024 "Optimization of Airfoils for the Design of Long Endurance Aircrafts Using Deep Learning Models and Metaheuristics Algorithms" Preprints. https://doi.org/10.20944/preprints202407.1992.v1
Abstract
This paper presents a methodology based on deep learning models and metaheuristic algorithms for the optimization of airfoils for the design of aircraft wings with large endurance. The use of AZTLI-NN (a neural network with an architecture composed of a multilayer perceptron and a variational autoencoder) is implemented for the prediction of graphs of the aerodynamic coefficients of the airfoil as a function of the angle of attack. This neural network presents good predictions of the aerodynamic coefficients, similar to the coefficients obtained with computational fluid dynamics simulations. AZTLI-NN in combination of metaheuristic algorithms and the CST profile parameterization method show excellent performance in single-objective and multi-objective profile optimization tasks.
Keywords
airfoil optimization; OpenFOAM; deep learning models; metaheuristics algorithms; method CST.
Subject
Engineering, Aerospace Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
