Longitudinal aerodynamic modeling of a reduced-scale aircraft using neuro-fuzzy hybridized with differential evolution-Reference-Cited by-同舟云学术

Longitudinal aerodynamic modeling of a reduced-scale aircraft using neuro-fuzzy hybridized with differential evolution

Published:2024-06-05 Issue:7 Volume:46 Page:
ISSN:1678-5878
Container-title:Journal of the Brazilian Society of Mechanical Sciences and Engineering
language:en
Short-container-title:J Braz. Soc. Mech. Sci. Eng.

Author:

Sant’Ana Vitor^ORCID,Staack Ingo,Neto Roberto Finzi

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s40430-024-04930-x.pdf

Reference23 articles.

1. Rajkumar T, Bardina JE (2002) Prediction of aerodynamic coefficients using neural networks for sparse data. In: FLAIRS conference. Florida, USA, pp 242–246

2. Cerezo-Pacheco AD, Pérez-Velasco CA, Lozano-Hernández Y, Rodríguez-Cortés H, Sánchez-Meza VG (2021) Integration of x-plane and matlab for modeling and simulation of a tiltrotor uav. In: 2021 International conference on mechatronics, electronics and automotive engineering (ICMEAE), pp 39–44. https://doi.org/10.1109/ICMEAE55138.2021.00014 . IEEE

3. Tao J, Sun G (2019) Application of deep learning based multi-fidelity surrogate model to robust aerodynamic design optimization. Aerosp Sci Technol 92:722–737. https://doi.org/10.1016/j.ast.2019.07.002

4. Karali H, Inalhan G, Umut Demirezen M, Adil Yukselen M (2021) A new nonlinear lifting line method for aerodynamic analysis and deep learning modeling of small unmanned aerial vehicles. Int J Micro Air Veh 13. https://doi.org/10.1177/17568293211016817

5. Zan B-W, Han Z-H, Xu C-Z, Liu M-Q, Wang W-Z (2022) High-dimensional aerodynamic data modeling using a machine learning method based on a convolutional neural network. Adv Aerodyn 4(1):1–31. https://doi.org/10.1186/s42774-022-00128-8