Optimization and design of a flexible droop-nose leading-edge morphing wing based on a novel black widow optimization algorithm—part I

Bashir, Musavir, Longtin-Martel, Simon, Botez, Ruxandra Mihaela and Wong, Tony. 2022. « Optimization and design of a flexible droop-nose leading-edge morphing wing based on a novel black widow optimization algorithm—part I ». Designs, vol. 6, nº 1.
Compte des citations dans Scopus : 7.

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Abstract

An aerodynamic optimization for a Droop-Nose Leading-Edge (DNLE) morphing of a well-known UAV, the UAS-S45, is proposed, using a novel Black Widow Optimization (BWO) algorithm. This approach integrates the optimization algorithm with a modified Class-Shape Transformation (CST) parameterization method to enhance aerodynamic performance by minimizing drag and maximizing aerodynamic endurance at the cruise flight condition. The CST parameterization technique is used to parameterize the reference airfoil by introducing local shape changes and provide skin flexibility to obtain various optimized morphing airfoil configurations. The optimization framework uses an in-house MATLAB algorithm, while the aerodynamic calculations use the XFoil solver with flow transition estimation criteria. These results are validated with a CFD solver utilizing the Transition (γ−Reθ) Shear Stress Transport (SST) turbulence model. Numerical studies verified the effectiveness of the optimization strategy, and the optimized airfoils have shown a significant improvement in overall aerodynamic performance by up to 12.18% drag reduction compared to the reference airfoil, and an increase in aerodynamic endurance of up to 10% for the UAS-S45 optimized airfoil configurations over its reference airfoil. These results indicate the importance of leading-edge morphing in enhancing the aerodynamic efficiency of the UAS-S45 airfoil.

Item Type:	Peer reviewed article published in a journal
Professor:	Professor Botez, Ruxandra Wong, Tony
Affiliation:	Génie des systèmes, Génie des systèmes
Date Deposited:	07 Mar 2022 19:06
Last Modified:	10 Mar 2022 18:47
URI:	https://espace2.etsmtl.ca/id/eprint/24044

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