Parametric study of NACA 4-series airfoil designs for lift-to-drag performance

Airfoil design has a great influence on the overall flight performance of flying vehicles. In conceptual design stage where design decisions have to be quickly made, having a metamodel that can properly capture the relationship between airfoil design parameters and resultant aerodynamic performance...

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Bibliographic Details
Main Authors: Romli, Fairuz Izzuddin, Nasir, Muhammad Naqib Md
Format: Article
Language:English
Published: The Aeronautical and Astronautical Society of the Republic of China 2024
Online Access:http://psasir.upm.edu.my/id/eprint/112198/1/112198.pdf
http://psasir.upm.edu.my/id/eprint/112198/
https://www.airitilibrary.com/Article/Detail/P20140627004-N202403020027-00002
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Institution: Universiti Putra Malaysia
Language: English
Description
Summary:Airfoil design has a great influence on the overall flight performance of flying vehicles. In conceptual design stage where design decisions have to be quickly made, having a metamodel that can properly capture the relationship between airfoil design parameters and resultant aerodynamic performance is very useful for the airfoil selection process. In line with this notion, this study is done to perform computational fluid dynamics (CFD) simulation analysis to obtain the effects of varying airfoil design parameters on the resultant lift-to-drag (L/D) performance and establish a mathematical metamodel for this relationship. 140 airfoil designs have been constructed by varying the digits in the NACA 4-series airfoil numbering system and they are analyzed using XFLR5 software at zero-degree angle of attack, Reynolds number of 100,000 and 0.3 Mach with sea level conditions. Using the simulation analysis results, regression analysis is used to form a mathematical metamodel for the effects relationship between the airfoil design variables as described by the NACA 4-series numbering system and the resultant L/D. The maximum thickness of the airfoil design is found to have the highest impact on L/D. Moreover, the metamodel has been shown to have acceptable goodness-of-fit level with R² value of 98.81 and prediction error of less than 20.