Optimization Design of NACA 4412 Airfoil Based on Genetic Algorithm for Efficiency and Maximum Lift Force

The NACA 4412 airfoil, renowned for its exceptional subsonic aerodynamic performance, faces critical operational constraints including elevated drag characteristics and premature stall onset at high angles of attack. While previous investigations have independently examined internal slot modifications and gurney flap implementations for aerodynamic enhancement, the synergistic potential of their combined application to the NACA 4412 configuration remains largely unexplored. This research presents a comprehensive optimization study that strategically integrates internal slot geometry with gurney flap configuration to maximize lift coefficient (CL) and aerodynamic efficiency (CL/CD) through advanced genetic algorithm optimization. The methodology employed high-fidelity computational fluid dynamics simulations using FLUENT with Spalart-Allmaras turbulence modeling, validated against experimental data to ensure accuracy. PCHIP numerical interpolation techniques were utilized to estimate aerodynamic coefficients across comprehensive angle-of-attack ranges not directly simulated. The optimized configuration demonstrated remarkable performance improvements, including a 75.68% increase in lift coefficient, an extension of the critical stall angle from 14° to 16°, enhanced aerodynamic efficiency, and significantly improved flow stability with reduced separation characteristics. These findings establish that the synergistic combination of internal slot and gurney flap modifications can fundamentally transform NACA 4412 aerodynamic performance, particularly excelling in high-angle-of-attack operational scenarios.

Authors

Publication Details

Journal
Jurnal Rekayasa Mesin
Published
2025-08-31
DOI
https://doi.org/10.32497/jrm.v20i2.6531
Primary Topic
Robotic Path Planning Algorithms
Type
article
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article

Optimization Design of NACA 4412 Airfoil Based on Genetic Algorithm for Efficiency and Maximum Lift Force

Aldias Bahatmaka, Haris Nubli, Deni Fajar Fitriyana, Md. Habibullah et al.
Jurnal Rekayasa Mesin
Robotic Path Planning Algorithms
article

Optimization Design of NACA 4412 Airfoil Based on Genetic Algorithm for Efficiency and Maximum Lift Force

Aldias Bahatmaka, Haris Nubli, Deni Fajar Fitriyana, Md. Habibullah, Cho Joung Hyung, Aryo Eko Laksono, Galih Tri Laksono
article en

Abstract

The NACA 4412 airfoil, renowned for its exceptional subsonic aerodynamic performance, faces critical operational constraints including elevated drag characteristics and premature stall onset at high angles of attack. While previous investigations have independently examined internal slot modifications and gurney flap implementations for aerodynamic enhancement, the synergistic potential of their combined application to the NACA 4412 configuration remains largely unexplored. This research presents a comprehensive optimization study that strategically integrates internal slot geometry with gurney flap configuration to maximize lift coefficient (CL) and aerodynamic efficiency (CL/CD) through advanced genetic algorithm optimization. The methodology employed high-fidelity computational fluid dynamics simulations using FLUENT with Spalart-Allmaras turbulence modeling, validated against experimental data to ensure accuracy. PCHIP numerical interpolation techniques were utilized to estimate aerodynamic coefficients across comprehensive angle-of-attack ranges not directly simulated. The optimized configuration demonstrated remarkable performance improvements, including a 75.68% increase in lift coefficient, an extension of the critical stall angle from 14° to 16°, enhanced aerodynamic efficiency, and significantly improved flow stability with reduced separation characteristics. These findings establish that the synergistic combination of internal slot and gurney flap modifications can fundamentally transform NACA 4412 aerodynamic performance, particularly excelling in high-angle-of-attack operational scenarios.

Jurnal Rekayasa MesinVol. 20(2)
Openalex Percentile: Top 77%
Robotic Path Planning Algorithms
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