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CFD Analysis of an RC Aircraft Wing Optimization

Project Summary

Understanding airflow behavior around aircraft wings is critical in designing high-performance aerial platforms. With the growing usage of RC (radio-controlled) aircraft in training, recreational flying, and UAV applications, optimizing wing design can significantly enhance flight stability, endurance, and lift generation. This project compared two wing geometries under identical simulation conditions to extract valuable insights for design improvement.

Objectives and Approach

This project involved a comprehensive Computational Fluid Dynamics (CFD) analysis to investigate and optimize the aerodynamic performance of two custom-designed RC aircraft wings. The aim was to evaluate lift and drag characteristics using Ansys Fluent, and to determine which wing configuration — a single wing or a multi-winglet design — provided better aerodynamic efficiency for RC flight applications.
Understanding airflow behavior around aircraft wings is critical in designing high-performance aerial platforms. With the growing usage of RC (radio-controlled) aircraft in training, recreational flying, and UAV applications, optimizing wing design can significantly enhance flight stability, endurance, and lift generation. This project compared two wing geometries under identical simulation conditions to extract valuable insights for design improvement.

The key objectives of this project were:

To evaluate aerodynamic coefficients (lift and drag) of two wing designs using CFD analysis in Ansys.
To determine the impact of multi-winglet configurations on flow separation, turbulent intensity, and lift enhancement.
To visualize the flow field using contours of Mach number, static pressure, and dynamic pressure.
To improve flight performance and lift-to-drag ratio by modifying winglet geometry and orientation.

Two wings were designed and simulated:

Wing 1 – A baseline straight wing with no winglets, modeled at a 13.3° angle of attack.
Wing 2 – An optimized wing with multi-winglet design featuring three blades positioned at 90°, 0°, and –90°, also analyzed at 13.3° angle of attack.

CAD modeling was performed in SolidWorks, and simulations were carried out using Ansys Fluent 21 with a pressure-based solver, SST k-omega turbulence model, and steady-state assumptions. The airflow was treated as incompressible with ideal gas properties.

Simulation Conditions and Assumptions

Flow type: Steady, incompressible, turbulent
Solver: Ansys Fluent (pressure-based)
Turbulence model: SST k-omega
Velocity formulation: Absolute
Material: Air (ideal gas)
Boundary Conditions: Far-field pressure boundaries with initialization from inlet. Turbulence intensity of 5%, viscosity ratio of 10
Meshing: Structured mesh with refinement at wing surfaces and wake region
Lift and drag calculated using standard force vector settings in Fluent

Results and Findings

Aerodynamic Coefficients:

Lift Coefficient (Cl):

Wing 1 showed a moderate lift coefficient with gradual convergence after 30 iterations.
Wing 2 demonstrated a significantly higher lift due to enhanced surface interaction from the multi-winglet geometry.

Drag Coefficient (Cd):

Wing 1 had lower initial drag but plateaued quickly.
Wing 2 showed slightly higher drag initially, which later stabilized, but the overall lift-to-drag ratio was superior in Wing 2.

Flow Visualization:

Pressure Coefficient (Cp):

Wing 2 had a higher pressure difference between the upper and lower surfaces, contributing to increased lift.
Sharp pressure gradients near winglet edges confirmed effective airflow redirection.

Mach Number:

Both wings operated in subsonic flow regimes.
Wing 2 experienced slightly higher localized flow acceleration due to its complex geometry.

Turbulent Intensity and Dynamic Pressure:

Wing 2 had increased turbulent intensity at winglet junctions, indicating more energetic boundary layer interaction.
Despite added complexity, the wake profile was narrower, suggesting better downstream pressure recovery.

Comparative Insight:

The multi-winglet wing design (Wing 2) offered improved aerodynamic performance, particularly in lift generation, while maintaining acceptable drag. The structural complexity introduced by additional winglets resulted in enhanced flow reattachment, delayed separation, and better pressure recovery, making it more suitable for efficient RC aircraft flight.

Conclusion

This CFD simulation project using Ansys Fluent successfully demonstrated the aerodynamic advantage of a multi-winglet design over a standard single wing for RC aircraft applications. By comparing lift, drag, and flow behavior through high-fidelity simulations, this study validated how geometry optimization directly impacts flight performance.

This analysis not only enhances understanding of aerodynamic principles at smaller scales but also reinforces the value of CFD-based design workflows in educational and experimental aerospace engineering.