CFD and Fluent Meshing Study: T-Tail Aircraft Aerodynamics

This repository contains the ANSYS Fluent meshing journals, setup files, and related documentation for a computational aerodynamics study of a T-tail aircraft configuration. The project focuses on:

• Cruise performance analysis at Mach 0.8 and 30,000 ft altitude.
• Deep stall investigation at high angles of attack (20°, 22.5°, 25°).
• Comparison of aerodynamic tools: VSPAero (panel method) vs. ANSYS Fluent (CFD).
• Turbulence modelling using k-ω SST and k-kl transition models.

📁 Repository Structure

✈️ Project Overview

The study analyses the aerodynamic behaviour of a DC-9-type T-tail aircraft in both normal cruise and deep stall conditions. Deep stall is a critical safety concern for T-tail configurations where the horizontal stabiliser becomes immersed in the separated wake of the main wing, leading to loss of pitch control.

Key Objectives:

1. Identify the optimal cruise angle of attack for maximum L/D ratio.
2. Evaluate deep stall characteristics at high AoA.
3. Compare fast panel methods (VSPAero) with high-fidelity CFD (Fluent).
4. Assess turbulence model performance (k-ω SST vs. k-kl transition).

🛠️ Computational Setup

Geometry & Mesh

• Aircraft: DC-9-like T-tail configuration.
• Half-model symmetry used to reduce computational cost.
• Mesh size: ~3.1 million cells (coarse due to computational constraints).
• Mesh type: Poly-hexcore with prism layers for boundary resolution.
• Mesh quality: Skewness < 0.66, orthogonality > 0.45.

Solver Settings

• Solver: ANSYS Fluent (density-based, explicit).
• Turbulence models: k-ω SST and k-kl transition.
• Mach number: 0.8 at 30,000 ft (ISA conditions).
• Reynolds number: ~2.06 × 10⁷ based on mean aerodynamic chord.

Boundary Conditions

• Inlet: Velocity inlet with AoA specification.
• Outlet: Pressure outlet.
• Walls: No-slip condition for aircraft surfaces.
• Symmetry: Symmetry plane for half-model.

📊 Key Findings

1. Cruise Performance:

   • Optimal L/D occurs at 2° angle of attack.
   • VSPAero over-predicts lift and under-predicts drag compared to CFD.

2. Deep Stall Analysis:

   • At 25° AoA, lift coefficient drops to near-zero.
   • Drag coefficient increases by an order of magnitude.
   • Horizontal tail experiences significant wake ingestion, confirming deep stall risk.

3. Tool Comparison:

   • VSPAero is useful for rapid trend analysis but lacks viscous flow modelling.
   • CFD provides physically realistic predictions, essential for safety-critical analyses like deep stall.

📈 Results Visualization

(Example plots to include in repo)

• Lift and drag coefficients vs. AoA.
• Pressure contours and streamlines at cruise and deep stall.
• Comparison bar charts: VSPAero vs. Fluent.

⚠️ Limitations & Notes

• Mesh resolution was limited (~3.1M cells); transition modelling requires finer near-wall resolution (y+ ≈ 1).
• Engine nacelles were omitted from the model due to an oversight in geometry preparation.
• k-ω SST model showed convergence issues; k-kl transition model performed more robustly.

🔗 References

1. Skybrary, Deep Stall – https://skybrary.aero/articles/deep-stall
2. OpenVSP Documentation – https://openvsp.org/wiki/doku.php
3. Anderson, J. D., Fundamentals of Aerodynamics (6th ed.), McGraw-Hill, 2017.
4. Menter, F. R., Two-Equation Eddy-Viscosity Turbulence Models for Engineering Applications, AIAA Journal, 1994.

👨‍🔬 Author

Saiyed Mohammad Mudassir
MSc Aerospace Computational Engineering, Cranfield University (2024–2025)
Project Supervisor: Tom Robin Teschner
Date: 2nd December 2024

📄 License

This project is shared for academic and research purposes. Please cite appropriately if used in further work.