The Navier-Stokes equations govern every fluid flow in existence, yet no general analytical solution exists in 3D. CFD discretises these equations and solves them numerically — giving aerospace engineers the ability to simulate aerodynamics without building a single physical model.
Every CFD solver numerically approximates the solution to the Navier-Stokes equations. Understanding these equations is essential to using any CFD tool correctly.
Unsteady / local acceleration. Zero for steady RANS. Critical for flutter, gust response, rotating machinery.
Convective acceleration. The nonlinear term responsible for turbulence and why no general solution exists.
Pressure gradient force. Fluid accelerates from high to low pressure. The primary lift-generating mechanism.
Viscous diffusion. Significant only in the thin boundary layer adjacent to walls (y⁺ ≤ 1 region).
Body force (gravity). Often negligible in external aerodynamics but important for buoyancy-driven flows.
Resolving every turbulent eddy requires DNS at a cost proportional to Re³ — infeasible at aircraft Reynolds numbers. RANS models introduce turbulent viscosity to close the equations at a fraction of the cost.
The k-ω Shear Stress Transport (SST) model by Menter blends k-ε in the freestream (where it performs well) with k-ω in the near-wall boundary layer (where k-ω is more accurate). It is the standard model for external aerodynamics because it accurately predicts flow separation and adverse pressure gradient behaviour. Two transport equations are solved: one for turbulent kinetic energy k and one for specific dissipation rate ω. SST is the recommended starting model for any aerofoil or aircraft simulation in ANSYS Fluent.
The k-ε model solves for turbulent kinetic energy k and dissipation rate ε. It performs well in the freestream and for fully turbulent internal flows (pipes, ducts, jet mixing). It over-predicts separation in adverse pressure gradients near walls and is not recommended for external aerodynamics. The Realizable k-ε variant improves on the standard model by satisfying physical realizability constraints. Often used as a first-pass solver to initialise SST runs.
LES resolves eddies larger than the mesh filter width directly and only models the small sub-grid scales. It captures unsteady flow phenomena that RANS cannot — vortex shedding, separated shear layers, and jet noise. Cost scales as Re^1.8 vs Re³ for DNS, making it feasible for moderate Reynolds numbers. Detached Eddy Simulation (DES) hybridises RANS near walls with LES in separated regions — an excellent compromise for high-AoA separated flows and store separation problems.
Mesh quality determines solution accuracy more than solver choice. The dimensionless wall distance y⁺ = u*·y/ν must be ≤ 1 for low-Reynolds RANS to resolve the viscous sublayer. Cells with skewness above 0.9 or orthogonality below 0.1 cause solver divergence. Structured hexahedral meshes give highest accuracy per cell count. Hybrid meshes use prismatic layers near walls and unstructured tetrahedra elsewhere. A mesh independence study — showing solution does not change with further refinement — is mandatory before trusting any CFD result.
A CFD study follows five sequential phases. Errors in early phases propagate through to final results — garbage in, garbage out applies absolutely in CFD.
Define the aerofoil or aircraft geometry. For a NACA profile: import surface coordinates, create the fluid domain extending 20 chord lengths upstream and 30 downstream, and Boolean-subtract the geometry from the fluid block. Named Selections: aerofoil_wall, inlet, outlet, far-field.
Generate the computational mesh. Target 80,000–200,000 quad cells for a 2D RANS aerofoil. Apply inflation layers near the wall targeting y⁺ ≤ 1 with growth ratio ≤ 1.2. Check quality metrics: max skewness < 0.9, min orthogonality > 0.1. Poor mesh is the leading cause of solver divergence.
Set boundary conditions in Fluent: Velocity Inlet (V = 50 m/s, Tu = 0.1%), Pressure Outlet (gauge = 0 Pa), Wall (no-slip). Select k-ω SST turbulence model. Enable Realizable k-ε as a comparison run. These conditions define the physical problem — wrong BCs give a correct solution to the wrong problem.
Run the solver. Monitor residuals (target < 1×10⁻⁴) and force coefficients CL and CD. If CL oscillates without settling, the flow is unsteady and requires a transient simulation. A well-setup 2D RANS aerofoil converges in 200–400 iterations. Initialise with Hybrid Initialization.
Extract CL, CD, and the Cp distribution. Compare the Cp plot against NACA experimental data or XFOIL. If agreement is not within 5–10%, revisit mesh quality and boundary conditions before using results for any design decision. Wall y⁺ contours confirm near-wall mesh adequacy.
Visualise flow fields, mesh generation, and turbulence models — the concepts made visible.
The physics that CFD simulates — lift, drag, boundary layers, and shock waves. Understand the theory before running simulations.
Inlet aerodynamics, nozzle flows, and combustor CFD — propulsion and CFD go hand in hand.
The CL and CD values your CFD produces feed directly into flight mechanics performance calculations.
Import Cp distributions from Fluent into MATLAB, integrate for CL and CD, and validate against NACA experimental data.
Turbulence model sensitivity studies, supersonic inlet simulations, NACA aerofoil parametric studies — all with defined methodologies.
New to CFD? Start with the broader picture of aerospace engineering disciplines and where CFD fits in.
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