Automated computational fluid dynamics analysis of airfoil geometries across multiple angles of attack using SU2 RANS with hybrid C-grid meshing.
Airfoils are the fundamental building block of aerodynamic design. Every surface that moves through air or has air moving over it relies on the same physics: pressure differences created by carefully shaped cross-sections generate lift, produce drag, and determine efficiency. From aircraft wings and turbine blades to racing car spoilers and drone rotors, airfoil performance dictates the limits of what these systems can achieve.
The NACA 4-digit series, developed by the National Advisory Committee for Aeronautics in the 1930s, remains one of the most studied and trusted airfoil families in existence. Its parametric formulation - defined by camber, camber position, and thickness - makes it an ideal subject for automated CFD analysis. Understanding how these geometric parameters affect aerodynamic coefficients under various flow conditions is essential knowledge for any aerospace or mechanical engineer.
Computational Fluid Dynamics has transformed how engineers approach aerodynamic design. Before CFD, the design cycle relied almost exclusively on wind tunnel testing - expensive, time-consuming, and limited in the number of configurations that could be tested. A single wind tunnel session might cost tens of thousands of dollars and take weeks to set up. CFD changes this calculus entirely.
With an automated pipeline like the one powering this site, an engineer can evaluate 10, 50, or 100 airfoil variants in the time it would take to prepare a single wind tunnel model. Each variant generates the same quantitative metrics: lift coefficient ($C_l$), drag coefficient ($C_d$), lift-to-drag ratio, flow separation patterns, pressure distributions, and boundary layer behavior. This data feeds directly into design decisions without bottlenecking on test infrastructure.
The NACA 4-digit family is inherently parametric - three numbers define the entire geometry. CFD enables systematic exploration of this parameter space: How does increasing thickness from 12% to 15% affect stall angle? What happens to drag when you move the camber position? These questions can be answered in hours instead of months.
Every aircraft wing is a collection of airfoil sections. CFD analysis determines the optimal airfoil for each spanwise station - thicker root sections for structural integrity, thinner tips for low drag. The NACA 0012 results here (sweeping 0° to 16° AoA) mirror the analysis a wing designer would perform for the horizontal stabilizer or symmetric wing sections. The lift curve slope of 0.109 per degree validates that our solver captures the correct physics.
Rear wings on high-performance vehicles are essentially inverted airfoils generating downforce. The same CFD pipeline applies: choose an airfoil, sweep angles of attack, compute CL/CD. A spoiler operating at 8° AoA (our High Lift regime) might generate significant downforce at the cost of drag - the tradeoff every motorsport engineer optimizes. Our drag polar analysis directly maps this design space.
Wind turbine blades are airfoils operating across a wide range of Reynolds numbers and angles of attack. Unlike aircraft wings, they experience constantly changing relative wind as the blade rotates. CFD analysis at multiple AoA regimes (from attached flow at 0° to deep stall at 16°) helps turbine designers avoid operating in separated flow conditions that reduce power output and increase fatigue loads.
CFD Airfoil Explorer is an automated pipeline that generates high-quality C-grid meshes (Gmsh), solves compressible RANS equations (SU2, Spalart-Allmaras), and produces publication-ready visualizations - all from a single command. Each airfoil analysis includes:
The pipeline is designed to be extensible: any NACA 4-digit airfoil can be substituted, run through the same workflow, and added to the atlas.
The Implementation page documents the full code architecture: the six physics/ modules (geometry, meshing, solving, post-processing, analysis, validation, and optimization), the SU2 configuration reference with all solver parameters used in production, and expandable source blocks for every module.