Python

Objective Preliminary aerodynamic design of a hypothetical four-seat electric general aviation aircraft, with cruise efficiency as the primary design driver. Approach Built a Python workflow to batch-evaluate 170+ NACA airfoils through XFOIL, scoring each against a weighted performance matrix (lift-to-drag, stall behaviour, pitching moment) to select the wing’s baseline section. Refined the resulting 3D wing in XFLR5, iterating aspect ratio and taper ratio to maximise cruise L/D while keeping the design within realistic structural and geometric bounds. Ran a high-lift system sizing pass to satisfy take-off and landing stall requirements, alongside compressibility checks (critical Mach number) and a safety assessment including sizing of a two-stage ballistic parachute system. Result Cruise glide ratio: 31.46 Final grade: 96% Why this project This was the first time I treated airfoil selection as an optimisation problem rather than a lookup exercise — writing the scoring workflow forced me to think explicitly about how to trade off lift, drag, and stall margin against each other, not just read the numbers off a polar. It’s the project that pointed me toward CFD and design optimisation as the specific corner of aerodynamics I want to keep working in. ...

Objective A foundational study into how airfoil geometry drives aerodynamic performance — built to properly understand the fundamentals before relying on optimisation tools as a black box. Approach Used the XFOIL solver (via XFLR5) to systematically vary Reynolds number, Mach number, airfoil thickness, camber magnitude, and camber location across a set of NACA 4-digit airfoils. Analysed the resulting effects on lift, drag, stall behaviour, and overall aerodynamic efficiency. Built a Python automation workflow to run and compare geometries at scale, replacing manual XFLR5 sweeps with a repeatable script. This became the basis for the airfoil-selection workflow used in the wing optimisation project. Result Final grade: 99% Why this project This is where I built the habit of automating the analysis loop instead of running it by hand — a small decision that ended up shaping how I approach every aerodynamics project since. It also gave me a solid, first-principles grip on why camber and thickness distributions do what they do to a polar, rather than just knowing the trend. ...