Aircraft Design Optimizer
OpenFOAM → graph neural network → thousands of airliners → the ones that burn less, fly faster and land slower

The mission

Requirements (a design that misses one is not certifiable)

Stability, stall, trim, structure

Search 13 variables: wing section (6), area, aspect ratio, taper, sweep, twist, cruise Mach, wing position

Or start from

Design an airliner the way Airbus and Boeing start one

Every design is a whole single-aisle airliner (A320 / 737 class): the wing's airfoil (6 shape parameters, learned from OpenFOAM by a graph network), its area, aspect ratio, taper, sweep, twist and position, and the cruise Mach. Each one flies its mission (cruise at Mach 0.70–0.82, approach and landing) and goes through a design review:

Less impactg of CO₂ per passenger-km over the design mission (Breguet, turbofan fuel burn)
Fastercruise Mach, with the wave drag of the shocks that come with it (Korn equation)
Lands slowerapproach speed with slats and flaps: shorter runways, less noise
Certifiable36 m gate span, 1.3 g buffet margin, fuel in the wing, static margin, stall at the root, trim

The reference is an A320ceo-class airliner (wing 122.6 m², span 34.1 m, 25° sweep, Mach 0.78) with a conventional NACA 2412 section.

No search yet

Press “Find designs”.

API

Run the search, evaluate a design, get its flow field or a ready OpenFOAM case. Interactive documentation: /docs.

Python (PINNeAPPle)