October 5th, 2026

The tool combines six methods from propeller and electric-motor design theory.

1. Blade Element Momentum (BEM) theory
The blade is divided into radial elements. At each element the axial and tangential induction factors are iterated until the momentum change in the slipstream balances the aerodynamic forces on the blade section. This produces the local inflow angle, angle of attack, and section loads.

2. Minimum-induced-loss design (Larrabee / Betz)
The chord and twist distributions are chosen so that the slipstream velocity is constant along the blade — the Betz condition for minimum induced power. This is the same optimization used by Eugene Larrabee (1979) for hand-built racing propellers and is still the standard for high-efficiency propellers today.

3. NeuralFoil airfoil polars
Lift and drag coefficients at each blade section are predicted by NeuralFoil — a neural-network surrogate trained on hundreds of thousands of XFOIL solutions. It provides accurate 2D polars across the Reynolds-number range typical of small propellers (Re = 5×10⁴ to 5×10⁵) in milliseconds instead of seconds.

4. Snel 3D stall correction
In the rotating reference frame, stall is delayed by centrifugal and Coriolis effects. The Snel correction (Snel et al., 1994) blends the 2D post-stall lift back toward its inviscid value using a factor f(c/r) that depends on the local chord-to-radius ratio. This produces realistic force predictions beyond the static stall angle.

5. Prandtl tip and root loss factors
The finite number of blades means the flow cannot produce full circulation at the tip and root. Prandtl's loss factor corrects the momentum equations for this effect — the same model used in classical propeller theory.

6. Essen's rule (D²L) for motor sizing
The electromagnetic torque capability of an electric motor scales with rotor volume D²·L. Essen's rule relates required shaft power to stator geometry through an empirical output coefficient. The tool uses a coefficient of 300 kW/(m³·rev/s), validated against real racing outrunner motors (T-Motor 2207, SunnySky 2216), and derives stator diameter, stack length, and required KV.

Technical notes

• Language: Python (Flask backend, vanilla JS frontend)
• Main dependencies: AeroSandbox, NeuralFoil, NumPy, SciPy, CasADi
• Hosting: Docker container, self-hosted
• Source: open, running locally or on a private server

Reference
Larrabee, E. E. (1979). Practical Design of Minimum Induced Loss Propellers. SAE Technical Paper 790585.
Snel, H., Houwink, R., Bosschers, J. (1994). Sectional prediction of lift coefficients on rotating wind turbine blades in stall. ECN-C-93-052.

Propeller-Electric Brushless Motor Sizing Tool

Models used

This tool is based on my working Python scripts. It takes flight conditions as input and returns propeller geometry and performance (at the design advance ratio), as well as stator diameter, height, and KV. My primary goal was to help students estimate the required pitch angle for a propeller and match motor size without needing to handle heavy, complex code or download dependencies—while still fully understanding how the code works. So, here it is (making me so proud and happy!), and I’d appreciate your feedback!