CCBlade.jl

Summary: A blade element momentum method for propellers and turbines.

Author: Andrew Ning

Features:

• Methodology is provably convergent (see http://dx.doi.org/10.1002/we.1636 although multiple improvements have been made since then)
• Prandtl hub/tip losses (or user-defined losses)
• Glauert/Buhl empirical region for high thrust turbines
• Convenience functions for inflow with shear, precone, yaw, tilt, and azimuth
• Can do airfoil corrections beforehand or on the fly (Mach, Reynolds, rotation)
• Allows for flow reversals (negative inflow/rotation velocities)
• Allows for a hover condition (only rotation, no inflow) and rotor locked (no rotation, only inflow)
• Compatible with AD tools like ForwardDiff
• Callable from Python, with derivatives included (optionally)

Installation:

] add CCBlade

Documentation:

The documentation contains

• A quick start tutorial to learn basic usage,
• Guided examples to address specific or more advanced tasks,
• A reference describing the API,
• Theory in full detail.
• An example calling CCBlade.jl from Python, optionally with derivatives.

Run Unit Tests:

pkg> activate .
pkg> test

Citing:

Ning, A., “Using Blade Element Momentum Methods with Gradient-Based Design Optimization,” Structural and Multidisciplinary Optimization, Vol. 64, No. 2, pp. 994–1014, May 2021. doi:10.1007/s00158-021-02883-6