Dragonfly Rotor Optimization using Machine Learning Applied to an OVERFLOW Generated Airfoil Database

NASA’s 4th New Frontiers Mission is the Titan Dragonfly relocatable lander. This coaxial quadrotor vehicle will be
launched on a rocket to Titan in 2028. Following a gravity assisted Earth flyby and an approximate 6-year transit,
Dragonfly will enter the Titan atmosphere around 2034 with the goal of exploring Titan’s pre-biotic chemistry and
habitability. The multirotor design for this unique application has continually evolved since 2016 with constraints
such as Titan’s cryogenic atmosphere at 95 Kelvin (-288 F), gravity 14% that of Earth’s, atmospheric density 440%
of standard sea-level air, and the inability to test the entire system together under all these conditions until the first
flight on Titan. This paper focuses on rotor design aspects of the Dragonfly lander and introduces a novel framework
for multirotor design optimization considering multiple flight conditions. The methodology leverages machine
learning methods and is demonstrated in the context of Dragonfly. A new OVERFLOW Machine Learning Airfoil
Performance (PALMO) database is first presented. PALMO is then wrapped inside a Bayesian optimization
framework and applied to a 4-rotor system (one side of the Dragonfly lander). Training data is generated on each
iteration of the optimization using the CAMRAD-II comprehensive analysis software to evaluate successive rotor
designs in multiple relevant flight conditions. An optimal design for the 4-rotor system was found with approximately
900 rotor designs analyzed in CAMRAD-II, which required 9 million queries of the PALMO surrogate models. This
demonstration case evaluated 10,000,000 potential candidate rotor designs in 5.5 hours on 114 CPU cores using
uniform inflow, and in 27.8 hours using the prescribed wake model. This work thus enables mid-fidelity rotor design
optimization without requiring access to high-performance computing.