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

NASA’s fourth New Frontiers Mission is the Titan Dragonfly relocatable lander. This coaxial quadrotor vehicle is scheduled to launch on a rocket in 2028 with the goal of exploring Titan’s prebiotic chemistry and habitability. The multirotor design for this unique application has evolved to meet constraints such as Titan’s cryogenic atmosphere at 95 K (−288°F), gravity 14% that of Earth’s, atmospheric density 4.4 times of Earth’s standard sea level, and the inability to test the entire system under these conditions until the first flight on Titan. This paper focuses on rotor design aspects of the Dragonfly lander and introduces a framework for multirotor design optimization considering multiple flight conditions. A new OVERFLOW Machine Learning Airfoil Performance (PALMO) database is first presented. PALMO is then incorporated into a Bayesian optimization framework and applied to a four-rotor system (one side of the Dragonfly lander). Training data are generated on each iteration of the optimization using the CAMRAD-II comprehensive analysis to evaluate rotor designs over the planned mission profile. An optimal design for the four-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 candidate rotor designs in 5.5 h on 114 CPU cores using uniform inflow, and in 27.8 h using the prescribed wake model.