Recent updates in ﬁnite state inﬂow models to solve multi-rotor systems has come at the expense of extra computation time requirements, especially for higher harmonic cases. A potential solution to counter the lengthy time requirements is the application of machine learning algorithms to ﬁt to velocity distributions and predict future distributions. In this paper, we look at XGBoost as a potential application of machine learning to predict accurate velocity distributions across the rotor disk.
Mechanical Engineering and Material Sciences Independent Study
Date of Submission
Genter, Ethan; Seidel, Cory; and Peters, David, "Prediction of On-Disk Velocity Across a Coaxial Rotor with XGBoost" (2020). Mechanical Engineering and Materials Science Independent Study. 112.