Prediction of On-Disk Velocity Across a Coaxial Rotor with XGBoost

Authors

Ethan Genter, Washington University in St. LouisFollow
Cory Seidel, Washington University in St. LouisFollow
David Peters, Washington University in St. LouisFollow

Abstract

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.

Document Type

Final Report

Author's School

McKelvey School of Engineering

Author's Department

Mechanical Engineering and Materials Science

Class Name

Mechanical Engineering and Material Sciences Independent Study

Date of Submission

1-7-2020

Recommended Citation

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.
https://openscholarship.wustl.edu/mems500/112