DGLR-Publications Database - Detailview

Title:

Rotor Loads Prediction via Virtual Sensors - Blending Physics and AI

Author(s):
F. Midei, A.A. Trezzini, M. Zilletti, C. Sbarufatti
Abstract:
The paper focuses on the prediction of the load generated by a helicopter lead-lag damper, which is not directly measured in production helicopters but is a valuable parameter for rotor health monitoring and maintenance scheduling. The approach blends the prediction obtained from an artificial intelligence model, called the black box, with the prediction made by a physical model, called the white box. The black box is used to predict the blade motions from the aeromechanical parameters of the helicopter measured in flight. Given the complexity of helicopter aeromechanics and the large data set acquired on helicopter prototypes, an artificial intelligence modelling approach is well suited for this task. The damper force is finally predicted using a white box model describing the core physical behaviour of the damper, which takes as inputs the blade motions predicted by the black box. In this case, the damper dynamics can be described by a relatively simple physical model, yet giving an acceptable level of accuracy. A comparison between the predicted and measured damper loads on a helicopter prototype demonstrates the approachs effectiveness.
Event:
49th European Rotorcraft Forum 2023, Bückeburg, 2023
Mediatype:
Conference Paper
Language:
englisch
Format:
21,0 x 29,7 cm, 8 Pages
Published in:
DGLR-Bericht, 2023, 2023-01, 49th European Rotorcraft Forum 2023 - Proceedings; S.1-8; 2023; Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V., Bonn
Price:
NA
ISBN:
ISSN:
Comment:
Classifikation:
Keywords:
Available:
Bestellbar
Published:
2023


This Document is part of a superordinate publication:
49th European Rotorcraft Forum 2023 - Proceedings