DGLR-Publications Database - Detailview
Title:
Deep Learning Surrogate Model for Rotorcraft Aeroacoustic Simulations using a Geodesic Convolutional Network
Author(s):
B. Erwee, A. Scandroglio, N. Sanguini, T. Benacchio, F. Di Cintio
Abstract:
This paper presents a new state of the art surrogate assisted aeroacoustics toolchain, with a deep learning surrogate model able to accurately predict blade pressure distributions for helicopter main rotors in various flight conditions. By training a state of the art geodesic convolutional neural network, the surrogate model is able to accurately predict new cases in 0.1s with an R2 of 99% - 10,000 faster than the existing tool. Surrogate models have been built for four LH platforms, and a multi-blade model developed which showed very promising rotor performance predictions for an unseen blade design. The helicopter aeroacoustic tool chain can now run in just 8 minutes, down from 8 hours. In future work, the training of the surrogate models will be extended, to consider a wider flight envelope of flight conditions, and grow its ability to predict unseen rotor blade performance.
Event:
49th European Rotorcraft Forum 2023, Bückeburg, 2023
Mediatype:
Conference Paper
Language:
englisch
Format:
21,0 x 29,7 cm, 13 Pages
Published in:
DGLR-Bericht, 2023, 2023-01, 49th European Rotorcraft Forum 2023 - Proceedings; S.1-13; 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 publication083:
49th European Rotorcraft Forum 2023 - Proceedings