DGLR-Publications Database - Detailview

Title:

Development and Application of Improved Tandem Neural Networks for Inverse Design of Rotorcraft Airfoil

Author(s):
A. Anand, K. Marepally, B. Lee, J.D. Baeder
Abstract:
Designing an airfoil shape with specific performance characteristics is a fundamental problem in the field of rotorcraft blade design. Traditional aerodynamic design methodologies often involve iterative optimization of the shape using low-fidelity modeling techniques during the early design phase, owing to the demanding computational costs of high-fidelity adjoint-CFD based optimization. In this study, an efficient accurate approach using Deep Neural Networks for the inverse design of rotorcraft airfoils is investigated. We leverage the Tandem Neural Network (T-NN) architectures to design the airfoil for a required performance curves (lift, lift-to-drag ratio, and pitching moment) in a novel and efficient way. The T-NN architecture allows for a modified and flexible cost function making them highly efficient and accurate for the inverse design of airfoils. Three different improvements to the T-NN architecture are proposed in this work to allow for improved accuracy and better constraint handling capabilities, enabling a paradigm shift in the rotorcraft component design methodologies.
Event:
49th European Rotorcraft Forum 2023, Bückeburg, 2023
Mediatype:
Conference Paper
Language:
englisch
Format:
21,0 x 29,7 cm, 17 Pages
Published in:
DGLR-Bericht, 2023, 2023-01, 49th European Rotorcraft Forum 2023 - Proceedings; S.1-17; 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