A rotorcraft in-flight ice detection framework using computational aeroacoustics and Bayesian neural networks

This work develops a novel ice detection framework specifically suitable for rotorcraft using computational aeroacoustics and Bayesian neural networks. In an offline phase of the work, the acoustic signature of glaze and rime ice shapes on an oscillating wing are computed. In addition, the aerodynam...

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Bibliographic Details
Published in:Structural and multidisciplinary optimization 2023-09, Vol.66 (9), p.197, Article 197
Main Authors: Morelli, Myles, Hauth, Jeremiah, Guardone, Alberto, Huan, Xun, Zhou, Beckett Y.
Format: Article
Language:English
Subjects:
Acoustic mapping
Acoustics
Advanced Optimization Enabling Digital Twin Technology
Aerospace engineering
Aircraft accidents & safety
Aviation
Bayesian analysis
Business metrics
Computational aeroacoustics
Computational Mathematics and Numerical Analysis
Decision making
Engineering
Engineering Design
Flight
Indicators
Neural networks
Research Paper
Rotary wing aircraft
Safety critical
Theoretical and Applied Mechanics
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Summary:This work develops a novel ice detection framework specifically suitable for rotorcraft using computational aeroacoustics and Bayesian neural networks. In an offline phase of the work, the acoustic signature of glaze and rime ice shapes on an oscillating wing are computed. In addition, the aerodynamic performance indicators corresponding to the ice shapes are also monitored. These performance indicators include the lift, drag, and moment coefficients. A Bayesian neural network is subsequently trained using projected Stein variational gradient descent to create a mapping from the acoustic signature generated by the iced wings to predict their performance indicators along with quantified uncertainty that is highly important for time- and safety-critical decision-making scenarios. While the training is carried out fully offline, usage of the Bayesian neural network to make predictions can be conducted rapidly online allowing for an ice detection system that can be used in real time and in-flight.
ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-023-03610-z