Results from a neural network study of the noise data from a full-scale XV-15 tilt-rotor are presented. Specifically, this database was acquired during the 1998 NASA Ames 80-by 120-foot wind tunnel test to establish the blade-vortex-interaction noise signature. The present study has three objectives: 1) To conduct a neural-network-based quality assessment of the noise data; 2) To obtain neural network representations of the noise data and to demonstrate their sensitivity to test conditions; 3) To obtain neural-network-based noise predictions. Overall, neural networks are successfully used to assess the quality of the noise data and to represent the complete database as well as to predict tilt rotor noise using the minimal amount of input data. As major findings, the data quality is found to be acceptable, and accurate neural network representations are obtained for the test-condition sensitivity cases.
Neural network representation of external tilt-rotor noise
Repräsentation der Schallerzeugung eines Schwenkrotors mit einem neuronalen Netz
Journal of the American Helicopter Society ; 47 , 2 ; 109-114
2002
6 Seiten, 10 Bilder, 10 Quellen
Article (Journal)
English
Neural Network Representation of External Tilt-Rotor Noise
Online Contents | 2002
|Advances in Tilt Rotor Noise Understanding
British Library Conference Proceedings | 1992
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