Estimating Helicopter Noise Abatement Information with Machine Learning
Eric Greenwood, NASA Langley Research Center

Estimating Helicopter Noise Abatement Information with Machine Learning
- Presented at Forum 74 - Best Paper for this session
- 14 pages
- SKU # : 74-2018-0056
- Your Price : $30.00
Join or log in to receive the member price of $15.00!
Estimating Helicopter Noise Abatement Information with Machine Learning
Authors / Details: Eric Greenwood, NASA Langley Research CenterAbstract
Machine learning techniques are applied to the NASA Langley Research Center's expansive database of helicopter noise measurements containing over 1500 steady flight conditions for ten different helicopters. These techniques are then used to develop models capable of predicting the operating conditions under which significant Blade-Vortex Interaction noise will be generated for any conventional helicopter. A measure for quantifying the overall ground noise exposure of a particular helicopter operating condition is developed. This measure is then used to classify the measured flight conditions as noisy or not-noisy. These data are then parameterized on a nondimensional basis that defines the main rotor operating condition and are then scaled to remove bias. Several machine learning methods are then applied to these data. The developed models show good accuracy in identifying the noisy operating region for helicopters not included in the training data set. Noisy regions are accurately identified for a variety of different helicopters. One of these models is applied to estimate changes in the noisy operating region as vehicle drag and ambient atmospheric conditions are varied.
Recently Viewed Items
-
Estimating Helicopter Noise Abatement Information with Machine Learning
- Member Price :
- $15.00
- Your Price :
- $30.00