OMB Individually Reported
Graph Neural Networks for Airfoil Performance Prediction
Low riskExact public inventory row
Description
We are investigating the use of Graph Convolutional Neural Networks to learn relationship between airfoil coordinates and predict the performance for aerodynamics analysis. Inputs include the shape of the airfoil and outputs are the coefficient of lift, drag, and moment.
Detailed example
Predictions of blade loss
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
The impact is we have a new type of neural network architecture that we can potentially use for other projects.
Controls / human review
ATO: Not reported; PIA: Not published