1. Determining Airfoil Self-Noise Levels
Josh Merchant

2. Executive Summary
The goal of the project is to develop a better way to predict the level of noise that is produced by an airfoil in high velocity air
Completed – data preprocessing, model development, general testing and tuning of the model
To Do – fine tuning of the model, in depth performance analysis
Model is performing as well or better than the target of 0.89 Coefficient of Determination

3. Data
Data was taken from a NASA experiment to determine self-noise levels
Data needed to be scaled and centered to allow for learning from all variables
Order of the data was randomized
Original test run to minimize setup changes
Frequency (Hz)
Angle of Attack (Degrees)
Chord Length (Meters)
Free Stream Velocity (m/s)
Suction Side Displacement Thickness (m)
Sound Pressure (dB)
Average
6.78
0.14
50.86
0.01
124.84 Sd
5.92
0.09
15.57
6.90
Scaled
Sd Scaled 1

4. Method scikit learn MLPRegressor Used CS computers
Written in Python
Utilized numpy functions
Used CS computers
Small models, about 1000 samples
10 second run time without graphing
Iteratively optimized parameters
Made gross changes with scripts to see initial trends
Fine tuning the model by hand
Current best performance
Activation Function – Logistic
Nodes – 5 – 15 – 15 – 1
Solver Function – lbfgs
Learning Rate – Constant
Initial learning Rate – 0.001

5. Results Model has a Coefficient of Determination of 0.95
Model has a Mean Squared Error of 0.05
Performing better than target network which had
coefficient of Determination of 0.89
Mean Squared Error of 0.08
12 or less nodes resulted in underfitting
25 or more nodes resulted in overfitting

6. Discussion Data processing is Important Tuning Parameters Future
Non-Scaled and Non-Centered models had Negative Coefficient of Determination
Non-Randomized models had Negative Coefficient of Determination
Tuning Parameters
Not one dominant parameter
Different combinations have similar results
Future
Tweak Parameters
In Depth Analysis