Comparison of Dimensional Reduction Methods for Rotor Blade Parameterization

Dimensional reduction of data can be accomplished through various methods and has applications critical to machine learning and surrogate modeling. Within the rotorcraft community, leveraging these techniques allows for improved rotor parameterization and performance prediction. Machine learning models generally perform faster and better with lower input dimensions, so long as all necessary information is retained, making appropriate dimension reduction paramount. Data can also be arranged in a one-dimensional (concatenated/stacked) or two-dimensional arrays to take advantage of function correlations, and this arrangement may allow for greater reduction at lower reconstruction costs. Principal Component Analysis with a stacked input shape proves to be the most effective reduction method considered, with reconstruction accuracy being validated though a suite of mid-fidelity aerodynamic simulations. A blade geometry defined using 204 original parameters can be fully described using just 10 component parameters with the reconstructed blade maintaining performance figures within 1% of the original blade.