  We present a framework for airfoil shape optimization to reduce the trailing
edge noise for the design of wind turbine blades. Far-field noise is evaluated
using Amiet's theory coupled with the TNO-Blake model to calculate the wall
pressure spectrum and fast turn-around XFOIL simulations to evaluate the
boundary layer parameters. The computational framework is first validated using
a NACA0012 airfoil at zero angle of attack. Particle swarm optimization is used
to find the optimized airfoil configuration. The multi-objective optimization
minimizes the A-weighted overall sound pressure level at various angles of
attack, while ensuring enough lift and minimum drag. We compare classic
parametrization methods to define the airfoil geometry (i.e., CST) to a machine
learning method (i.e., a variational autoencoder). We observe that variational
autoencoders can represent a wide variety of geometries, with only four encoded
variables, leading to efficient optimizations, which result in improved optimal
shapes. When compared to the baseline geometry, a NACA0012, the
autoencoder-based optimized airfoil reduces by 3% (1.75 dBA) the overall sound
pressure level (with decreased noise across the entire frequency range), while
maintaining favorable aerodynamic properties in terms of lift and drag.
