Cross-flow turbines, also known as verticalaxis
turbines, use blades that rotate about an axis perpendicular
to the incoming flow to convert the kinetic energy
in moving fluid to mechanical energy. In this work, the
performance of a two-turbine array in a recirculating water
channel is modeled using Gaussian process regression. In
prior experiments, we optimized “coordinated control” set
points (equal tip-speed ratios with an azimuthal phase
offset between turbines) to maximize the power output 64
unique geometric configurations with the turbines counterrotating.
While this approach identified promising configurations
where turbine pair out-performed geometricallyidentical
turbines in isolation, the experiments were timeconsuming
to conduct. In this work, a Gaussian process
regression model is initialized with a subset of random
points from the geometric configuration space and returns
confidence intervals for the full parameter space. Subsequently
tested points are then chosen based on the regions
of the parameter space model with the highest uncertainty.
This is repeated until the model converges (i.e., the model
is unchanged with additional points tested). Results are
benchmarked against the experimental “truth”, but, in
future work, would actively guide experimental exploration
of high-dimensional spaces.