Parameter Exploration of a Two-Turbine Array Under Advanced Control Strategies

Cross-flow turbines, also known as vertical-axis turbines, use blades that rotate about an axis perpendicular to the incoming flow to convert the kinetic energy in a moving fluid into mechanical energy. Arrays of cross-flow turbines under advanced control strategies have been shown to outperform equivalent turbines in isolation by up to 30%. The array performance is dependent on a high-dimensional parameters space. This parametric dependence is challenging to characterize, as traditional uniform sampling quickly becomes prohibitively expensive. In this work, we implement a real-time active learning strategy, based on Gaussian Process Regression, to accurately and efficiently sample and model the high-dimensional parameter space of turbines operating in a recirculating water channel. This model-based, hardware-in-the loop experimental approach results in a surrogate model that may be used for future optimization and control efforts.