High-Fidelity Remote Sensing: Optimizing Acoustic Tomography for Wind Turbine Wake Measurements

Acoustic tomography is an emerging remote sensing methodology based on an optimal linear mapping between the observed times of flight of acoustic signals between an array of speakers and microphones and the turbulent velocity and temperature fields within. Most often, this is accomplished by defining an optimal stochastic inverse operator that relates modeled and observed velocity and temperature fluctuations, but relies heavily on an assumed distribution of their covariances, each parameterized by a characteristic length scale and standard deviation. This work details the accuracy of the estimated fields to their ground truth values, shown by large eddy simulations. Sensitivity of the method and the retrieved fluctuating fields is contained in the Jacobian of the reconstruction error, describing optimal parameter values and the tolerance of the methods to noise representative of measurement error. In the simulated ABL, the Gaussian distribution of covariances is a good approximation to first order. However, when applying acoustic tomography to wind turbine wakes and other industrial flows, many of the underlying assumptions may need to be revisited to ensure that no biases are introduced to the measured turbulent fields.