Aerodynamic probe calibration using Gaussian process regression

During the calibration of an aerodynamic probe, the correlation between the present representative flow quantities of the fluid and the measurand is determined. Thus, a large number, sometimes several thousands, of different calibration points are set and measured, making this a very time-consuming process. The differences in the calibration data of similar constructed probes are very small. With the help of statistical methods, more precisely Gaussian process regressions, this similarity is exploited in order to use existing calibration data of different probes reducing the calibration time with sufficient reconstruction accuracy. Data from single-wire hot-wire probes and from five-hole probes are tested and show a very high reconstruction accuracy compared to the full calibration data set. The number of calibration points in the five-hole probe case is reduced by at least one order of magnitude with comparable reconstruction accuracy.