Online Monitoring of Data Fusion Results in Wind Tunnel Applications

Wind tunnel testing is an essential element in the aerodynamic assessment of various technical systems as it offers high accuracy at moderate cost compared to full scale testing. However, wind tunnel measurements usually provide information for specific parts of the flow field and may not quantify all quantities of interest. Hence, data fusion methods can be used to fill these gaps using information from simulation data. These data fusion methods are usually applied as a post-processing step. In contrast, they are applied in real-time during wind tunnel testing in this work. We introduce a framework for online monitoring and apply it to wind tunnel experiments with a wind turbine blade tip featuring highly separated flow and an isolated wing with significant aero-elastic effects. The online monitoring aims at complementing the measurement data by providing a deeper insight into the flow field. This is achieved by data driven methods, which predict distributed quantities and operational parameters from sparse pressure measurements. The presented framework achieves refresh rates, which are suitable for quasi-steady wind tunnel measurements.