The visualization of the temperature distribution on a whole wind tunnel model is possible using Temperature Sensitive Paint (TSP). TSP measurement method is based on the dependence of the emission intensity or decay time of its luminescence on the temperature, caused by thermal quenching. One major area of interest for such technique is the examination of the laminar-to-turbulent boundary-layer transition behavior on wind tunnel models. However, the naturally established adiabatic wall temperature difference caused by the recovery process is typically too small to be detected with TSP, especially in low-speed tests. Therefore, the TSP technique requires an increase of the adiabatic wall temperature difference for transition detection measurements. The basic working principle of this approach is the imposition of a heat flux between the flow and the surface of the wind tunnel model. In the previous work carbon nanotubes (CNT) were presented as a source for electrical heating in order to generate temperature differences between laminar and turbulent boundary layers which are sufficient for the TSP technique. CNT has several desirable properties: for example a very high electric conductivity, and its ease of applicability as a coating with a thickness of few micrometers, which enables a successful combination with TSP for transition detection measurements in the wind tunnel environment (called cntTSP).
One big advantage of cntTSP is that the boundary-layer transition can be detected without changing the flow parameters. In addition, based on its capability of constant model surface heating, the cntTSP has a potential to be used for the dynamic visualization of boundary-layer transition, leading to the application of cntTSP in continuous model angle-sweep tests. The use of cntTSP in the angle-sweep tests is strongly desirable by the industrial partners in order to improve the data productivity of wind tunnel tests.
This work reports investigation of the transition detection methods in pitch-sweep wind tunnel tests for improving the reliability and the data productivity with various measurement and data reduction methods