Laminar Flow Control by means of a suction system appears to be one of the most promising technologies to significantly reduce the fuel consumption and hence, pollutant emission of modern large transport aircraft through drag reduction. The consortium, composed of a renowned University (TUBS) and world leading wind tunnel provider DNW, performs high-quality large-scale wind tunnel tests with a full-scale vertical tail plane equipped with a hybrid laminar flow control (HLFC) system, which is based on the tailored skin single duct (TSSD) concept.
The DNW-LLF is one of the world’s largest low speed facilities and offers unique capabilities for wind tunnel tests with large scale models at representative flow conditions. The framework provided for by ALVAR includes state-of-the-art data acquisition, aerodynamic data, lift, drag, static pressure distributions, transition location with infrared and hot films, and the extremely high flow quality of the LLF 8x6 m test section configuration.
One of the root problems in wind tunnel tests with hybrid laminar flow control is that it is technically unfeasible to actually measure the local suction flow rate over individual surface areas, but only the overall suction flow rate including a number of additional values, such as pressures at certain locations, is feasible. However, the local flow rate can be post-processed based on a complex measurement chain, employing data mappings (e.g. calibration of the suction skin), interpolation/extrapolation schemes of the pressure distribution, and geometry measurements. This makes the local flow rate prone to the propagation of uncertainties and errors. ALVAR therefore provides a scientific analysis of the whole measurement chain to establish a precise uncertainty of the local flow rate using Bayesian inference. Based on this, and through analysis, design, optimization and implementation of a specific main mass flow meter including a proven pumping system to provide stable suction rates a comprehensive quantification of boundary layer suction is performed by ALVAR.