The first experimental application of the Parametric Flutter Margin method for identification of aeroelastic instabilities is presented. The experiment was performed in two steps using a two degree-of-freedom wing segment mounted in the wind tunnel. First, the reference flutter and divergence conditions were found by increasing the free-stream velocity until the observed response diverged. Then, the system was stabilised according to the Parametric Flutter Margin methodology, and the flutter and divergence conditions of the original test model were identified positively while being in a stable regime demonstrating excellent agreement with the reference instability conditions. Although the new experimental methodology is not model based, the results were compared with a theoretical model showing good agreement as well. The acquired data demonstrates both the accuracy of the Parametric Flutter Margin method as well as its capability to test for aeroelastic instabilities, both flutter and divergence, in stable and predictable testing conditions.

The first experimental application of the Parametric Flutter Margin method for identification of aeroelastic instabilities is presented. The experiment was performed in two steps using a two degree-of-freedom wing model mounted in the wind tunnel. First, the reference flutter and divergence conditions were found by increasing the airstream velocity until the observed response diverged. Then, the system was stabilized according to the Parametric Flutter Margin methodology, and the flutter and divergence conditions of the original wing were identified positively while being in a stable regime demonstrating excellent agreement with the reference instability conditions. Although, the new experimental methodology is not model based, the results were compared with a theoretical model showing good agreement as well. The acquired data proves both the accuracy of the Parametric Flutter Margin method as well as its capability to test for aeroelastic instabilities, both flutter and divergence, in stable and predictive testing conditions.