Multi-Model Ensemble Wake Vortex Prediction

  • Multi-Model Ensemble Wirbelschleppenvorhersagen

Körner, Stephan; Stumpf, Eike (Thesis advisor); Breitsamter, Ch. (Thesis advisor)

Köln : Deutsches Zentrum für Luft- und Raumfahrt (2017)
Book, Dissertation / PhD Thesis

In: Forschungsbericht / DLR, Deutsches Zentrum für Luft- und Raumfahrt 2017-44
Page(s)/Article-Nr.: xviii, 155 Seiten

Dissertation, RWTH Aachen University, 2017


As a response to lift, a complex flow pattern is shed from the wings of an aircraft that evolves into a pair of counter-rotating vortices. Due to the rolling momentum that the vortices may induce and the forces that they can exert they pose a potential hazard to following air traffic. To avoid dangerous incidents fast-time wake vortex models have been developed in the past that predict the vortex position and strength dependent on aircraft parameters and ambient conditions. This thesis investigates the capability to further enhance wake vortex forecasts by combining various independent models in a Multi-Model Ensemble, which has shown to increase the deterministic forecast skill and generate reliable probabilistic predictions in other applications. Therefore the fast-time models of NASA (APA 3.2, APA 3.4, TDP 2.1) and DLR (D2P) are combined by the methods Direct Ensemble Averaging, Reliability Ensemble Averaging and Bayesian Model Averaging. The ensemble output is validated with wake vortex measurements and compared to the respective best model.