Statistical Analysis of Wake Vortex Data Towards Finding a Criterion for Safe Reduction of Aircraft Departure Separations

A long term measurement campaign of wake vortices from departing aircraft has been conducted at major European airports. Meteorological as well as aircraft trajectory data have been collected simultaneously. Data from the various sensors have been fused and correlated in order to characterize the vortices in an operational and meteorological context. The objective of this campaign is to elaborate a criterion that safely allows re-ducing departure separations under certain weather conditions. This paper describes some first results that were obtained from analyzing the wake vortex data and the weather data accordingly. Modern data min-ing techniques have been applied by which characteristic wake vortex types could be discovered allowing to generalize findings within such wake vortex clusters. Comparisons between today’s baseline scenario and the criterion-based scenario reveal the feasibility of reduced departure separations while preserving the present safety level. Nomenclature Γ = wake vortex circulation (m2/s) t = wake vortex age (s) X = wake vortex dataset J = clustering objective function x j = wake vortex feature vector vi = wake vortex prototype vector n = number of data elements c = number of clusters di j = dissimilarity measure m = fuzzifier ui j = fuzzy membership degree Rp = feature space p = feature space dimensionality Subscripts i = variable number j = variable number k = variable number I