A Stochastic Detection Models Comparison in Turbulent Flow Events

The need to establish the downwind fluid dynamic field of aerodynamic bodies subjected to a given velocity field is well known, to verify their aerodynamic characteristics. In this context, different techniques allow us to establish the characteristics of the field. It is almost always necessary to carry out quantitative determinations to describe the field correctly, particularly when the field is made up of turbulent wakes. In this sense, in the experimental field, it is common to use hot-wire anemometry techniques, which have great capabilities to quantify high-frequency events. Previous work has analyzed the determination of changes in hot-wire anemometry signals for the detection of events in turbulent flows with different models, based on stochastic algorithms (CPM ‐ Change Point Model).The present work aims to compare the results obtained previously with the application of different CPM models developed. Previously applied and evaluated measurements are used, the implementation of the models is carried out and the results are compared. All the algorithms used can detect changes in data that do not have a known distribution, i.e. non-parametric distributions, which are typical for turbulent flow field signals. Measurements of the fluctuating components of the wind tunnel velocity at a specific point are considering. The signals used correspond to periodic detachments downstream of a flow control device (Gurney mini-flap) at the trailing edge of an airfoil. The results show which are the best models to use for the experimental detection of such turbulent events in the flow field.