The effect of wake passing on a flow separation in a low pressure turbine cascade

This paper describes an investigation into the effect that passing wakes have on a separation bubble that exists on the pressure surface and near the leading edge of a low pressure turbine blade. Previous experimental studies have shown that the behaviour of this separation is strongly incidence dependent and that it responds to its disturbance environment. The results presented in this paper examine the effect of wake passing in greater detail. Two dimensional, Reynolds averaged, numerical predictions are first used to examine qualitatively the unsteady interaction between the wakes and the separation bubble. The separation is predicted to consist of spanwise vortices whose development is in phase with the wake passing. However, comparison with experiments shows that the numerical predictions exaggerate the coherence of these vortices and also overpredict the time-averaged length of the separation. Nonetheless, experiments strongly suggest that the predicted phase locking of the vortices in the separation onto the wake passing is physical.

Bayesian Online Changepoint Detection

Changepoints are abrupt variations in the generative parameters of a data sequence. Online detection of changepoints is useful in modelling and prediction of time series in application areas such as finance, biometrics, and robotics. While frequentist methods have yielded online filtering and prediction techniques, most Bayesian papers have focused on the retrospective segmentation problem. Here we examine the case where the model parameters before and after the changepoint are independent and we derive an online algorithm for exact inference of the most recent changepoint. We compute the probability distribution of the length of the current ``run,'' or time since the last changepoint, using a simple message-passing algorithm. Our implementation is highly modular so that the algorithm may be applied to a variety of types of data. We illustrate this modularity by demonstrating the algorithm on three different real-world data sets.