Interpretation of measured profile losses in unsteady wake-turbine blade interaction studies

The interaction of wakes shed by a moving bladerow with a downstream bladerow causes unsteady flow. The meaning of the freestream stagnation pressure and stagnation enthalpy in these circumstances has been examined using simple analyses, measurements and CFD. The unsteady flow in question arises from the behaviour of the wakes as so-called negative-jets. The interactions of the negative-jets with the downstream blades lead to fluctuations in static pressure which in turn generate fluctuations in the stagnation pressure and stagnation enthalpy. It is shown that the fluctuations of the stagnation quantities created by unsteady effects within the bladerow are far greater than those within the incoming wake. The time-mean exit profiles of the stagnation pressure and stagnation enthalpy are affected by these large fluctuations. This phenomenon of energy separation is much more significant than the distortion of the time-mean exit profiles that is caused directly by the cross-passage transport associated with the negative-jet, as described by Kerrebrock and Mikolajczak. Finally, it is shown that if only time-averaged values of loss are required across a bladerow, it is nevertheless sufficient to determine the time-mean exit stagnation pressure.

A Physical Interpretation of Stagnation Pressure and Enthalpy Changes in Unsteady Flow

This paper provides a physical interpretation of the mechanism of stagnation enthalpy and stagnation pressure changes in turbomachines due to unsteady flow, the agency for all work transfer between a turbomachine and an inviscid fluid. Examples are first given to illustrate the direct link between the time variation of static pressure seen by a given fluid particle and the rate of change of stagnation enthalpy for that particle. These include absolute stagnation temperature rises in turbine rotor tip leakage flow, wake transport through downstream blade rows, and effects of wake phasing on compressor work input. Fluid dynamic situations are then constructed to explain the effect of unsteadiness, including a physical interpretation of how stagnation pressure variations are created by temporal variations in static pressure; in this it is shown that the unsteady static pressure plays the role of a time-dependent body force potential. It is further shown that when the unsteadiness is due to a spatial nonuniformity translating at constant speed, as in a turbomachine, the unsteady pressure variation can be viewed as a local power input per unit mass from this body force to the fluid particle instantaneously at that point. © 2012 American Society of Mechanical Engineers.

A Maximum-Likelihood Interpretation for Slow Feature Analysis

The brain extracts useful features from a maelstrom of sensory information, and a fundamental goal of theoretical neuroscience is to work out how it does so. One proposed feature extraction strategy is motivated by the observation that the meaning of sensory data, such as the identity of a moving visual object, is often more persistent than the activation of any single sensory receptor. This notion is embodied in the slow feature analysis (SFA) algorithm, which uses “slowness” as an heuristic by which to extract semantic information from multi-dimensional time-series. Here, we develop a probabilistic interpretation of this algorithm showing that inference and learning in the limiting case of a suitable probabilistic model yield exactly the results of SFA. Similar equivalences have proved useful in interpreting and extending comparable algorithms such as independent component analysis. For SFA, we use the equivalent probabilistic model as a conceptual spring-board, with which to motivate several novel extensions to the algorithm.