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.

Pattern formation and spatial solitons in bistable liquid-crystal microcavities

We report on spatial pattern formation, and appearances of 'optical bullet holes' in single-mode microcavities that are filled with liquid-crystals, when pumped above the cavity resonance frequency. These phenomena only occur beyond the bistability threshold. ©2002 Optical Society of America.

Slowness and sparseness lead to place, head-direction, and spatial-view cells.

We present a model for the self-organized formation of place cells, head-direction cells, and spatial-view cells in the hippocampal formation based on unsupervised learning on quasi-natural visual stimuli. The model comprises a hierarchy of Slow Feature Analysis (SFA) nodes, which were recently shown to reproduce many properties of complex cells in the early visual system []. The system extracts a distributed grid-like representation of position and orientation, which is transcoded into a localized place-field, head-direction, or view representation, by sparse coding. The type of cells that develops depends solely on the relevant input statistics, i.e., the movement pattern of the simulated animal. The numerical simulations are complemented by a mathematical analysis that allows us to accurately predict the output of the top SFA layer.