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.

AI in the 21st century - With historical reflections

The discipline of Artificial Intelligence (AI) was born in the summer of 1956 at Dartmouth College in Hanover, New Hampshire. Half of a century has passed, and AI has turned into an important field whose influence on our daily lives can hardly be overestimated. The original view of intelligence as a computer program - a set of algorithms to process symbols - has led to many useful applications now found in internet search engines, voice recognition software, cars, home appliances, and consumer electronics, but it has not yet contributed significantly to our understanding of natural forms of intelligence. Since the 1980s, AI has expanded into a broader study of the interaction between the body, brain, and environment, and how intelligence emerges from such interaction. This advent of embodiment has provided an entirely new way of thinking that goes well beyond artificial intelligence proper, to include the study of intelligent action in agents other than organisms or robots. For example, it supplies powerful metaphors for viewing corporations, groups of agents, and networked embedded devices as intelligent and adaptive systems acting in highly uncertain and unpredictable environments. In addition to giving us a novel outlook on information technology in general, this broader view of AI also offers unexpected perspectives into how to think about ourselves and the world around us. In this chapter, we briefly review the turbulent history of AI research, point to some of its current trends, and to challenges that the AI of the 21st century will have to face. © Springer-Verlag Berlin Heidelberg 2007.

Numerical analysis of the settlement induced damage to Palazzo Loggia in Brescia

One of the main causes of failure of historic buildings is represented by the differential settlements of foundations. Finite element analysis provides a useful tool for predicting the consequences of given ground displacements in terms of structural damage and also assesses the need of strengthening techniques. The actual damage classification for buildings subject to settlement bases the assessment of the potential damage on the expected crack pattern of the structure. In this paper, the correlation between the physical description of the damage in terms of crack width and the interpretation of the finite element analysis output is analyzed. Different discrete and continuum crack models are applied to simulate an experiment carried on a scale model of a masonry historical building, the Loggia Palace in Brescia (Italy). Results are discussed and a modified version of the fixed total strain smeared crack model is evaluated, in order to solve the problem related to the calculation of the exact crack width.