67 resultados para Brain cooling


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This paper presents a time-stepping shaker modeling scheme. The new method improves the accuracy of analysis of armature-position-dependent inductances and force factors, analysis of axial variation of current density in copper plates (short-circuited turns), and analysis of cooling holes in the magnetic circuit. Linear movement modeling allows armature position to be precisely included in the shaker analysis. A more accurate calculation of eddy currents in the coupled circuit is in particular crucial for the shaker analysis in a mid-or high-frequency operation range. Large currents in a shaker, including eddy currents, incur large Joule losses, which in turn require the use of a cooling system to keep temperature at bay. Sizable cooling holes have influence on the saturation state of iron poles, and hence have to be properly taken into account.

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The application of automated design optimization to real-world, complex geometry problems is a significant challenge - especially if the topology is not known a priori like in turbine internal cooling. The long term goal of our work is to focus on an end-to-end integration of the whole CFD Process, from solid model through meshing, solving and post-processing to enable this type of design optimization to become viable & practical. In recent papers we have reported the integration of a Level Set based geometry kernel with an octree-based cut- Cartesian mesh generator, RANS flow solver, post-processing & geometry editing all within a single piece of software - and all implemented in parallel with commodity PC clusters as the target. The cut-cells which characterize the approach are eliminated by exporting a body-conformal mesh guided by the underpinning Level Set. This paper extends this work still further with a simple scoping study showing how the basic functionality can be scripted & automated and then used as the basis for automated optimization of a generic gas turbine cooling geometry. Copyright © 2008 by W.N.Dawes.

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To control combustion instabilities occurring in LPP gas turbine combustors, several active and passive systems have been developed in recent years. The combustion chamber cooling geometry has the potential to influence instability feedback loops by absorbing acoustical energy inside the combustor. The design of the cooling liner and the geometry of the cooling plenum and the cooling air flow rate have a significant influence on the absorption characteristics of the system. This paper presents the results of a cold flow study which was carried out in the course of a comprehensive study on the influence of the cooling geometry on combustor thermoacoustics. Absorption characteristics of three different cooling liner geometries and non-perforated plates were determined over a frequency range from 50 Hz to 600 Hz for different cooling flow rates and different cooling plenum volumes. The experimental results compared well with results from a low order thermoacoustic network model. The acoustic energy absorption spectrum of a cooling liner with 90°-hole configuration was found to be strongly dependent on cooling flow rate and cooling plenum volume, whereas the absorption spectrum of cooling liners with 25°-holes were found to be strongly dependent on the cooling plenum volume, but less dependent on the cooling air flow rate. All cooling liner setups with perforations were capable of increased acoustic absorption over a broad band of frequencies compared to the case of non-perforated combustor walls. © 2010 by Johannes Schmidt.

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Humans have exceptional abilities to learn new skills, manipulate tools and objects, and interact with our environment. In order to be successful at these tasks, our brain has developed learning mechanisms to deal with and compensate for the constantly changing dynamics of the world. If this mechanism or mechanisms can be understood from a computational point of view, then they can also be used to drive the adaptability and learning of robots. In this paper, we will present a new technique for examining changes in the feedforward motor command due to adaptation. This technique can then be utilized for examining motor adaptation in humans and determining a computational algorithm which explains motor learning. © 2007.

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Humans have exceptional abilities to learn new skills, manipulate tools and objects, and interact with our environment. In order to be successful at these tasks, our brain has become exceptionally well adapted to learning to deal not only with the complex dynamics of our own limbs but also with novel dynamics in the external world. While learning of these dynamics includes learning the complex time-varying forces at the end of limbs through the updating of internal models, it must also include learning the appropriate mechanical impedance in order to stabilize both the limb and any objects contacted in the environment. This article reviews the field of human learning by examining recent experimental evidence about adaptation to novel unstable dynamics and explores how this knowledge about the brain and neuro-muscular system can expand the learning capabilities of robotics and prosthetics. © 2006.

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