Asymptotic Multi-Layer Analysis of Wind Over Unsteady Monochromatic Surface Waves

Asymptotic multi-layer analyses and computation of solutions for turbulent flows over steady and unsteady monochromatic surface wave are reviewed, in the limits of low turbulent stresses and small wave amplitude. The structure of the flow is defined in terms of asymptotically-matched thin-layers, namely the surface layer and a critical layer, whether it is elevated or immersed, corresponding to its location above or within the surface layer. The results particularly demonstrate the physical importance of the singular flow features and physical implications of the elevated critical layer in the limit of the unsteadiness tending to zero. These agree with the variational mathematical solution of Miles (1957) for small but finite growth rate, but they are not consistent physically or mathematically with his analysis in the limit of growth rate tending to zero. As this and other studies conclude, in the limit of zero growth rate the effect of the elevated critical layer is eliminated by finite turbulent diffusivity, so that the perturbed flow and the drag force are determined by the asymmetric or sheltering flow in the surface shear layer and its matched interaction with the upper region. But for groups of waves, in which the individual waves grow and decay, there is a net contribution of the elevated critical layer to the wave growth. Critical layers, whether elevated or immersed, affect this asymmetric sheltering mechanism, but in quite a different way to their effect on growing waves. These asymptotic multi-layer methods lead to physical insight and suggest approximate methods for analyzing higher amplitude and more complex flows, such as flow over wave groups.Comment: 20 page

Neural Modeling and Control of Diesel Engine with Pollution Constraints

The paper describes a neural approach for modelling and control of a turbocharged Diesel engine. A neural model, whose structure is mainly based on some physical equations describing the engine behaviour, is built for the rotation speed and the exhaust gas opacity. The model is composed of three interconnected neural submodels, each of them constituting a nonlinear multi-input single-output error model. The structural identification and the parameter estimation from data gathered on a real engine are described. The neural direct model is then used to determine a neural controller of the engine, in a specialized training scheme minimising a multivariable criterion. Simulations show the effect of the pollution constraint weighting on a trajectory tracking of the engine speed. Neural networks, which are flexible and parsimonious nonlinear black-box models, with universal approximation capabilities, can accurately describe or control complex nonlinear systems, with little a priori theoretical knowledge. The presented work extends optimal neuro-control to the multivariable case and shows the flexibility of neural optimisers. Considering the preliminary results, it appears that neural networks can be used as embedded models for engine control, to satisfy the more and more restricting pollutant emission legislation. Particularly, they are able to model nonlinear dynamics and outperform during transients the control schemes based on static mappings.Comment: 15 page

Spatial patterns of desynchronization bursts in networks

We adapt a previous model and analysis method (the {\it master stability function}), extensively used for studying the stability of the synchronous state of networks of identical chaotic oscillators, to the case of oscillators that are similar but not exactly identical. We find that bubbling induced desynchronization bursts occur for some parameter values. These bursts have spatial patterns, which can be predicted from the network connectivity matrix and the unstable periodic orbits embedded in the attractor. We test the analysis of bursts by comparison with numerical experiments. In the case that no bursting occurs, we discuss the deviations from the exactly synchronous state caused by the mismatch between oscillators

Measurement of transverse beam emittance of split beams for the CERN Proton Synchrotron Multi-Turn Extraction

Crossing a horizontal nonlinear resonance is the approach that can be used to split a beam in several beamlets with the goal to perform multi-turn extraction from a circular particle accelerator. Such an approach has been successfully implemented in the CERN Proton Synchrotron and is used routinely for the production of high-intensity proton beams for fixed-target physics at the Super Proton Synchrotron. Recently, thanks to the deployment of diamond detectors, originally installed to monitor the beam losses at extraction, it has been possible to measure the horizontal beam emittance of the split beam just prior to extraction. This is the first time that an emittance measurement is attempted for split beams, i.e. in a regime of highly nonlinear beam dynamics. In this paper, the technique is presented and its application to the analysis of the experimental data is presented and discussed in detail. This result is essential for the performance assessment of the splitting process and for the design of further performance improvements

Natural scene statistics and the structure of orientation maps in the visual cortex

Visual activity after eye-opening influences feature map structure in primary visual cortex (V1). For instance, rearing cats in an environment of stripes of one orientation yields an over-representation of that orientation in V1. However, whether such changes also affect the higher-order statistics of orientation maps is unknown. A statistical bias of orientation maps in normally raised animals is that the probability of the angular difference in orientation preference between each pair of points in the cortex depends on the angle of the line joining those points relative to a fixed but arbitrary set of axes. Natural images show an analogous statistical bias; however, whether this drives the development of comparable structure in V1 is unknown. We examined these statistics for normal, stripe-reared and dark-reared cats, and found that the biases present were not consistently related to those present in the input, or to genetic relationships. We compared these results with two computational models of orientation map development, an analytical model and a Hebbian model. The analytical model failed to reproduce the experimentally observed statistics. In the Hebbian model, while orientation difference statistics could be strongly driven by the input, statistics similar to those seen in experimental maps arose only when symmetry breaking was allowed to occur spontaneously. These results suggest that these statistical biases of orientation maps arise primarily spontaneously, rather than being governed by either input statistics or genetic mechanisms

Revertant fibres and dystrophin traces in Duchenne muscular dystrophy: Implication for clinical trials

Duchenne muscular dystrophy (DMD) is characterised by the absence of dystrophin in muscle biopsies, although residual dystrophin can be present, either as dystrophin-positive (revertant) fibres or traces. As restoration of dystrophin expression is the end point of clinical trials, such residual dystrophin is a key factor in recruitment of patients and may also confound the analysis of dystrophin restoration in treated patients, if, as previously observed in the mdx mouse, revertant fibres increase with age. In 62% of the diagnostic biopsies reports of 65 DMD patients studied, traces or revertants were recorded with no correlation between traces or revertants, the patients' performance, or corticosteroids response. In nine of these patients, there was no increase in traces or revertants in biopsies taken a mean of 8.23 years (5.8-10.4 years) after the original diagnostic biopsy. This information should help in the design and execution of clinical trials focused on dystrophin restoration strategies. (C) 2010 Elsevier B.V. All rights reserved