Comparison of uniform perturbation solutions and numerical solutions for some potential flows past slender bodies

Approximate solutions for potential flow past an axisymmetric slender body and past a thin airfoil are calculated using a uniform perturbation method and then compared with either the exact analytical solution or the solution obtained using a purely numerical method. The perturbation method is based upon a representation of the disturbance flow as the superposition of singularities distributed entirely within the body, while the numerical (panel) method is based upon a distribution of singularities on the surface of the body. It is found that the perturbation method provides very good results for small values of the slenderness ratio and for small angles of attack. Moreover, for comparable accuracy, the perturbation method is simpler to implement, requires less computer memory, and generally uses less computation time than the panel method. In particular, the uniform perturbation method yields good resolution near the regions of the leading and trailing edges where other methods fail or require special attention

A unified model of the electrical power network

Traditionally, the different infrastructure layers, technologies and management activities associated with the design, control and protection operation of the Electrical Power Systems have been supported by numerous independent models of the real world network. As a result of increasing competition in this sector, however, the integration of technologies in the network and the coordination of complex management processes have become of vital importance for all electrical power companies. The aim of the research outlined in this paper is to develop a single network model which will unify the generation, transmission and distribution infrastructure layers and the various alternative implementation technologies. This 'unified model' approach can support ,for example, network fault, reliability and performance analysis. This paper introduces the basic network structures, describes an object-oriented modelling approach and outlines possible applications of the unified model

Update of an early warning fault detection method using artificial intelligence techniques

This presentation describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. In an earlier paper [11], a computer simulated medium length transmission line has been tested by the detector and the results clearly demonstrate the capability of the detector. Today’s presentation considers a case study illustrating the suitability of this AI Technique when applied to a distribution transformer. Furthermore, an evolutionary optimisation strategy to train ANNs is also briefly discussed in this presentation, together with a ‘crystal ball’ view of future developments in the operation and monitoring of transmission systems in the next millennium

Power system fault prediction using artificial neural networks

The medium term goal of the research reported in this paper was the development of a major in-house suite of strategic computer aided network simulation and decision support tools to improve the management of power systems. This paper describes a preliminary research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. To achieve this goal, an AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system . Simulation will normally take place using equivalent circuit representation. Artificial Neural Networks (ANNs) are used to construct a hierarchical feed-forward structure which is the most important component in the fault detector. Simulation of a transmission line (2-port circuit ) has already been carried out and preliminary results using this system are promising. This approach provided satisfactory results with accuracy of 95% or higher

Multi-hadron states in Lattice QCD spectroscopy

The ability to reliably measure the energy of an excited hadron in Lattice QCD simulations hinges on the accurate determination of all lower-lying energies in the same symmetry channel. These include not only single-particle energies, but also the energies of multi-hadron states. This talk deals with the determination of multi-hadron energies in Lattice QCD. The group-theoretical derivation of lattice interpolating operators that couple optimally to multi-hadron states is described. We briefly discuss recent algorithmic developments which allow for the efficient implementation of these operators in software, and present numerical results from the Hadron Spectrum Collaboration.Comment: 5 pages, 3 figures, talk given at Hadron 2009, Tallahassee, Florida, December 1, 200

Early warning fault detection using artificial intelligent methods

This paper describes a research investigation to access the feasibility of using an Artificial Intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial Neural Networks (ANNs) are being used as the core of the fault detector. A simulated medium length transmission line has been tested by the detector and the results demonstrate the capability of the detector. Furthermore, comments on an evolutionary technique as the optimisation strategy for ANNs are included in this paper

Novel Bose-Einstein Interference in the Passage of a Fast Particle in a Dense Medium

When an energetic particle collides coherently with many medium particles at high energies, the Bose-Einstein symmetry with respect to the interchange of the exchanged virtual bosons leads to a destructive interference of the Feynman amplitudes in most regions of the phase space but a constructive interference in some other regions of the phase space. As a consequence, the recoiling medium particles have a tendency to come out collectively along the direction of the incident fast particle, each carrying a substantial fraction of the incident longitudinal momentum. Such an interference appearing as collective recoils of scatterers along the incident particle direction may have been observed in angular correlations of hadrons associated with a high-$p_T$ trigger in high-energy AuAu collisions at RHIC.Comment: 10 pages, 2 figures, invited talk presented at the 35th Symposium on Nuclear Physics, Cocoyoc, Mexico, January 3, 2012, to be published in IOP Conference Serie