Nonlinear numerical modelling of lightning strike effect on composite panels with temperature dependent material properties

This paper presents a physics based modelling procedure to predict the thermal damage of composite material when struck by lightning. The procedure uses the Finite Element Method with non-linear material models to represent the extreme thermal material behaviour of the composite material (carbon/epoxy) and an embedded copper mesh protection system. Simulation predictions are compared against published experimental data, illustrating the potential accuracy and computational cost of virtual lightning strike tests and the requirement for temperature dependent material modelling. The modelling procedure is then used to examine and explain a number of practical solutions to minimize thermal material damage. © 2013 Elsevier Ltd.

Nonlocality of two- and three-mode continuous variable systems

We address the nonlocality of fully inseparable three-mode Gaussian states generated either by bilinear three-mode Hamiltonians or by a sequence of bilinear two-mode Hamiltonians. Two different tests revealing nonlocality are considered, in which the dichotomic Bell operator is represented by the displaced parity and by the pseudospin operator respectively. Three-mode states are also considered as a conditional source of two-mode non-Gaussian states, whose nonlocality properties are analysed. We found that the non-Gaussian character of the conditional states allows violation of Bell's inequalities (by parity and pseudospin tests) stronger than with a conventional twin-beam state. However, the non-Gaussian character is not sufficient to reveal nonlocality through a dichotomized quadrature measurement strategy.

Clarifying the acquis humanitaire: a transnational legal perspective on the internalization of humanitarian norms

Although the international obligations and institutional frameworks for disaster response are not yet settled, as evidenced by the International Law Commission’s work on the protection of persons in the event of disasters and the on-going promotion of disaster laws by the Red Cross Movement; the diverse source and nature of such initiatives suggests that the international community is engaged in a process of norm creation, elaboration and interpretation reflecting a desire for legal clarity in humanitarian operations. Situated within the framework of transnational law, this paper argues that an acquis humanitaire, based on the principle of humanity, encapsulates the evolving body of law and practice specifically relating to the protection of persons in times of humanitarian crisis in both armed conflicts and natural or human-made disasters. Reflecting the non-traditional, non-statist, dynamic and normative basis of transnational legal process, as elaborated by Harold Koh, the constant flow of ideas and principles between the national, regional and international spheres provides an analytical framework for the on-going transnational dialogues on the social, political and legal internalization of humanitarian norms. Drawing on the internalization of humanitarian norms within the United Kingdom, this paper concludes that as the international community examines the codification of a universal legal framework for the protection of persons in the event of disasters it is necessary to understand the transnational process of interpretation and internalization of humanitarian norms, and how this may vary across different regions and countries.

A novel radial basis function neural network principal component analysis scheme for PMU-based wide-area power system monitoring

A novel model-based principal component analysis (PCA) method is proposed in this paper for wide-area power system monitoring, aiming to tackle one of the critical drawbacks of the conventional PCA, i.e. the incapability to handle non-Gaussian distributed variables. It is a significant extension of the original PCA method which has already shown to outperform traditional methods like rate-of-change-of-frequency (ROCOF). The ROCOF method is quick for processing local information, but its threshold is difficult to determine and nuisance tripping may easily occur. The proposed model-based PCA method uses a radial basis function neural network (RBFNN) model to handle the nonlinearity in the data set to solve the no-Gaussian issue, before the PCA method is used for islanding detection. To build an effective RBFNN model, this paper first uses a fast input selection method to remove insignificant neural inputs. Next, a heuristic optimization technique namely Teaching-Learning-Based-Optimization (TLBO) is adopted to tune the nonlinear parameters in the RBF neurons to build the optimized model. The novel RBFNN based PCA monitoring scheme is then employed for wide-area monitoring using the residuals between the model outputs and the real PMU measurements. Experimental results confirm the efficiency and effectiveness of the proposed method in monitoring a suite of process variables with different distribution characteristics, showing that the proposed RBFNN PCA method is a reliable scheme as an effective extension to the linear PCA method.

Generating Realistic Labelled, Weighted Random Graphs

Generative algorithms for random graphs have yielded insights into the structure and evolution of real-world networks. Most networks exhibit a well-known set of properties, such as heavy-tailed degree distributions, clustering and community formation. Usually, random graph models consider only structural information, but many real-world networks also have labelled vertices and weighted edges. In this paper, we present a generative model for random graphs with discrete vertex labels and numeric edge weights. The weights are represented as a set of Beta Mixture Models (BMMs) with an arbitrary number of mixtures, which are learned from real-world networks. We propose a Bayesian Variational Inference (VI) approach, which yields an accurate estimation while keeping computation times tractable. We compare our approach to state-of-the-art random labelled graph generators and an earlier approach based on Gaussian Mixture Models (GMMs). Our results allow us to draw conclusions about the contribution of vertex labels and edge weights to graph structure.

Atomic data for modelling fusion and astrophysical plasmas

Trends and focii of interest in atomic modelling and data are identified in connection with recent observations and experiments in fusion and astrophysics. In the fusion domain, spectral observations are included of core, beam penetrated and divertor plasma. The helium beam experiments at JET and the studies with very heavy species at ASDEX and JET are noted. In the astrophysics domain, illustrations are given from the SOHO and CHANDRA spacecraft which span from the solar upper atmosphere, through soft x-rays from comets to supernovae remnants. It is shown that non-Maxwellian, dynamic and possibly optically thick regimes must be considered. The generalized collisional-radiative model properly describes the collisional regime of most astrophysical and laboratory fusion plasmas and yields self-consistent derived data for spectral emission, power balance and ionization state studies. The tuning of this method to routine analysis of the spectral observations is described. A forward look is taken as to how such atomic modelling, and the atomic data which underpin it, ought to evolve to deal with the extended conditions and novel environments of the illustrations. It is noted that atomic physics influences most aspects of fusion and astrophysical plasma behaviour but the effectiveness of analysis depends on the quality of the bi-directional pathway from fundamental data production through atomic/plasma model development to the confrontation with experiment. The principal atomic data capability at JET, and other fusion and astrophysical laboratories, is supplied via the Atomic Data and Analysis Structure (ADAS) Project. The close ties between the various experiments and ADAS have helped in this path of communication.

Modeling the Rossiter-McLaughlin Effect: Impact of the Convective Center-to-limb Variations in the Stellar Photosphere

bservations of the Rossiter–McLaughlin (RM) effect provide information on star–planet alignments, which can inform planetary migration and evolution theories. Here, we go beyond the classical RM modeling and explore the impact of a convective blueshift that varies across the stellar disk and non-Gaussian stellar photospheric profiles. We simulated an aligned hot Jupiter with a four-day orbit about a Sun-like star and injected center-to-limb velocity (and profile shape) variations based on radiative 3D magnetohydrodynamic simulations of solar surface convection. The residuals between our modeling and classical RM modeling were dependent on the intrinsic profile width and v sin i; the amplitude of the residuals increased with increasing v sin i and with decreasing intrinsic profile width. For slowly rotating stars the center-to-limb convective variation dominated the residuals (with amplitudes of 10 s of cm s−1 to ~1 m s−1); however, for faster rotating stars the dominant residual signature was due a non-Gaussian intrinsic profile (with amplitudes from 0.5 to 9 m s−1). When the impact factor was 0, neglecting to account for the convective center-to-limb variation led to an uncertainty in the obliquity of ~10°–20°, even though the true v sin i was known. Additionally, neglecting to properly model an asymmetric intrinsic profile had a greater impact for more rapidly rotating stars (e.g., v sin i = 6 km s−1) and caused systematic errors on the order of ~20° in the measured obliquities. Hence, neglecting the impact of stellar surface convection may bias star–planet alignment measurements and consequently theories on planetary migration and evolution.

Application of Volterra Equations to Solve Unit Commitment Problem of Optimised Energy Storage and Generation

Development of reliable methods for optimised energy storage and generation is one of the most imminent challenges in modern power systems. In this paper an adaptive approach to load leveling problem using novel dynamic models based on the Volterra integral equations of the first kind with piecewise continuous kernels. These integral equations efficiently solve such inverse problem taking into account both the time dependent efficiencies and the availability of generation/storage of each energy storage technology. In this analysis a direct numerical method is employed to find the least-cost dispatch of available storages. The proposed collocation type numerical method has second order accuracy and enjoys self-regularization properties, which is associated with confidence levels of system demand. This adaptive approach is suitable for energy storage optimisation in real time. The efficiency of the proposed methodology is demonstrated on the Single Electricity Market of Republic of Ireland and Northern Ireland.

The Conditional Phase-type distribution: Graphical Models for Continuous Non-Normal Data:The Conditional Phase-type (C-Ph) distribution:

Conditional Gaussian (CG) distributions allow the inclusion of both discrete and continuous variables in a model assuming that the continuous variable is normally distributed. However, the CG distributions have proved to be unsuitable for survival data which tends to be highly skewed. A new method of analysis is required to take into account continuous variables which are not normally distributed. The aim of this paper is to introduce the more appropriate conditional phase-type (C-Ph) distribution for representing a continuous non-normal variable while also incorporating the causal information in the form of a Bayesian network.

Characteristics of the complex received signal in dynamic body area networks

This paper investigates the characteristics of the complex received signal in body area networks for two environments at the opposite ends of the multipath spectrum at 2.45 GHz. Important attributes of the complex channel such as the Gaussianity of the quadrature components and power imbalance, which form the basis of many popular fading models, are investigated. It is found that in anechoic environments the assumption of Gaussian distributed quadrature components will not always yield a satisfactory fit. Using a complex received signal model which considers a non-isotropic scattered signal contribution along with the presence of an optional dominant signal component, we use an autocorrelation function originally derived for mobile-to-mobile communications to model the temporal behavior of a range of dynamic body area network channels with considerable success. In reverberant environments, it was observed that the real part of the complex autocorrelation function for body area network channels decayed slightly quicker than that expected in traditional land mobile channels. © 2013 IEEE.

Identification and bisection of temporal durations and tone frequencies: Common models for temporal and non-temporal stimuli.

Two experiments examined identification and bisection of tones varying in temporal duration (Experiment 1) or frequency (Experiment 2). Absolute identification of both durations and frequencies was influenced by prior stimuli and by stimulus distribution. Stimulus distribution influenced bisection for both stimulus types consistently, with more positively skewed distributions producing lower bisection points. The effect of distribution was greater when the ratio of the largest to smallest stimulus magnitude was greater. A simple mathematical model, temporal range frequency theory, was applied. It is concluded that (a) similar principles describe identification of temporal durations and other stimulus dimensions and (b) temporal bisection point shifts can be understood in terms of psychophysical principles independently developed in nontemporal domains, such as A. Parducci's (1965) range frequency theory.