Analysis of fields in an air-cored superconducting synchronous motor with an HTS racetrack field winding

High temperature superconducting (HTS) synchronous motors can offer significant weight and size reductions, as well as improved efficiency, over conventional copper-wound machines due to the higher current density of high temperature superconducting (HTS) materials. In order to optimise the design parameters and performance of such a machine, this paper proposes a basic physical model of an air-cored HTS synchronous motor with a copper armature winding and HTS field winding. An analytical method for the field analysis in the synchronous motor is then presented, followed by a numerical finite element analysis (FEA) model to verify the analytical solution. The model is utilised to study the influence of the geometry of the HTS coils on the magnetic field at the armature winding, and geometrical parameter optimisation is carried out using this theoretical model to obtain a more sinusoidal magnetic field at the armature, which has a major influence on the performance of the motor.

A backward particle interpretation of Feynman-Kac formulae

We design a particle interpretation of Feynman-Kac measures on path spaces based on a backward Markovian representation combined with a traditional mean field particle interpretation of the flow of their final time marginals. In contrast to traditional genealogical tree based models, these new particle algorithms can be used to compute normalized additive functionals "on-the-fly" as well as their limiting occupation measures with a given precision degree that does not depend on the final time horizon. We provide uniform convergence results with respect to the time horizon parameter as well as functional central limit theorems and exponential concentration estimates. Our results have important consequences for online parameter estimation for non-linear non-Gaussian state-space models. We show how the forward filtering backward smoothing estimates of additive functionals can be computed using a forward only recursion.

Two problems with variational expectation maximisation for time-series models

Variational methods are a key component of the approximate inference and learning toolbox. These methods fill an important middle ground, retaining distributional information about uncertainty in latent variables, unlike maximum a posteriori methods (MAP), and yet generally requiring less computational time than Monte Carlo Markov Chain methods. In particular the variational Expectation Maximisation (vEM) and variational Bayes algorithms, both involving variational optimisation of a free-energy, are widely used in time-series modelling. Here, we investigate the success of vEM in simple probabilistic time-series models. First we consider the inference step of vEM, and show that a consequence of the well-known compactness property of variational inference is a failure to propagate uncertainty in time, thus limiting the usefulness of the retained distributional information. In particular, the uncertainty may appear to be smallest precisely when the approximation is poorest. Second, we consider parameter learning and analytically reveal systematic biases in the parameters found by vEM. Surprisingly, simpler variational approximations (such a mean-field) can lead to less bias than more complicated structured approximations.

Conceptual transformation framework for servitization

This paper presents a Conceptual Transformation Framework for Servitization. The challenges experienced by manufacturers on their journey to servitization are investigated in order to develop our understanding of the transformation that traditional manufacturers undergo to become servitized manufacturers. Three change theories are applied to develop the Framework: Lewin's force field analysis, Senge's system thinking and Pettigrew and Whipp's framework of strategic change. The Framework includes the dimensions of the change that takes place and the elements of change when transforming towards Product-Service Systems.

The analysis of stochastic EMC problems using the concept of diffuse field reciprocity

There has been much progress in recent years in the analysis of complex random vibro-acoustic systems, and general analysis methods have been developed which are based on the properties of diffuse wave fields. It is shown in the present paper that such methods can also be applied to high frequency EMC problems, avoiding the need for costly full wave solutions to Maxwell's equations in complex cavities. The theory behind the approach is outlined and then applied to the relatively simple case of a wiring system which is subject to reverberant electromagnetic wave excitation. © 2011 IEEE.

Analysis of CFRP strap-strengthened reinforced concrete beams using the modified compression field theory (MCFT)

External, prestressed carbon fiber reinforced polymer (CFRP) straps can be used to enhance the shear strength of existing reinforced concrete beams. In order to effectively design a strengthening system, a rational predictive theory is required. The current work investigates the ability of the modified compression field theory (MCFT) to predict the behavior of rectangular strap strengthened beams where the discrete CFRP strap forces are approximated as a uniform vertical stress. An unstrengthened control beam and two strengthened beams were tested to verify the predictions. The experimental results suggest that the MCFT could predict the general response of a strengthened beam with a uniform strap spacing < 0.9d. However, whereas the strengthened beams failed in shear, the MCFT predicted flexural failures. It is proposed that a different compression softening model or the inclusion of a crack width limit is required to reflect the onset of shear failures in the strengthened beams.