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https://dspace.lboro.ac.uk/2134/80
2017-05-28T01:06:42ZAnalytical and computational modelling for wave energy systems: the example of oscillating wave surge converters
https://dspace.lboro.ac.uk/2134/25171
Title: Analytical and computational modelling for wave energy systems: the example of oscillating wave surge converters
Authors: Dias, F.; Renzi, Emiliano; Gallagher, Sarah; Sarkar, Dripta; Wei, Yanji; Abadie, Thomas; Cummins, Cathal; Rafiee, Ashkan
Abstract: The development of new wave energy converters has shed light on a number of unanswered questions in fluid mechanics, but has also identified a number of new issues of importance for their future deployment. The main concerns relevant to the practical use of wave energy converters are sustainabiliy, survivability, and maintainability. And of course, it is also necessary to maximize the capture per unit area of the structure as well as to minimize the cost. In this review, we consider some of the questions related to the topics of sustainability, survivability, and maintenance access, with respect to sea conditions, for generic wave energy converters with an emphasis on the oscillating wave surge converter (OWSC). New analytical models that have been developed are a topic of particular discussion. It is also shown how existing numerical models have been pushed to their limits to provide answers to open questions relating to the operation and characteristics of wave energy converters.
Description: This paper is closed access until 12 months after publication.2017-01-01T00:00:00ZOptical random Riemann waves in integrable turbulence
https://dspace.lboro.ac.uk/2134/25167
Title: Optical random Riemann waves in integrable turbulence
Authors: Randoux, Stephane; Gustave, Francois; Suret, Pierre; El, G.A.
Abstract: We examine integrable turbulence (IT) in the framework of the defocusing cubic one-dimensional nonlinear Schrodinger equation. This is done theoretically and experimentally, by realizing an optical
fiber experiment in which the defocusing Kerr nonlinearity strongly dominates linear dispersive effects. Using a dispersive-hydrodynamic approach, we show that the development of IT can be divided into two distinct stages, the initial, pre-breaking stage being described by a system of
interacting random Riemann waves. We explain the low-tailed statistics of the wave intensity in IT and show that the Riemann invariants of the asymptotic nonlinear geometric optics system represent the observable quantities that provide new insight into statistical features of the initial stage of the IT development by exhibiting stationary probability density functions.
Description: This paper is in closed access until it is published.2017-01-01T00:00:00ZAspects of neutron residual stress analysis
https://dspace.lboro.ac.uk/2134/25162
Title: Aspects of neutron residual stress analysis
Authors: Wimpory, Robert C.
Abstract: This thesis is concerned with the physical principles, methodology and applications of
neutron diffraction in the measurement of residual stress. Work on three main areas is
presented. 1) Carbon steels 2) Data and Peak Broadening analysis and 3) Single lap
glue shear joints. The Carbon steels section shows the drastic effect of the content of
carbon on the measured stress. This is an aspect which has been somewhat neglected
in the past. The carbon is in the form of cementite, which is a hard compound and
causes the carbon steel to act like a composite material, the ferrite acting as a soft
matrix and the cementite as a reinforcement. The consequence of this is that the two
components develop high microstresses with plastic deformation. This is clearly
illustrated in the work of [Bon 97] where values of approx. 460 MPa in the residual
stress in the ferrite are balanced by negative residual stresses of 2300 MPa in
cementite yielding an overall macro residual stress of zero. In this work it has been
shown that even knowledge of the cementite and ferrite residual stresses and fractions
may not be sufficient to accurately calculate the macro stress since the ferrite
unloading curve is non linear. The use of a single valued constant modulus to convert
from strain to stress is hence not valid.
Peak shape analysis enables dislocation density and cell size estimates to be made.
The thesis examines several methods of data weighting and deconvolution in order to
asses the best means of extracting this information from standard residual stress data.
Care should be taken for the peaks with very low backgrounds when finding the
Gaussian and Lorentzian components. A weighting that avoids the strong bias of zero
and I counts in the detector channels should be used e.g. W = I / ( 10 + Y). Lorentzian
and Gaussian components can be successfully extracted from asymmetrical peaks (of
peaks that broaden symmetrically), using deconvolution method 1, although the data
should be of good quality. Reproducibility has been shown in the Gaussian,
Lorentzian and FWHM for different instruments at different institutes. This is
extremely important for the use of these values for peak broadening analysis and for
estimation of the plastic deformation within a sample.
The neutron diffraction technique has been used to investigate the longitudinal
stresses in the adherend produced as a result of cure and due to the application of a
tensile load in a single lap shear joint. The results throw doubt on widely used finite
element predictions.
Description: A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.1999-01-01T00:00:00ZFast learning neural networks for classification
https://dspace.lboro.ac.uk/2134/25161
Title: Fast learning neural networks for classification
Authors: Tay, Leng Phuan
Abstract: Neural network applications can generally be divided into two categories. The first
involves function approximation, where the neural network is trained to perform intelligent
interpolation and curve fitting from the training data. The second category involves
classification, where specific exemplar classes are used to train the neural network. This
thesis directs its investigations towards the latter, i.e. classification.
Most existing neural network models are developments that arise directly from human
cognition research. It is felt that while neural network research should head towards the
development of models that resemble the cognitive system of the brain, researchers should
not abandon the search for useful task oriented neural networks. These may not possess the
intricacies of human cognition, but are efficient in solving industrial classification tasks.
It is the objective of this thesis to develop a neural network that is fast learning, able
to generalise and achieve good capacity to discern different patterns even though some
patterns may be similar in structure. This eventual neural network will be used in the
pattern classification environment.
The first model developed, was the result of studying and modifying the basic ART I
model. The "Fast Learning Artificial Neural Network I" (FLANN I) maintains good
generalisation properties and is progressive in learning. Although this neural network
achieves fast learning speeds of one epoch, it was limited only to binary inputs and was
unable to operate on continuous values. This posed a real problem because industrial
applications usually require the manipulation of continuous values.
The second model, FLANN II, was designed based on the principles of FLANN I. It was
built on the nearest neighbour recall principle, which allowed the network to operate On
continuous values. Experiments were conducted on the two models designed and the results
were favourable. FLANN II was able to learn the points in a single epoch and obtain
exceptional accuracy. This is a significant improvement to other researcher's results.
A further study was conducted on the FLANN models in the parallel processing
environment. The parallel investigations led to the development of a new paradigm;
Parallel Distributed Neural Networks (PDNNs), which allows several neural networks to
operate concurrently to solve a single classification problem. This paradigm is powerful
because it is able to reduce the overall memory requirements for some classification
problems.
Description: A Doctoral Thesis. Submitted in partial fulfilment of the requirements for the award of Doctor of Philosophy of Loughborough University.1994-01-01T00:00:00Z