Numerical and Experimental Investigation On The Prevention of Co Deflagration

The exhaust gases from industrial furnaces contain a huge amount of heat and chemical enthalpy. However, it is hard to recover this energy since exhaust gases invariably contain combustible components such as carbon monoxide (CC). If the CO is unexpectedly ignited during the heat recovery process, deflagration or even detonation could occur, with serious consequences such as complete destruction of the equipment. In order to safely utilize the heat energy contained in exhaust gas, danger of its explosion must be fully avoided. The mechanism of gas deflagration and its prevention must therefore be studied. In this paper, we describe a numerical and experimental investigation of the deflagration process in a semi-opened tube. The results show that, upon ignition, a low-pressure wave initially spreads within the tube and then deflagration begins. For the purpose of preventing deflagration, an appropriate amount of nitrogen was injected into the tube at a fixed position. Both simulation and experimental results have shown that the injection of inert gas can successfully interrupt the deflagration process. The peak value of the deflagration pressure can thereby be reduced by around 50%. (C) 2008 Elsevier Ltd. All rights reserved.

Numerical methods for ill-posed, linear problems

A means of assessing the effectiveness of methods used in the numerical solution of various linear ill-posed problems is outlined. Two methods: Tikhonov' s method of regularization and the quasireversibility method of Lattès and Lions are appraised from this point of view.

In the former method, Tikhonov provides a useful means for incorporating a constraint into numerical algorithms. The analysis suggests that the approach can be generalized to embody constraints other than those employed by Tikhonov. This is effected and the general "T-method" is the result.

A T-method is used on an extended version of the backwards heat equation with spatially variable coefficients. Numerical computations based upon it are performed.

The statistical method developed by Franklin is shown to have an interpretation as a T-method. This interpretation, although somewhat loose, does explain some empirical convergence properties which are difficult to pin down via a purely statistical argument.

Numerical solution of parabolic equations by the box scheme

The box scheme proposed by H. B. Keller is a numerical method for solving parabolic partial differential equations. We give a convergence proof of this scheme for the heat equation, for a linear parabolic system, and for a class of nonlinear parabolic equations. Von Neumann stability is shown to hold for the box scheme combined with the method of fractional steps to solve the two-dimensional heat equation. Computations were performed on Burgers' equation with three different initial conditions, and Richardson extrapolation is shown to be effective.

Report on the physical, bacteriological and biochemical analyses of seawater samples collected off existing sewage outfalls in the Sierra Leone River Estuary

The studies reported were undertaken as part of a wide environmental feasibility study for the establishment of a modern sewage system in Freetown. The aim of this part of the study was to determine whether the hydrological regime of the Sierra Leone River Estuary would permit the large-scale introduction of sewage into the estuary without damaging the environment. The important factors were whether: 1) there would be sufficient dilution of the sewage; 2) fleatable particles or other substances would create significant adverse effects in the estuarine ecosystem. The outfall sites are described together with the sampling stations, methods and analyses. Results include: 1) T/S profiles; 2) chemical analysis of the water. A review of literature on the Sierra Leone River Estuary is included which provides information on the plankton, benthos and fisheries. Results suggest that at certain points where local circulations occur it would be inadvisable to locate untreated sewage outfalls. Such points are frequently observed in small embayments. These studies have been of short duration but the data can serve as baseline for more extended investigations which would give a more complete picture of the seasonal patterns in the estuary.

Topics in randomized numerical linear algebra

This thesis studies three classes of randomized numerical linear algebra algorithms, namely: (i) randomized matrix sparsification algorithms, (ii) low-rank approximation algorithms that use randomized unitary transformations, and (iii) low-rank approximation algorithms for positive-semidefinite (PSD) matrices.

Randomized matrix sparsification algorithms set randomly chosen entries of the input matrix to zero. When the approximant is substituted for the original matrix in computations, its sparsity allows one to employ faster sparsity-exploiting algorithms. This thesis contributes bounds on the approximation error of nonuniform randomized sparsification schemes, measured in the spectral norm and two NP-hard norms that are of interest in computational graph theory and subset selection applications.

Low-rank approximations based on randomized unitary transformations have several desirable properties: they have low communication costs, are amenable to parallel implementation, and exploit the existence of fast transform algorithms. This thesis investigates the tradeoff between the accuracy and cost of generating such approximations. State-of-the-art spectral and Frobenius-norm error bounds are provided.

The last class of algorithms considered are SPSD "sketching" algorithms. Such sketches can be computed faster than approximations based on projecting onto mixtures of the columns of the matrix. The performance of several such sketching schemes is empirically evaluated using a suite of canonical matrices drawn from machine learning and data analysis applications, and a framework is developed for establishing theoretical error bounds.

In addition to studying these algorithms, this thesis extends the Matrix Laplace Transform framework to derive Chernoff and Bernstein inequalities that apply to all the eigenvalues of certain classes of random matrices. These inequalities are used to investigate the behavior of the singular values of a matrix under random sampling, and to derive convergence rates for each individual eigenvalue of a sample covariance matrix.

Near-field x-ray phase contrast imaging and phase retrieval algorithm

Theoretical analyses of x-ray diffraction phase contrast imaging and near field phase retrieval method are presented. A new variant of the near field intensity distribution is derived with the optimal phase imaging distance and spatial frequency of object taken into account. Numerical examples of phase retrieval using simulated data are also given. On the above basis, the influence of detecting distance and polychroism of radiation on the phase contrast image and the retrieved phase distribution are discussed. The present results should be useful in the practical application of in-line phase contrast imaging.