The Pebble Bed Modular Reactor (PBMR) design is one of the High Temperature Gas Cooled Reactors (HTGR) under the Generation IV initiative. These designs incorporate numerous inherent passive safety features. Graphite is an important material of construction for the reactor core and the fuel pebbles. Knowledge of the high temperature oxidative behaviour of the graphite materials utilized in such reactors is important for design and accident modelling purposes. Despite large amounts of research into the oxidation of graphite, a coherent framework for the comparison and assessment of the relative reactivity’s of graphite samples from different origins has not yet been established. This is mainly due to a lack of clarity regarding the relative contribution of different factors which influence the overall behaviour of a given sample. The objective of this work was to identify and isolate the key factors which influence the oxidation of graphite and understand their operation across the entire range of conversion. Based on this understanding a comprehensive model for the oxidation can be established. The framework of this model will allow the sensible comparison of samples from different origins, based on the relative contribution of the relevant mechanisms. The work focused purely on the kinetic factors which influence the oxidation and extreme care was taken to avoid mass transfer limitations where possible. Through a carefully established experimental methodology three key factors were found to influence the progression of oxidation in a given sample: <ul><li> First and foremost, the development of the active surface area of a given sample</li><li> Secondly, the presence of catalytic impurities</li><li> Thirdly, the influence of inhibiting impurities/li></ul> Based on these three effects, a finite element type, Monte Carlo simulation was developed. In this simulation virtually any geometry can be easily represented and the progression of the active surface area as the oxidation proceeds can be monitored. Furthermore, catalytic impurities could be easily incorporated into the simulation in a clear, consistent manner. This leads to an overall simulation which produces results that visually reflect the observed behaviour as well as accounting for the kinetic aspects of the experimentally determined conversion behaviour. This work provides a starting point for assessing samples from different origins to first determine differences in the three basic governing effects, followed by a relative assessment of their reactivity’s on a common basis. Future work should focus on refining the understanding of the mechanistic aspects of each of the individual governing effects, especially the influence of surface complexes and different reaction pathways. In addition, the work should be extended to cover a more comprehensive selection of graphite samples from different origins and a wider variety of impurity behaviours. / Thesis (PhD(Eng))--University of Pretoria, 2012. / Chemical Engineering / unrestricted

In this thesis work we have extended the SkePU framework by designing a new container data structure for the representation of generic two dimensional sparse matrices. Computation on matrices is an integral part of many scientific and engineering problems. Sometimes it is unnecessary to perform costly operations on zero entries of the matrix. If the number of zeroes is relatively large then a requirement for more efficient data structure arises. Beyond the sparse matrix representation, we propose an algorithm to judge the condition where computation on sparse matrices is more beneficial in terms of execution time for an ongoing computation and to adapt a matrix's state accordingly, which is the main concern of this thesis work. We present and implement an approach to switch automatically between two data container types dynamically inside the SkePU framework for a multi-core GPU-based heterogeneous system. The new sparse matrix data container supports all SkePU skeletons and nearly all SkePU operations. We provide compression and decompression algorithms from dense matrix to sparse matrix and vice versa on CPU and GPUs using SkePU data parallel skeletons. We have also implemented a context aware switching mechanism in order to switch between two data container types on the CPU or the GPU. A multi-state matrix representation, and selection on demand is also made possible. In order to evaluate and test effectiveness and efficiency of our extension to the SkePU framework, we have considered Matrix-Vector Multiplication as our benchmark program because iterative solvers like Conjugate Gradient and Generalized Minimum Residual use Sparse Matrix-Vector Multiplication as their basic operation. Through our benchmark program we have demonstrated adaptive switching between two data container types, implementation selection between CUDA and OpenMP, and converting the data structure depending on the density of non-zeroes in a matrix. Our experiments on GPU-based architectures show that our automatic switching mechanism adapts with the fastest SkePU implementation variant, and has a limited training cost.