Call number: LD2668 .T4 1952 V6 / Master of Science

Published Article / Numerical modelling has successfully been used as an efficient tool to convert a gravity cast brake calliper to a thixocasting process. The thixo-module of Procast has been used for the modelling process to obtain optimum processing parameters. Results from interrupted shot castings show excellent correlation with the fluid dynamics and flow pattern of the model. The level and location of porosity revealed by non-destructive X-rays and microscopic analyses showed good correlation with the model prediction.

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As transistor counts, power dissipation, and wire delays increase, the microprocessor industry is transitioning from chips containing large monolithic processors to multi-core architectures. The granularity of cores determines the mechanisms for branch prediction, instruction fetch and map, data supply, instruction execution, and completion. Accurate control flow prediction is essential for high performance processors with large instruction windows and high-bandwidth execution. This dissertation considers cores with very large granularity, such as TRIPS, as well as cores with extremely small granularity, such as TFlex, and explores control flow speculation issues in such processors. Both TRIPS and TFlex are distributed block-based architectures and require control speculation mechanisms that can work in a distributed environment while supporting efficient block-level prediction, misprediction detection, and recovery. This dissertation aims at providing efficient control flow prediction techniques for distributed block-based processors. First, we discuss simple exit predictors inspired by branch predictors and describe the design of the TRIPS prototype block predictor. Area and timing trade-offs in the predictor implementation are presented. We report the predictor misprediction rates from the prototype chip for the SPEC benchmark suite. Next, we look at the performance bottlenecks in the prototype predictor and present a detailed analysis of exit and target predictors using basic prediction components inspired from branch predictors. This study helps in understanding what types of predictors are effective for exit and target prediction. Using the results of our prediction analysis, we propose novel hardware techniques to improve the accuracy of block prediction. To understand whether exit prediction is inherently more difficult than branch prediction, we measure the correlation among branches in basic blocks and hyperblocks and examine the loss in correlation due to hyperblock construction. Finally, we propose block predictors for TFlex, a fully distributed architecture that uses composable lightweight processors. We describe various possible designs for distributed block predictors and a classification scheme for such predictors. We present results for predictors from each of the design points for distributed prediction. / text

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A new stochastic rainfall field model is described which employs the Turning Bands Method (TBM) to transform a unidimensional Gaussian process, generated by the fractional differencing process along a line, into a multidimensional space-time Gaussian process with a specified space-time correlation structure. Transformations are applied to give the rainfall process a non-Gaussian and non-stationary structure. A correction factor is introduced into the model to take account of the effect of topography on rainfall. The model has been applied to the small upland Wye catchment in mid Wales (area 10.55 km2) and shown to reproduce satisfactorily the statistics and correlation structure of observed hourly point rainfall. As an extension to the rainfall field model, a new technique of conditional simulation has been used to generate the rainfall fields. The conditionally simulated rainfall fields reproduce exactly the observed point rainfalls at measurement points and likely realizations of rainfall fields between points. Rainfall fields generated by the above mentioned rainfall field model and the conditional simulation technique are fed directly into the Systeme Hydrologique European (SHE) model and the sensitivity of runoff prediction errors to (i) level of space-time correlation (ii) sampling of rainfall with different schemes in space and (iii) antecedent conditions are explored. It is found that in case of Wye catchment the errors deriving from sampling procedure used are generally small when rainfall fields were based on observed correlation structure. Sensitivity of errors to different correlation levels give the impression that errors increase with a decrease in correlation level. Further it is noticed that this trend of errors is more pronounced in `dry' catchment conditions as compared to `wet' catchment conditions. Overall the results for the small Wye catchment illustrate that the catchment acts as a smoother of the spatially distributed rainfall input at this spatial scale and for the rainfall regime in question. However, the results imply that for the typical raingauge densities encountered for larger catchments, significant errors may occur.