A project report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Engineering. Johannesburg, 2016 / Underground chilling installations have an important role in deep mining operations because the total cost of cooling a mine is minimized when underground machines deliver as high a proportion of the required cooling as practicable. Thus the refrigerating load of an underground installation should be maximized to the extent permitted both by the environment in which the installation operates, and by the physical characteristics of the machines in the installation. This study analyses how, and to what extent, the refrigerating load of older, already installed water chilling machines rejecting heat into a limited supply of return water may be maximized through configuration of their water circuits and capacity control of their compressors. Multiple-machine installations are simulated in a range of scenarios, using the thermodynamically efficient series-counterflow arrangement, to predict both the potential maximum refrigerating load and the expected refrigerating load of such installations. The simulation results indicate significant potential for installations to chill water more efficiently and thus deliver larger, maximized, refrigerating loads. For scenarios where a larger-than-design flowrate of return water is available, so permitting machines to be operated with little or no capacity control, the simulated chilling efficiency and thus the expected refrigerating loads tend toward, and in some cases almost match, the potential maximum values. For simulations in which compressor capacity control is used to prevent the return water temperature from exceeding its maximum permitted value, expected refrigerating loads fall short of their potential values, by varying amounts, due to the low machine cycle efficiency caused largely by reduced compressor isentropic efficiency at part load. For a limited supply of return water for heat rejection, the simulations indicate that load maximization efforts should focus on the machines in an installation being connected in a series-counterflow arrangement and operated, as far as practicable, at or near full capacity to create the best prospect for approaching potential maximum refrigerating load.

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Includes bibliographical references (leaves 11-1-11-26). / xv, 222 leaves : ill. (some col.) : 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--Adelaide University, Dept. of Agronomy and Farming Systems, 2001

Radiation film dosimetry process using the RIT 113 v.4 dosimetry software and the film digitizer VXR-l2plus was used to evaluate the accuracy of electron dose calculations of the RAHD radiation therapy treatment planning system at Samaritan Regional Cancer Center. Kodak Ready-Pack EDR-2 film is recommended for dose distribution analysis in clinically practical dose ranges. The pencil-beam algorithm has a limitation for calculating dose in the penumbra region and in the tail region where the dose falls off. / Graduation date: 2005

Graduation date: 2003

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Coded-aperture imaging without detector motion can be used to reconstruct three-dimensional radionuclide distributions in the context of nuclear medicine. This approach offers several advantages over the rotating gamma-ray camera systems presently employed in the clinic. These advantages include improved sensitivity, potentially better spatial resolution, and the capability of doing dynamic studies. There are two problems associated with the coded-aperture approach, however. First, the data is "multiplexed", which refers to the fact that many line integrals of the source distribution are combined together and not measured individually, so that information is lost. Second, the number of resolvable detector elements is typically an order of magnitude less than the number of object elements to be reconstructed, so that the reconstruction problem is underdetermined. Consequently, the reconstruction is not unique. By using various types of a priori information in forming the reconstruction, however, it is possible to augment the incomplete data set. Two algorithms are presented to reconstruct objects from their coded-image projections and various types of a priori information. The first, a Monte Carlo algorithm, is a flexible and computationally efficient approach using the a priori knowledge of positivity and nearest-neighbor correlation. This algorithm is used to qualitatively explore the effect of the data-taking geometry on reconstruction performance. The second algorithm is a linear estimator incorporating as a priori knowledge completely general first- and second-order statistical information about the object class to be reconstructed. The linear-estimator formalism also provides a minimum-variance expression for system optimization. This linear algorithm is used to explore the effects of correct and incorrect a priori information on reconstruction performance, and to quantitatively investigate reconstruction quality with respect to data-taking geometry.

This thesis describes the design and implementation of an extended knowledge-based modeling and simulation environment, in which the management of continuous-time models in DEVS-SCHEME is realized to meet the requirements of the modeling and simulation of a robot-managed laboratory aboard the forthcoming Space Station Freedom. The modular hierarchical modeling scheme is preserved in the continuous models by using DYMOLA, a continuous modeling language, as a bridge between the abstracted DEVS models and the continuous simulation language code (in DESIRE). Through operations on the System Entity Structure (SES), a knowledge representation scheme, models at different granularity levels are generated. Time-windows can be obtained by manipulating a pruned SES. These time windows can be used to generate an equivalent discrete-event model at a coarser granularity. Therefore, an event-based intelligent control strategy can be realized in this knowledge-based multi-facetted modeling environment. Continuous-time and discrete-event modeling and simulation can be merged with AI techniques.

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