Not available / text

Among the most formidable challenges facing our world is the need for safe, clean, affordable energy sources. Growing concerns over global warming induced climate change and the rising costs of fossil fuels threaten conventional means of electricity production and are driving the current nuclear renaissance. One concept at the forefront of international development efforts is the High Temperature Gas-Cooled Reactor (HTGR). With numerous passive safety features and a meltdown-proof design capable of attaining high thermodynamic efficiencies for electricity generation as well as high temperatures useful for the burgeoning hydrogen economy, the HTGR is an extremely promising technology. Unfortunately, the fundamental understanding of neutron behavior within HTGR fuels lags far behind that of more conventional watercooled reactors. HTGRs utilize a unique heterogeneous fuel element design consisting of thousands of tiny fissile fuel kernels randomly mixed with a non-fissile graphite matrix. Monte Carlo neutron transport simulations of the HTGR fuel element geometry in its full complexity are infeasible and this has motivated the development of more approximate computational techniques. A series of MATLAB codes was written to perform Monte Carlo simulations within HTGR fuel pebbles to establish a comprehensive understanding of the parameters under which the accuracy of the approximate techniques diminishes. This research identified the accuracy of the chord length sampling method to be a function of the matrix scattering optical thickness, the kernel optical thickness, and the kernel packing density. Two new Monte Carlo methods designed to focus the computational effort upon the parameter conditions shown to contribute most strongly to the overall computational error were implemented and evaluated. An extended memory chord length sampling routine that recalls a neutron’s prior material traversals was demonstrated to be effective in fixed source calculations containing densely packed, optically thick kernels. A hybrid continuous energy Monte Carlo algorithm that combines homogeneous and explicit geometry models according to the energy dependent optical thickness was also developed. This resonance switch approach exhibited a remarkably high degree of accuracy in performing criticality calculations. The versatility of this hybrid modeling approach makes it an attractive acceleration strategy for a vast array of Monte Carlo radiation transport applications. / text

Not available / text

published_or_final_version / Physics / Master / Master of Philosophy

No description available.

No description available.

No description available.

No description available.

No description available.

This thesis shows highly encouraging results as the gain of accuracy reached 18.4% when the pairwise comparisons method was used instead of the direct method for comparing random shapes. The thesis describes a heuristic for generating random but nice shapes, called placated shapes. Random, but visually nice shapes, are often needed for cognitive experiments and processes. These shapes are produced by applying the Gaussian blur to randomly generated polygons. Afterwards, the threshold is set to transform pixels to black and white from di erent shades of gray. This transformation produces placated shapes for easier estimation of areas. Randomly generated placated shapes are used to perform the Monte Carlo method to test the accuracy of cognitive processes by using pairwise comparisons. An on-line questionnaire has been implemented and participants were asked to estimate the areas of ve shapes using a provided unit of measure. They were also asked to compare the shapes in pairs. Such Monte Carlo experiment has never been conducted for 2D case. The received results are of considerable importance.