In their exploratory study Muniz & O’Guinn (2001) found three markers of a brand community: a sense of belonging, rituals and tradition and a sense of duty toward the community. Two of the three markers of community have been included in conceptual models on brand communities. However, the third marker (sense of duty) has not been implemented up to now. Hence, the objective of this thesis is to extend Bagozzi & Dholakia’s (2006) brand community model by incorporating the construct “sense of duty”. In this research, a conceptual model of brand communities is developed. Overall, the findings support the conceptual model. The results show that sense of duty is a decisive mediator of brand community behaviours and that sense of duty is divided into three distinct components: new member integration, product usage and member retention. Further, this research indicates that community-related behavioural intentions are not significantly related to purchase intentions.

Neural network models were developed to model the short-term concentration distribution of aerosols released from point sources. Those models were based on data from a wide range field experiments (November 2002, March, May and August 2003). The study focused on relative dispersion from the puff centroid. The influence of puff/cloud meandering and large-scale gusts were not considered, the modelling was limited to studying the dispersion caused by small-scale turbulence. The data collected were based on short range/time dispersion, usually shorter than 150 s. The ANN (Artificial Neural Network) models considered explicitly a number of meteorological and turbulence parameters, as opposed to the Gaussian models that used a single fitting parameter, the dispersion coefficient. The developed ANN models were compared with predictions generated from COMBIC (Combined Obscuration Model for Battlefield Induced Contaminants), a sophisticated model based on Gaussian distributions, and a traditional Gaussian puff model using Slade’s dispersion coefficients. Neural network predictions have been found to have better agreement with concentration measurements than either of the other two Gaussian puff models. All models underestimate the maximum concentration, but ANN predictions are much closer to observations. Simulations of concentration distributions under different stability conditions were also checked using the developed ANN model, and it showed that, for a short time, Gaussian distributions are a good fit for puff dispersion in the downwind, crosswind and vertical directions. For Gaussian puff models, the key issue is to determine appropriate dispersion coefficients (standard deviations). ANN models for puff dispersion coefficients were trained and their average predictions were compared with the results of measurements. Very good agreement was observed, with a high correlation coefficient (>0.99). The ANN models for dispersion coefficients were used to analyze which input variables were more significant for puff expansions. Dispersion time, particle position relative to the centroid, turbulent kinetic energy and insolation showed the most significant influence on puff dispersion. The Gaussian puff model with dispersion coefficients from the ANN models was compared with COMBIC and a Gaussian puff model using Slade’s dispersion coefficients. Generally speaking, predictions generated by the Gaussian puff model with dispersion coefficients generated by ANN models showed better agreement with concentration measurements than the other two Gaussian puff models, by giving a much higher fraction within a factor of two, and lower normalised mean square errors. / Thesis (Ph.D, Chemical Engineering) -- Queen's University, 2007-10-17 12:13:42.923 / NSERC, DGNS

A mathematical model for polymer gel dosimeters was extended to simulate the effects of radiation depth doses of various radiation beams on the mass of polymer formed. The influences of monomer diffusion and temperature variation were investigated and predicted by the model. Simulation results indicate that both diffusion and temperature effects are most noticeable at the depth of maximum dose. Diffusion effects are larger for steep depth-dose curves with large dose gradients, while temperature effects are larger for extensive depth-dose curves that deliver high doses of radiation to a greater depth. Based on simulation results, involving a maximum dose of 5 Gy, the amount of additional polymer formed due to diffusion is small, ranging from 0.1 % for 15 MV x-ray photons to 2.6 % for Co60 γ-radiation. This small amount of additional polymer should not cause significant problems for the accuracy of depth-dose calibration curves, particularly if the depth of maximum dose is avoided. Inaccuracies caused by temperature effects are expected to be smaller than those caused by diffusion. Experimental studies were undertaken to improve the radiation dose response using x-ray Computed Tomography (CT). A new polymer gel dosimeter recipe with enhanced dose response was achieved by using a large quantity of N-isopropyl acrylamide (NIPAM) (15 wt%) to help dissolve the N,N’-methylene bisacrylamide (Bis) crosslinker. The solubility of Bis was substantially increased, allowing for large quantities of dissolved NIPAM and Bis in the system. The new dosimeter exhibits an enhanced dose sensitivity and dose resolution for x-ray CT imaging, which holds promise for clinical applications. The dose resolution of approximately 0.1 Gy, for up to absorbed doses of 50 Gy, for the new recipe is superior to that for previous dosimeter formulations developed for x-ray CT. / Thesis (Master, Chemical Engineering) -- Queen's University, 2010-12-21 18:10:28.37

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No description available.

This thesis investigated the dissolution of dense non aqueous phase liquids (DNAPL) source zones in the subsurface and the effectiveness of enhanced bioremediation for the treatment of chlorinated ethene DNAPLs, using numerical modeling. For this purpose, an existing multiphase numerical model was extended to include comprehensive models for the processes of dissolution and reaction. The first part of the thesis examined DNAPL dissolution. First, a thermodynamic-based dissolution model was validated using experimental data from two complex heterogeneous DNAPL releases. Model predictions for DNAPL spatial distribution and effluent concentrations agreed well with experimental measurements, without requiring calibration. This is the first successful application of a predictive dissolution model in the literature. Model results showed the important effects of relative permeability and interfacial areas on dissolution rates. Then, the thermodynamic dissolution model was compared to simpler models typically used in the literature. Five Sherwood-Gilland (SG) empirical correlations were evaluated and their limitations were illustrated. A new dissolution model was proposed that combined the predictive ability of the thermodynamic model and the simplicity of SG models, and is applicable for complex source zones. Lastly, the relationship between the DNAPL source architecture and downstream concentrations was investigated, focusing on multistage concentration profiles. A new upscaled model was proposed that is able to capture such complex behavior. In the second part of this thesis the thermodynamic dissolution model was combined with a model for reductive dechlorination of chlorinated ethenes to simulate DNAPL bioremediation. Simulations were conducted for simple DNAPL source zones to investigate the impact of dissolution-related processes on bioremediation effectiveness. Dissolution kinetics and back-partitioning of daughter products in the DNAPL were shown to affect dechlorination. Then, the investigation was extended to DNAPL source zones of complex architectures in heterogeneous domains, illustrating the importance of the source zone architecture for the effectiveness of DNAPL bioremediation. Overall, this thesis presents a comprehensive numerical model that will be an important research tool for evaluating the effectiveness of in-situ bioremediation for DNAPL source zones, and will provide the means for a better understanding and control of the critical factors affecting this technology in the field.

This thesis investigated the dissolution of dense non aqueous phase liquids (DNAPL) source zones in the subsurface and the effectiveness of enhanced bioremediation for the treatment of chlorinated ethene DNAPLs, using numerical modeling. For this purpose, an existing multiphase numerical model was extended to include comprehensive models for the processes of dissolution and reaction. The first part of the thesis examined DNAPL dissolution. First, a thermodynamic-based dissolution model was validated using experimental data from two complex heterogeneous DNAPL releases. Model predictions for DNAPL spatial distribution and effluent concentrations agreed well with experimental measurements, without requiring calibration. This is the first successful application of a predictive dissolution model in the literature. Model results showed the important effects of relative permeability and interfacial areas on dissolution rates. Then, the thermodynamic dissolution model was compared to simpler models typically used in the literature. Five Sherwood-Gilland (SG) empirical correlations were evaluated and their limitations were illustrated. A new dissolution model was proposed that combined the predictive ability of the thermodynamic model and the simplicity of SG models, and is applicable for complex source zones. Lastly, the relationship between the DNAPL source architecture and downstream concentrations was investigated, focusing on multistage concentration profiles. A new upscaled model was proposed that is able to capture such complex behavior. In the second part of this thesis the thermodynamic dissolution model was combined with a model for reductive dechlorination of chlorinated ethenes to simulate DNAPL bioremediation. Simulations were conducted for simple DNAPL source zones to investigate the impact of dissolution-related processes on bioremediation effectiveness. Dissolution kinetics and back-partitioning of daughter products in the DNAPL were shown to affect dechlorination. Then, the investigation was extended to DNAPL source zones of complex architectures in heterogeneous domains, illustrating the importance of the source zone architecture for the effectiveness of DNAPL bioremediation. Overall, this thesis presents a comprehensive numerical model that will be an important research tool for evaluating the effectiveness of in-situ bioremediation for DNAPL source zones, and will provide the means for a better understanding and control of the critical factors affecting this technology in the field.

Interaction potentials for the double-perovskite cryolite, Na3AlF6, have been developed for use in classical Molecular Dynamics (MD) simulations using a method whereby ionic configurations are generated with empirical pair potentials, the multipoles and forces on the ions and the stress tensor of the cell are extracted from ab initio single-point DFT calculations, and then the multipoles, forces and stresses from the MD simulations are ‘fit’ to the ab initio quantities in a series of steps in which the potential parameters are optimized, for models of varying complexity. Previously, interaction potentials have been developed empirically by tuning the parameters to reproduce experimentally-derived properties such as structure factors and densities, and so the testing and development of the newer method is necessary in order to standardize a way of obtaining potentials from first principle considerations. A fitted potential was then used to characterize the ion dynamics in crystalline cryolite: a monoclinic to orthorhombic phase transition and the low-temperature-phase tilt-domain structure of the AlF3− 6 , the dominant structural features, are reproduced. The motional processes, which have been studied indirectly in NMR, conductivity and diffraction experiments, include oscillation of the AlF3− 6 and sodium ion diffusion - it has been suggested that these occur at a remarkably fast rate. The nature of the AlF3− 6 oscillatory motion is studied in more depth than accessible to experiment, and its connection with diffusion is investigated. Given the intrinsically defective nature of cryolite and the absence of diffusion in the initial simulations, defects are introduced to observe their effect on the dynamics: they are shown to be necessary for diffusion. This work has been written up in an article accepted for publication in the Journal of Physical Chemistry. The ab initio potentials developed as above involve representing a system with formally charged monatomic ions. We extended the scope of the method significantly with technical developments to allow for the inclusion of molecular ions, such as the hydroxide ion, the sulphate ion or the uranyl ion, where the intraionic bonding has significant covalent character. The appropriate modifications of the MD code were made and a modified force-fitting procedure was developed. The new method was applied to Mg(OH)2 which is an important mineral (brucite) and to the melts of uranyl chloride which are of interest in nuclear waste reprocessing. Although we found good potentials were harder to obtain for these compounds, we found this arose from their layered structure rather than the molecular nature of the ions, and that our method could achieve a level of success approaching that used in the cryolite work on further iterations of the fitting process.

This thesis presents a detailed investigation of the capability of a modern three-dimensional Navier-Stokes solver to predict the secondary flows and losses in a linear cascade of high turning turbine rotor blades. Three codes were initially tested, to permit selection of the best of the available numerical solvers for this case. This program was then tested in more detail. Results showed that although very accurate prediction of the effects of inviscid fluid mechanics is now possible, the Reynolds stress modelling can have profound effects upon the quality of the solutions obtained. Solutions using two different calculation meshes, have shown that the results are not significantly grid dependent. The flowfield of the cascade was traversed with hot-wires to obtain measurements of the turbulent Reynolds stresses. A turbulence generating grid was placed upstream of the cascade, to produce a more realistic inlet turbulence intensity. Results showed that regions of high turbulent kinetic energy are associated with regions of high total pressure loss. Calculation of eddy viscosities from the Reynolds stresses showed that downstream of the -cascade the eddy viscosity is fairly isotropic. Evaluation of terms in the kinetic energy equation, also indicated that both the normal and shear Reynolds stresses are important as loss producing mechanisms in the downstream flow. The experimental Reynolds stresses have been compared with those calculated from the eddy viscosity and velocity fields of Navier-Stokes predictions using a mixing length turbulence model, a one equation model, and K - ϵ model. It was found that in the separated, shear flows, agreement was poor, although the K - ϵ model performed best. Further experimental work is suggested to obtain data with which to determine the accuracy of the models within the blade and endwall boundary layers.

Riparian areas are ecologically and economically critical habitats in the Canadian Prairies. An estimated 80% of riparian zones in North America are threatened by anthropogenic development. While riparian conservation is integrated into agricultural, watershed, and forestry best management practices across Canada, existing riparian health assessments are reliant on resource-intensive field surveys. The objective of this thesis was to develop a riparian health assessment using high-resolution remotely sensed imagery. Riparian health surveys were conducted along the La Salle River. High-resolution imagery and LiDAR data were integrated into an object-based image analysis of vegetation. Topographic analysis was conducted using a high-resolution DEM. These data were input into a linear discriminant classifier to model riparian health. Riparian health models containing both vegetation and topographic variables, and only vegetation variables, produced good agreement with field assessments. LiDAR data and the object-based image analysis method were successfully used to develop a remote riparian health assessment.