In recent years Computational Fluid Dynamics (CFD) has been extensively used in the study of the indoor environment and the thermal comfort conditions for the design of modern buildings, however, there remains the need to thoroughly evaluate the accuracy of the results given by CFD methods. In the present work, numerical investigations of the indoor thermal environment in the atria of two existing buildings and in a simple three-storey atrium building design have been undertaken using CFD techniques. The initial work involved the evaluation of various turbulence models and a radiation model used in CFD simulations for the prediction of the thermal environment in atria of different geometrical configurations in two buildings for which experimental data is available. The airflow patterns and temperature distributions were determined, under both forced and hybrid ventilation conditions and thermal comfort conditions were evaluated. The numerical predictions were compared with the available experimental measurements and, in general, good agreement was obtained between the numerical and experimental results. After the evaluation of the adequacy of available turbulence models and the validation of the accuracy of the CFD model used, a simple full-scale three-storey atrium building was modeled to explore the potential of using buoyancy-driven natural ventilation. The validated CFD model was used to determine the ventilation flow rates, airflow patterns, and temperature distributions in the building. The dynamic effect of the thermal mass of the external walls on the performance of the building was also investigated using transient CFD simulations. Atria with various geometrical configurations were studied in order to investigate the effect of atrium design changes on the air flow and temperature distributions in the simple atrium building considered. A parametric study was carried out to assess the sensitivity of the ventilation performance to the change in various geometric and solar parameters. On the basis of this parametric study, a few changes were carried out in the design of the building to examine their effect on ventilation performance. Finally, the use of night ventilation in the atrium building was explored and it was found that night ventilation can be increased by using hot water circulation in the chimney walls. / Thesis (Ph.D, Mechanical and Materials Engineering) -- Queen's University, 2012-07-22 12:57:00.947

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This thesis studies the one-factor latent trait model for binary data. In examines the sensitivity of the model when the assumptions about the model are violated, it investigates the information about the prior distribution when the model is estimated semi-parametrically and it also examines the goodness-of-fit of the model using Monte-Carlo simulations. Latent trait models are applied to data arising from psychometric tests, ability tests or attitude surveys. The data are often contaminated by guessing, cheating, unwillingness to give the true answer or gross errors. To study the sensitivity of the model when the data are contaminated we derive the Influence Function of the parameters and the posterior means, a tool developed in the frame of robust statistics theory. We study the behaviour of the Influence Function for changes in the data and also the behaviour of the parameters and the posterior means when the data are artificially contaminated. We further derive the Influence Function of the parameters and the posterior means for changes in the prior distribution and study empirically the behaviour of the model when the prior is a mixture of distributions. Semiparametric estimation involves estimation of the prior together with the item parameters. A new algorithm for fully semiparametric estimation of the model is given. The bootstrap is then used to study the information on the latent distribution than can be extracted from the data when the model is estimated semiparametrically. The use of the usual goodness-of-fit statistics has been hampered for latent trait models because of the sparseness of the tables. We propose the use of Monte-Carlo simulations to derive the empirical distribution of the goodness-of-fit statistics and also the examination of the residuals as they may pinpoint to the sources of bad fit.

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No description available.

Nonadiabatic dynamical processes are ubiquitous in chemistry and biology. Such events are directly connected to the treatment of energetically close lying states which gives rise to strong vibronic interactions in which case the Born-Oppenheimer approximation tends to break down. In case of biradicals, nonadiabatic events are facilitated by conical intersections, as a result of symmetry lowering of degenerate electronic states due to Jahn-Teller distortion. A central problem in the treatment of the nonadiabatic molecular dynamics is posed by the representation of potential energy surfaces. A point by point calculation of a potential energy surface on a multi-dimensional grid is very cumbersome and in general does not provide with an analytical functional form of the potential. This becomes even more complicated when the adiabatic surfaces have cusps, where the function becomes non-differentiable. Vibronic model Hamiltonians, which represent the potential in the form of a potential matrix which contains the electronic energies as well as the couplings in a diabatic basis. A Taylor series expansion of the potential matrix can be done to get a smooth analytical functional form of the potential matrix elements. These models can then be used to perform nuclear dynamics using either exact diagonalization time-independent method or the wavepacket propagation based time-dependent methods. Thus, vibronic models provide a compact representation of complicated coupled potential energy surfaces, which can be used in conjunction with non-adiabatic nuclear dynamics Vibronic models have been constructed for selected biradicals, for which photodetachment spectra have been simulated using the time-independent (VIBRON) as well as time-dependent (MCTDH) methods. Consistent results have been obtained with both the approaches for small systems. This also assures the use of MCTDH program for larger systems, where the time-independent methods are not applicable. Moreover, for biradicals, the parent anionic state also undergoes a Jahn-Teller distortion, or often the ground state potential energy surface is highly anharmonic in nature. This requires the description of anionic ground state by a vibronic model. Therefore, in order to simulate the photodetachment spectra of biradicals, three vibronic models are constructed for each simulation. The first model describes the ground and excited states of the parent anionic (neutral) species. Two other vibronic models describe singlet and triplet states of the target neutral (cation) species, and the spectrum is simulated using the vibronic ground state(s) of the anion (neutral) as the absorbing state in VIBRON/MCTDH. The electronic states and vibronic model parameters are obtained using the IP-EOM-CCSD and DIP-STEOM-CCSD methodology as coded in the ACESII quantum chemistry program package. The photodetachment spectra of nitrate radical, cyclobutadiene negative ion and trimethylene negative ion have been studied using this methodology.

abstract: ABSTRACT The purpose of this study was to investigate the effects of E Bucks, a simulated classroom economy (a token economy system), in business classes on students' grades, absences, and tardiness. The study compared these variables in classes using E Bucks to those in similar classes before E Bucks was initiated. The following research questions were addressed: (a) How did the mean term grades in business classes that included E Bucks compare to those in similar classes prior to the E Bucks implementation? (b) How did the mean number of student absences in business classes that included E Bucks compare to those in similar classes prior to the E Bucks implementation? (c) How did the mean number of student tardies in business classes that included E Bucks compare to those in similar classes prior to the E Bucks implementation? Four teachers in 3 high schools in Phoenix, Arizona, participated in the study that included 22 sections of business classes with a total of 568 students. All participating teachers implemented the token economy voluntarily, although some implemented the program more consistently than others. All of the teachers administered district-aligned assessments with the same terms/occasions throughout the district. Archival data (term grades, attendance, and tardies) from 3 years of business, technology, and marketing courses were collected and analyzed. The results of 4 analyses of variance examining the dependent variables of grades, absences, and tardies were mixed. The results demonstrated significance for some but not all of the teachers' classes on all 3 dependent variables. In 1 of the 4 analyses 2 teachers had approached significant increases in grades when students were "paid" for grades. The same two teachers had nonsignificant decreases in the mean number of student absences during the grading period students were "paid" for grades. Recommendations included studying a larger number of students and measuring the impact of gender and socioeconomic status on the effects of the E Bucks simulation. / Dissertation/Thesis / Ed.D. Educational Leadership and Policy Studies 2010

Les silices nanostructurées sont des matériaux ultra-poreux (plus de 80% de porosité) utilisés pour la confection de Panneaux Isolants sous Vides (PIV). Elles possèdent des propriétés thermiques exceptionnelles, mais de piètres propriétés mécaniques.L’enjeu de cette thèse est d’étudier ces matériaux aux échelles de la particule (quelques nm), de l’agrégat de particules (quelques dizaines de nm) et de l’agglomérat d’agrégats (quelques centaines de nm), afin de mieux comprendre les comportements mécanique et thermique à l’aide de simulations, et de proposer des pistes pour améliorer le compromis thermique / mécanique. La nature particulaire du matériau et son caractère multi-échelle justifient l’utilisation de méthodes de simulations discrètes (DEM : Discrete Element Method). Un modèle original permettant de générer des agrégats à morphologiecontrôlée (dimension fractale, rayon de giration, porosité) est proposé. Le comportement à la compaction des agrégats est ensuite étudié par simulations DEM. Une approche par cyclage à faible densité a été développée pour obtenir des arrangements initiaux réalistes d’agrégats. La prépondérance des phénomènes adhésifs dans le système rend en effet celui-ci très sensible à l’arrangement initial. La réponse en traction des structures générées par compaction est également évaluée.L’influence de la morphologie des agrégats, de l’adhésion et du frottement ont été étudiées. L’accent est mis sur la comparaison de deux types de silices (pyrogénées et précipitées) présentant des morphologies différentes et pour lesquelles des données expérimentales permettent une confrontation avec les simulations. Les simulations présentées permettent d’apporter des réponses sur l’origine des différences de comportement mécanique observées expérimentalement pour ces deux types de silice.Une modélisation de la conductivité thermique du matériau, avec une focalisation sur la conductivité solide, est également proposée. / Nanostructured silicas are ultra-porous materials (more than 80 % porosity) used to make Vacuum Insulation Panels (VIP).They have exceptional thermal properties, but poor mechanical properties. The goal of this thesis is to study these materials at the scale of the particle (a few nm), the aggregate of particles (a few tens of nm) and the agglomerate of aggregates (a few hundred nm), in order to better understand mechanical and thermal behaviour using simulations, and to propose ways to improve the thermal / mechanical compromise. The particulate nature of the material and its multi-scale naturejustify the use of Discrete Element Methods (DEM). An original model allowing to generate aggregates with controlledmorphology (fractal dimension, radius of gyration, porosity) is proposed. The compaction behaviour of the aggregates is then studied by DEM. A low-density cycling approach has been developed to obtain realistic initial aggregate arrangements.The preponderance of adhesive phenomena in the system makes it very sensitive to the initial arrangement. The tensile response of structures generated by compaction is also evaluated. The influence of aggregate morphology, adhesion and friction were studied. Emphasis is placed on the comparison of two types of silica (pyrogenic and precipitated) with different morphologies and for which experimental data allow a comparison with simulations. The simulations presented allow us to provide answers on the origin of the differences in mechanical behaviour observed experimentally for these two types of silica.A modeling of the thermal conductivity of the material, with a focus on solid conductivity, is also proposed.

This dissertation addresses various aspects of estimation and inference for multivariate stochastic processes with jumps. The first chapter develops an unbiased Monte Carlo estimator of the transition density of a multivariate jump-diffusion process. The drift, volatility, jump intensity, and jump magnitude are allowed to be state-dependent and non-affine. The density estimator proposed enables efficient parametric estimation of multivariate jump-diffusion models based on discretely observed data. Under mild conditions, the resulting parameter estimates have the same asymptotic behavior as maximum likelihood estimators as the number of data points grows, even when the sampling frequency of the data is fixed. In a numerical case study of practical relevance, the density and parameter estimators are shown to be highly accurate and computationally efficient. In the second chapter, I examine continuous-time stochastic volatility models with jumps in returns and volatility in which the parameters governing the jumps are allowed to switch according to a Markov chain. I estimate the parameters and the latent processes using the S&P 500 and Nasdaq indices from 1990 to 2014. The Markov-switching parameters characterize well the periods of market stress, such as those in 1997-1998, 2001 and 2007-2010. Several statistical tests favor the model with Markov-switching jump parameters. These results provide empirical evidence about the state-dependent and time-varying nature of asset price jumps, a feature of asset prices that has recently been documented using high-frequency data. The third chapter considers applying Markov-switching affine stochastic volatility models with jumps in returns and volatility, where the jump parameters are not regime-switching. The estimation is performed via Markov Chain Monte Carlo methods, allowing to obtain the latent processes induced by the structure of the models. Furthermore, I propose some misspecification tests and develop a Markov-switching test based on the odds ratios. The parameters and the latent processes are estimated using the S&P 500 index from 1970 to 2014. I show that the S&P 500 stochastic volatility exhibits a Markov-switching behavior, and that most of the high volatility regimes coincide with the recessions identified ex-post by the National Bureau of Economic Research.