Thesis (Ph. D.)--Chalmers tekniska högskola and Göteborgs universitet, 2006. / Includes bibliographical references (p. 219-224) and index.

Chalmers Univ. of Technology and Göteborg Univ., Diss.--Göteborg, 2006.

This dissertation presents a methodology to simulate the dispersion of water droplets in the air flow typical of an Icing Tunnel. It is based on the understanding the physical parameters that influence the uniformity and the distribution of cloud of droplets in the airflow and to connect them with analytical parameters which may be used to describe the dispersion process. Specifically it investigates the main geometrical and physical parameters contributing to the droplets dispersion at different tunnel operative conditions, finding a consistent numerical approach to reproduce the local droplets dynamic, quantifying the possible limits of commercial CFD methods, pulling out the empirical parameters/constant needing to simulate properly the local conditions and validating the results with calibrated experiment. An overview of the turbulence and multiphase flow theories, considered relevant to the Icing Tunnel environment, is presented as well as basic concepts and terminology of particle dispersion. Taylor’s theory of particle dispersion has been taken as starting point to explain further historical development of discrete phase dispersion. Common methods incorporated in commercial CFD software are explained and relative shortcomings underlined. The local aerodynamic condition within tunnel, which are required to perform the calculation with the Lagrangian particle equation of motions, are generated numerically using different turbulent models and are compared to the historical K-ε model. Verification of the calculation is performed with grid independency studies. Stochastic Separated Flow methods are applied to compute the particle trajectories. The Discrete Random Walk, as described in the literature, has been used to perform particle dispersion analysis. Numerical settings in the code are related to the characteristics of the local turbulent condition such as turbulence intensity and length scales. Cont/d.

Stochastic models of biochemical reaction networks are used for understanding the properties of molecular regulatory circuits in living cells. The state of the cell is defined by the number of copies of each molecular species in the model. The chemical master equation (CME) governs the time evolution of the the probability density function of the often high-dimensional state space. The CME is approximated by a partial differential equation (PDE), the Fokker-Planck equation and solved numerically. Direct solution of the CME rapidly becomes computationally expensive for increasingly complex biological models, since the state space grows exponentially with the number of dimensions. Adaptive numerical methods can be applied in time and space in the PDE framework, and error estimates of the approximate solutions are derived. A method for splitting the CME operator in order to apply the PDE approximation in a subspace of the state space is also developed. The performance is compared to the most widely spread alternative computational method.

Thesis (Ph.D)--Architecture, Georgia Institute of Technology, 2010. / Committee Chair: Godfried Augenbroe; Committee Member: Ellis Johnson; Committee Member: Pieter De Wilde; Committee Member: Ruchi Choudhary; Committee Member: Russell Gentry. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (Ph. D.)--Industrial and Systems Engineering, Georgia Institute of Technology, 2007. / Yorai Wardi, Committee Member ; Gunter Sharp, Committee Member ; Spiridon Reveliotis, Committee Member ; Leon F. McGinnis, Committee Member ; Chen Zhou, Committee Chair.

Thesis (Ph.D.) - University of Queensland, 2005. / Includes bibliography.

Thesis (Ph. D.)--University of Texas at Austin, 2008. / Vita. Includes bibliographical references.

Bovine tuberculosis (bTB) is one of the most complex, persistent and controversial problems facing the British cattle industry. It is also potentially zoonotic and so has public health implications. The incidence of the disease has been increasing in Great Britain for more than 20 years and is now endemic in southwest regions of the country and occurs sporadically elsewhere. Scotland records very few incidences of bTB and was declared as an Officially bTB free (OTF) region in 2009 for the purposes of cattle trading. However, in order to retain its OTF status Scotland must continue to demonstrate the ability to report low level of disease prevalence whilst maintaining its vigilance to potential new outbreaks. This thesis uses a variety of epidemiological and statistical models to evaluate the ongoing control strategies for bTB in Scottish cattle herds and highlight potential limitations to the current surveillance programmes. In the absence of an established wildlife reservoir, livestock movements are considered the primary mechanism for introduction of bTB into cattle herds. I use movement and bTB data to estimate the within-herd incidence rate for each infected farm in Scotland. The results suggest that this rate varies across farms, and is dependent on the herd size and length of disease exposure. These incidence rates are then used to parameterise a multi-herd dynamic model using stochastic simulations that incorporate multiple disease transmission pathways. With this approach I evaluate the impact of different routine test protocols on the overall simulated epidemics. Based on the model outcome, abattoir surveillance alone is not sufficient to maintain infection at a low constant level. Whilst adapting to more frequent routine testing regime can reduce disease incidence, the sensitivity of the surveillance methods can also have a big impact on the long term stability of the disease prevalence and can act as the main barrier to eradicating the disease from low incidence regions. The single intra-dermal comparative cervical tuberculin (SICCT) test used in the current routine herd surveillance relies on stimulating an immune response and observing delayed hypersensitivity reactions in infected animals. The test suffers highly variable, and often poor, sensitivity with current estimates ranging from 50% to 80%. The lower sensitivities may be associated with early stages of infection, concurrent illness, and farm management conditions as well as the presence of sub-clinically infected carriers that can potentially escape detection. In addition, there was evidence that physiological stress can have a marked effect on the immune responses in animals affected with bTB. I conducted two different types of case-control analyses to investigate the potential effect of stress related events on the outcome of the SICCT test. In the first analysis, a matched design is implemented to examine the effect of recent calving on reactivity to the SICCT. SICCT test positive cattle (cases) were matched with test negative (control) animals within the same farm. By selecting herd-mates (i.e. animals within the same herd at the same time), the study aims to control for space and time. Furthermore, animal age and breed were used as additional selection criteria to control for previous exposure period and potential genetic variation to the reaction of SICCT test outcome. Results from a conditional logistic regression model indicated that animals calved within 60 days prior to test were less likely to respond to the SICCT test in comparison to non-recently calved animals, and that this effect was strongest in the first 2 weeks of the post-partum period. In the second analysis, animals identified with gross pathology at post-mortem (TB-like lesion and/or bacteria culture) and that were SICCT test negative within 60 days prior to slaughter (representing false negative) were compared with confirmed test positives (true positives). Results from multivariable logistic regression model suggested that the probability of missed infection by SICCT test increases with age and male cattle have higher odds of being a false negative compared to females. Repeated skin tests within 60 and 120 days, as well as recent movement and parturition, were all statistically associated with false negative test outcome. Under future surveillance systems, these results could be used to adjust the timings of testing relative to calving, movements and previous test occasions in order to minimise the risks of false negative test results. Alternatively, increasing the threshold for reactor definition in animals under these categories could be considered to complement the poor test sensitivity.

This dissertation presents a methodology to simulate the dispersion of water droplets in the air flow typical of an Icing Tunnel. It is based on the understanding the physical parameters that influence the uniformity and the distribution of cloud of droplets in the airflow and to connect them with analytical parameters which may be used to describe the dispersion process. Specifically it investigates the main geometrical and physical parameters contributing to the droplets dispersion at different tunnel operative conditions, finding a consistent numerical approach to reproduce the local droplets dynamic, quantifying the possible limits of commercial CFD methods, pulling out the empirical parameters/constant needing to simulate properly the local conditions and validating the results with calibrated experiment. An overview of the turbulence and multiphase flow theories, considered relevant to the Icing Tunnel environment, is presented as well as basic concepts and terminology of particle dispersion. Taylor’s theory of particle dispersion has been taken as starting point to explain further historical development of discrete phase dispersion. Common methods incorporated in commercial CFD software are explained and relative shortcomings underlined. The local aerodynamic condition within tunnel, which are required to perform the calculation with the Lagrangian particle equation of motions, are generated numerically using different turbulent models and are compared to the historical K-ε model. Verification of the calculation is performed with grid independency studies. Stochastic Separated Flow methods are applied to compute the particle trajectories. The Discrete Random Walk, as described in the literature, has been used to perform particle dispersion analysis. Numerical settings in the code are related to the characteristics of the local turbulent condition such as turbulence intensity and length scales. Cont/d.