Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal.

Flight testing has been the historical standard for determining aircraft airworthiness - however, increases in the cost of flight testing and the accuracy of inexpensive CFD promote certification by analysis to reduce or replace flight testing. A framework is introduced to predict the performance in the special case of a modification to an existing, previously certified aircraft. This framework uses a combination of existing flight test or high fidelity data of the original aircraft as well as lower fidelity data of the original and modified configurations. Two methods are presented which estimate the model form uncertainty of the modified configuration, which is then used to conduct non-deterministic simulations. The framework is applied to an example aircraft system with simulated flight test data to demonstrate the ability to predict the performance and associated uncertainty of modified aircraft configurations. However, it is important that the models and methods used are applicable and accurate throughout the intended use domain. The factors and limitations of the framework are explored to determine the range of applicability of the framework. The effects of these factors on the performance and uncertainty results are demonstrated using the example aircraft system. The framework is then applied to NASA's X-57 Maxwell and each of its modifications. The estimated performance and associated uncertainties are then compared to the airworthiness criteria to evaluate the potential of the framework as a component to the certification by analysis process. / Doctor of Philosophy / Aircraft are required to undergo an airworthiness certification process to demonstrate the capability for safe and controlled flight. This has historically been satisfied by flight testing, but there is a desire to use computational analysis and simulations to reduce the cost and time required. For aircraft which are based on an aircraft which has already been certified, but contain minor changes, computational tools have the potential to provide a large benefit. This research proposes a framework to estimate the flight performance of these modified aircraft using inexpensive computational or ground based methods and without requiring expensive flight testing. The framework is then evaluated to ensure that it provides accurate results and is suitable for use as a supplement to the airworthiness certification process.

This dissertation investigates various topics concerning the interpretation of determiner phrases and their connection to individual entities. The first chapter looks at a phenomenon called telescoping, in which a quantificational expression appears to bind a pronominal form across sentence boundaries, at odds with commonly assumed and well motivated constraints on binding. I investigate the limited circumstances under which telescoping is available and argue that the mechanism that makes it available should respect said locality constraints. In particular, I argue that the impression of co-variation arises not because of binding by the initial quantificational expression, but because an of independent, albeit unpronounced, quantificational operator in the second sentence. I will show cases where the domains of these two quantificational operators are independent, incompatible with approaches that assume a single operator. This result also entails that no reference to constructed individuals, e.g. prototypical or average individuals is needed. In the second chapter, I look at the German lexical item lauter and argue that DPs headed by lauter are purely predicational. After presenting an overview of the various kinds of interpretations that a DP can receive, and some discussion objecting to the idea of treating these as cases of lexical ambiguity, I show data that illustrate that lauter DPs cannot receive many of these interpretations. At the end of the chapter, I speculate about ways in which purely predicative DPs may appear and be interpreted in some, but not all, positions that arguments typically occupy, resulting in a restricted distribution and less freedom in the range of interpretations. In the last chapter, I look at an instance of a semantically complex determiner, the English item any. Instead of adding to the discussion based on an investigation of any, I propose that this hidden semantic complexity has a transparent reflex in German, where the lexical item überhaupt spells out a logically independent part of the proposed meaning of any, namely its domain widening meaning.

Probabilistic behavior in perceiving the environment and take critical decisions have an inevitable role in human life. A decision is concerned with a choice among the available alternatives and is always subject to unknown elements concerning the future. The lack of complete data, insufficient scientific, behavioral, and industry development and of course defects in measurement methods, affect the reliability of an action’s outcome. Thus, having a proper estimation of this reliability or uncertainty could be very advantageous particularly when an individual or generally a subject is faced with a high risk. With the fact that there are always uncertainty elements whose values are unknown and these enter into a processes through multiple sources, it has been a primary challenge to design an efficient representation of confidence objectively. With the aim of addressing this problem, a variety of researches have been conducted to introduce frameworks in metrology of uncertainty quantification that are comprehensive enough and have transferability into different areas. Moreover, it’s also a challenging task to define a proper index that reflects more aspects of the problem and measurement process. With significant advances in Artificial Intelligence in the past decade, one of the key elements, in order to ease human life by giving more control to machines, is to heed the uncertainty estimation for a prediction. With a focus on measurement aspects, this thesis attends to demonstrate how a different measurement index affects the quality of evaluated predictive uncertainty of neural networks. Finally, we propose a novel index that shows uncertainty values with the same or higher quality than existing methods which emphasizes the benefits of having a proper measurement index in managing the risk of the outcome from a classification model.

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This study has two parts. In the first part we develop a computational approach to the solution of an inverse modelling problem concerning the material properties of electrolytes used in Lithium-ion batteries. The dependence of the diffusion coefficient and the transference number on the concentration of Lithium ions is reconstructed based on the concentration data obtained from an in-situ NMR imaging experiment. This experiment is modelled by a system of 1D time-dependent Partial Differential Equations (PDE) describing the evolution of the concentration of Lithium ions with prescribed initial concentration and fluxes at the boundary. The material properties that appear in this model are reconstructed by solving a variational optimization problem in which the least-square error between the experimental and simulated concentration values is minimized. The uncertainty of the reconstruction is characterized by assuming that the material properties are random variables and their probability distribution estimated using a novel combination of Monte-Carlo approach and Bayesian statistics. In the second part of this study, we carefully analyze a number of secondary effects such as ion pairing and dendrite growth that may influence the estimation of the material properties and develop mathematical models to include these effects. We then use reconstructions of material properties based on inverse modelling along with their uncertainty estimates as a framework to validate or invalidate the models. The significance of certain secondary effects is assessed based on the influence they have on the reconstructed material properties. / Thesis / Doctor of Philosophy (PhD)

This dissertation addresses model form uncertainty quantification, non-deterministic simulations, and sensitivity analysis of the results of these simulations, with a focus on application to analysis of unmanned aircraft systems. The model form uncertainty quantification utilizes equation error to estimate the error between an identified model and flight test results. The errors are then related to aircraft states, and prediction intervals are calculated. This method for model form uncertainty quantification results in uncertainty bounds that vary with the aircraft state, narrower where consistent information has been collected and wider where data are not available. Non-deterministic simulations can then be performed to provide uncertainty quantified estimates of the system performance. The model form uncertainties could be time varying, so multiple sampling methods were considered. The two methods utilized were a fixed uncertainty level and a rate bounded variation in the uncertainty level. For analysis using fixed uncertainty level, the corner points of the model form uncertainty were sampled, providing reduced computational time. The second model better represents the uncertainty but requires significantly more simulations to sample the uncertainty. The uncertainty quantified performance estimates are compared to estimates based on flight tests to check the accuracy of the results. Sensitivity analysis is performed on the uncertainty quantified performance estimates to provide information on which of the model form uncertainties contribute most to the uncertainty in the performance estimates. The proposed method uses the results from the fixed uncertainty level analysis that utilizes the corner points of the model form uncertainties. The sensitivity of each parameter is estimated based on corner values of all the other uncertain parameters. This results in a range of possible sensitivities for each parameter dependent on the true value of the other parameters. / Ph. D.

Increasing awareness to global climate change has drastically focused attention on finding solutions to reduce environmental impacts while still providing sufficient food for the increasing world population. Beneficial Nitrogen Fixing (BNF) microbes provide a possible solution by delivering biological nitrogen to plants resulting in reduced environmental impacts due to fertilizer runoff and eutrophication. One well studied model is that of Sinorhizobium meliloti and its legume host Medicago sativa (alfalfa), the fourth largest USA crop used for animal feed. Advancing research for this symbiosis model can provide solutions to enhance yield while minimizing environmental impacts. Chapter 2 focuses on the deviation of the S. meliloti chemotaxis system from the enteric paradigm. Quantitative immunoblots determined the cellular amounts of chemotaxis proteins. Overall, chemotaxis protein levels were approximately 10-fold lower in S. meliloti compared to B. subtilis and E. coli. Focusing on cellular stoichiometric ratios, S. meliloti generally exhibits drastically higher values for CheB, CheR, and CheY to the histidine kinase CheA monomer compared to E. coli and B. subtilis. Chapter 3 characterizes the role of McpX to quaternary ammonium compound (QAC) sensing. QACs are exuded by germinating alfalfa seeds. In vitro binding assays were performed to determine ligand binding characteristics. S. meliloti chemotaxis behavior to QACs was analyzed in in vivo capillary assays under real-time imaging. These studies strengthen our knowledge of the chemotaxis system in the symbiosis model of S. meliloti and alfalfa. The data can further be used to create a mathematical model of the dynamics of bacteria-host interaction. The results can be used to optimize chemotaxis to host plants to improve crop yield and protect watersheds. / Master of Science / Increasing awareness of global climate change has drastically focused attention on finding solutions to reduce environmental impacts while still providing sufficient food for the increasing world population. Beneficial Nitrogen Fixing (BNF) microbes may offer a feasible solution in reducing environmental impacts by supplying biological nitrogen to plants, which reduces fertilizer use and ultimately runoff. One-well studied beneficial microbial model is that of Sinorhizobium meliloti and its legume host Medicago sativa (alfalfa), the fourth most cultivated USA crop used for animal feed. Advancing research for this symbiosis model can provide solutions to enhance crop yield while diminishing environmental impacts.