Many Small Unmanned Aerial Vehicles (SUAV) are driven by small scale, fixed blade propellers. The flow produced by the propeller, known as the propeller slipstream, can have significant impact on SUAV aerodynamics. In the design and analysis process for SUAVs, numerous Computational Fluid Dynamic (CFD) simulations of the coupled aircraft and propeller are often conducted which require a time-averaged, steady-state approximation of the propeller for computational efficiency. Most steady-state propeller models apply an actuator disk of momentum sources to model the thrust and swirl imparted to the flow field by a propeller. These momentum source models are based on simplified theories which lack accuracy. Currently, the most common momentum source models are based on blade element theory. Blade element theory discretizes the propeller blade into airfoil sections and assumes them to behave as two-dimensional (2D) airfoils. Blade element theory neglects many 3D flow effects that can greatly affect propeller performance limiting its accuracy and range of application. The research work in this dissertation uses a surrogate modeling method to develop a more accurate momentum source propeller model. Surrogate models for the time averaged thrust and swirl produced by each blade element are trained from a database of timeurate, highidelity 3D CFD propeller simulations. Since the surrogate models are trained from these highidelity CFD simulations, various 3D effects on propellers are inherently accounted for such as tip loss, hub loss, post stall effect, and element interaction. These efficient polynomial response surface surrogate models are functions of local flow properties at the blade elements and are embedded into 3D CFD simulations as locally adaptive momentum source terms. Results of the radial distribution of thrust and swirl for the steady-state surrogate propeller model are compared to that of time-dependent, highidelity 3D CFD propeller simulations for various aircraft-propeller coupled situations. This surrogate propeller model which is dependent on local flow field properties simulates the time-averaged flow field produced by the propeller at a momentum source term level of detail. Due to the nature of the training cases, it also captures the accuracy of time-dependent 3D CFD propeller simulations but at a much lower cost.

Conversion of natural grasslands for agricultural uses and intensification of agricultural production has been a major cause of natural ecosystem fragmentation and biodiversity loss. Diversifying agricultural landscapes by adding semi-natural grasslands can potentially help couple agricultural production (i.e., providing food, fiber, and energy to a growing human population) with environmental stewardship, enhancing ecosystem health, and increasing biodiversity. To ensure long-term ecological benefits of buffers and to maintain them as suitable habitat for prairie-associated species, periodic disturbance is required to keep buffers in an early-successional grassland plant community. In this dissertation, I fill knowledge gaps about biodiversity of semi-natural grassland buffers within agroecosystems. I investigate influence of prairie-history on buffer forb communities and effects of disking and burning on semi-natural grassland buffer plant and butterfly communities. I also investigate suitability of using butterflies as surrogates of birds and plants on semi-natural grassland buffers. Prairie-history influenced buffer forb communities, and disking increased forb richness and abundance. Disturbance guild butterflies responded positively to disking, most likely due to increased availability of nectar-rich plants. Grassland guild butterflies were not impacted negatively by disking or burning. Responses of plants and butterflies to disking and burning varied between sampling years. Effects of disking in fall 2007 persisted for two growing seasons, but I observed little effects of disking in fall 2008. Butterflies, with the possible exception of Pearl Crescent, were unsuitable as surrogates for birds. In contrast, butterflies, including Pearl Crescent, showed suitable or marginally suitable correlations with plants. Results of my study should help agricultural producers accomplish environmental conservation objectives and provide science-based information for developing and refining USDA practice standards and policy.

The focus of this work is an industrial refinery model developed by TotalEnergies SE. The model is a sparse, large-scale, nonconvex, mixed-integer nonlinear program (MINLP). The nonconvexity of the problem arises from the many bilinear, trilinear, fractional, logarithmic, exponential, and sigmoidal terms. In order to account for various sources of uncertainty in refinery planning, the industrial refinery model is extended into a two-stage stochastic program, where binary scheduling decisions must be made prior to the realization of the uncertainty, and mixed-integer recourse decisions are made afterwards. Two case studies involving uncertainty are formulated and solved in order to demonstrate the economic and logistical benefits of robust solutions over their deterministic counterparts. A full-space solution strategy is proposed wherein the integrality constraints are relaxed and a multi-step initialization strategy is employed in order to gradually approach the feasible region of the multi-scenario problem. The full-space solution strategy was significantly hampered by difficulties with finding a feasible point and numerical problems. In order to facilitate the identification of a feasible point and to reduce the incidence of numerical difficulties, a hybrid surrogate refinery model was developed using the ALAMO modelling tool. An evaluation procedure was employed to assess the surrogate model, which was shown to be reasonably accurate for most output variables and to be more reliable than the high-fidelity model. Feasible solutions are obtained for the continuous relaxations of both case studies using the full-space solution strategy in conjunction with the surrogate model. In order to solve the original MINLP problems, a decomposition strategy based on the generalized Benders decomposition (GBD) algorithm is proposed. The binary decisions are designated as complicating variables that, when fixed, reduce the full-space problem to a series of independent scenario subproblems. Through the application of the GBD algorithm, feasible mixed-integer solutions are obtained for both case studies, however optimality could not be guaranteed. Solutions obtained via the stochastic programming framework are shown to be more robust than solutions obtained via a deterministic problem formulation. / Thesis / Master of Applied Science (MASc)

Surrogate models are indispensable in the analysis of engineering systems. The quality of surrogate models is determined by the data quality and the model class but achieving a high standard of them is challenging in complex engineering systems. Heterogeneity, implicit constraints, and extreme events are typical examples of the factors that complicate systems, yet they have been underestimated or disregarded in machine learning. This dissertation is dedicated to tackling the challenges in surrogate modeling of complex engineering systems by developing the following machine learning methodologies. (i) Partitioned active learning partitions the design space according to heterogeneity in response features, thereby exploiting localized models to measure the informativeness of unlabeled data. (ii) For the systems with implicit constraints, failure-averse active learning incorporates constraint outputs to estimate the safe region and avoid undesirable failures in learning the target function. (iii) The multi-output extreme spatial learning enables modeling and simulating extreme events in composite fuselage assembly. The proposed methods were applied to real-world case studies and outperformed benchmark methods. / Doctor of Philosophy / Data-driven decisions are ubiquitous in the engineering domain, in which data-driven models are fundamental. Active learning is a subdomain in machine learning that enables data-efficient modeling, and extreme spatial modeling is suitable for analyzing rare events. Although they are superb techniques for data-driven modeling, existing methods thereof cannot effectively address modern engineering systems complicated by heterogeneity, implicit constraints, and rare events. This dissertation is dedicated to advancing active learning and extreme spatial modeling for complex engineering systems by proposing three methodologies. The first method is partitioned active learning that efficiently learns systems, changing their behaviors, by localizing the information measurement. Second, failure-averse active learning is established to learn systems subject to implicit constraints, which cannot be analytically solved, and to minimize constraint violations. Lastly, the multi-output extreme spatial model is developed to model and simulate rare events that are associated with extremely large values in the aircraft manufacturing system. The proposed methods overcome the limitations of existing methods and outperform benchmark methods in the case studies.

In recent times data-driven approaches to parametrically optimize and explore building geometry has been proven to be a powerful tool that can replace computationally expensive and time-consuming simulations for energy prediction in the early design process. In this research, we explore the use of surrogate models, i.e. efficient statistical approximations of expensive physics-based building simulation models, to lower the computational burden of large-scale building geometry analysis. We try different approaches and techniques to train a machine learning model using multiple datasets to analyze the impact of geometry and envelope features on the energy efficiency of buildings. These contributions are presented in the form of two conference papers and one journal paper (being prepared for submission) that iteratively build up the underlying methodology. The first conference paper contains preliminary experiments using 4 manually generated building geometries for office buildings. Data were generated by simulating various building samples in EnergyPlus for different geometries. We used the generated data to train a machine learning model using support vector regression. We trained two separate models for predicting heating and cooling loads. The lesson learned from this first experiment was that the prediction of the models was not great due to insufficient geometric features explaining the variability in geometry and the lack of sufficient data for varied geometries. The second conference paper developed a novel dataset of 38,000 building energy models for varied geometry using 2D images of real-world residences. We developed a workflow in the Grasshopper/Rhino environment which can convert 2D images of a floor plan into a vector format then into a building energy model ready to be simulated in EnergyPlus. The workflow can also extract up to 20 geometric features from the model, to be used as features in the machine learning process. We used these features and the simulation results to train a neural network-based surrogate model. A sensitivity analysis was performed to understand the impact and importance of each feature to the energy use of the building. From the results of the experiment, we found that off-the-shelf neural network-based surrogates provided with engineered features can very well emulate the desired simulation outputs. We also repeated the experiment for 6 different climatic zones across Canada to understand the impact of geometric features across various climates; these findings are presented in an appendix. iv In the journal paper, we explored two different methodologies to train surrogate models: monolithic and component-based. We explored the component-based modeling technique as it allows the model to be more versatile if we need to add more components to it, ultimately increasing the usability of the model. We conducted further experiments by adding complexity to the geometry surrogate model. We introduced 10 envelope features as an input to the surrogate along with the 20 geometric features. We trained 6 different surrogate models using different datasets by varying geometric and envelope features. From the results of the experiment, we found that the monolithic model performs the best but the component-based surrogate also falls into an acceptable range of accuracy. From the overall results across the three papers, we see that simple neural network-based surrogate models perform really well to emulate simulation outcomes over a wide variety of geometries and envelope features / Graduate

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Disturbance is one of the central concepts explaining how diversity arises and is perpetuated in ecological time. A good model system for testing hypotheses related to disturbance is the longleaf pine ecosystem in the southeastern U.S. because in this ecosystem frequent, low-severity fires acts as a disturbance that maintains a unique vegetation structure and high species richness. Vegetation structure influences animal distributions; in fire-dependent ecosystems many animals rely on open-structured, fire-maintained vegetation but shrubs and trees encroach into fire-dependent ecosystems where fire has been excluded. Prescribed burning and mechanical removal are commonly used as restoration tools to control encroachment. To better assess and compare the restoration potential of these tools, a more thorough understanding of how they change vegetation structure and habitat suitability for animals is necessary. The southeastern U.S. is a diversity hot-spot for amphibians, many of which require ephemeral wetlands embedded in longleaf pine uplands for the aquatic phase of their life cycle. Amphibian diversity has been declining in recent decades and habitat loss/degradation has been cited as one of the leading causes. Although often overlooked in studies of fire ecology, the ephemeral wetlands required by many amphibians are also fire-dependent habitats that have been negatively impacted by lack of fire. To understand how disturbance interacts with wetland vegetation and aquatic-phase amphibians, three disturbance treatments meant to mimic the effects of natural disturbance on vegetation structure were applied randomly to 28 dry ephemeral iii wetlands in the Lower Coastal Plain of South Carolina, U.S. The treatments consisted of early growing-season prescribed fire, mechanical vegetation removal (a proposed fire surrogate), and a combination of mechanical removal plus fire; some sites were left untreated for reference. Vegetation structure was quantified and amphibian assemblages were monitored before and after treatments. In addition, one species of amphibian was used in a tadpole survival experiment to examine differences in performance among treatments. Other factors that could be affected by treatments and in turn influence amphibians were measured, including water chemistry, wetland depth, quantity and quality of epilithon, and leaf litter composition. Amphibian survival was lowest, and species depauperateness highest in untreated wetlands. Depauperateness of species whose range was restricted to the range of longleaf pine was lowest in sites that had mechanical treatment plus fire. The mechanical plus fire treatment created the most open vegetation structure with lowest leaf litter accumulation, especially of hardwood litter, conditions correlated with high amphibian survival and diversity. When data from this study was combined with data from a previous study of similar nearby wetlands, a pattern emerged in which one suite of species was absent from recently burned sites, while an entirely different suite of species was absent from long-unburned sites. This evidence suggests that disturbance is related to a shift in amphibian assemblage possibly due to changes in vegetation structure and perhaps wetland ecology in general, from an algal-based system maintained by frequent fire to a detrital-based system that develops in the absence of fire

Despite numerous studies demonstrating the effectiveness of Restricted Crossing U-Turn (RCUT) intersection design, its implementation remains uneven and close to zero in some large states such as California. This research provides a comprehensive framework to estimate the operational and safety performance of future RCUT designs in California. The framework is demonstrated for five intersections located on high-speed rural expressways in California using VISSIM microsimulation models to measure operational performance for each intersection including the base condition with the existing Two-Way Stop-Controlled (TWSC) intersection and two RCUT designs. To evaluate future safety performance, the microsimulation models were further utilized to compile vehicle trajectory data to use with the Surrogate Safety Assessment Model (SSAM) to develop a surrogate measure-based approach to estimating future safety performance. Detailed Intersection Control Evaluation (ICE) studies found that the RCUT was cost-effective and the preferred alternative. This framework may be applied to the analysis of locations where a RCUT intersection may be appropriate. The framework demonstrated here may be used by agencies to estimate the future benefits of the first-time application of treatments that have been successful elsewhere. Based on simulation results, the proposed RCUT designs reduced or eliminated the more severe crossing conflicts.

The increasingly urbanized world needs a solution to solve one of the most difficult problems – traffic congestion and safety. Researchers, consultants, and local officials are all attempting to solve these problems with different methods. However, it is apparent that understanding the driving behaviors on the roadway network and implementing roadway configurations accordingly is one of the great solutions. Therefore, the modeling of driving behavior would be the focus of this two-part thesis. Chapter two of this thesis will elaborate on the modeling of various driving behavior types in the microsimulation software by providing an easier-to-calibrate alternative for the driver behavior model in the microsimulation. The calibration method would leverage VISSIM, its highly customizable External driver model (EDM) API, JMP Pro's experiment design and sensitivity analysis, and SSAM's trajectory analysis. Then a set of driver model parameters are produced through sensitivity analysis, which is effective in producing a set of traffic conflicts that matches a preset target. Chapter three of this thesis focuses on simulating aggressive driving behaviors in a microsimulation and driving simulator co-simulation environment. Two co-simulation platforms are demonstrated, and the data collection are done in the VISSIM-Unity platform to collect microscopic driving data and trajectory data from the aggressive driver. Data analysis are performed on both datasets and determine the aggressive driver's safety impact. / Master of Science / The increasingly urbanized world needs a solution to solve one of the most difficult problems – traffic jams and safety. Researchers, engineers, and local officials are all attempting to solve these problems with different methods. However, it is apparent that understanding people's driving behavior on the road and designing the roads and policies to cater to these driving behaviors is one of the great solutions. Therefore, the modeling of driving behavior would be the focus of this two-part thesis. Chapter two of this thesis will experiment with a traffic simulator (which is a tool used for designing and simulating different road configurations like roundabouts and numbers of lanes) and provide an easier and more accurate way to represent various driving styles in the traffic simulator. The calibration method would leverage a driving simulator called VISSIM, an adjustable driver behavior model, a vehicle route tracker, and a vehicle route conflict analysis tool. Then a set of driving behavior parameters would be produced to match the possible traffic accident count in the traffic simulator. Chapter three of this thesis focuses on simulating aggressive driving behaviors in a traffic simulator and driving simulator (like that of those with a steering). Two driving simulator platforms are tested, and the data collection are done in one of the platforms to collect driving data and vehicle route tracker data from the aggressive driver. Data analysis are performed on both types of data and determine the aggressive driver's safety impact.

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