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For a numerical or analytical model to be useful it should be ensured that the model outcome matches the observations or field measurements during calibration. This process has been typically done by manual perturbation of the model input parameters. This research investigates a methodology for using non linear parameter estimation technique (the Marquardt-Levenberg technique) with the multi component reactive solute transport model SEAM3D. The reactive multi-component solutes considered in this study are chlorinated ethenes. Previous studies have shown that this class of compounds can be degraded by four different biodegradation mechanisms, and the degradation path is a function of the prevailing oxidation reduction conditions. Tests were performed in three levels; the first level utilized synthetic model-generated data. The idea was to develop a methodology and perform preliminary testing where "observations" can be generated as needed. The second level of testing involved performing the testing on a single redox zone model. The methodology was refined and tested using data from a chlorinated ethenes-contaminated site. The third level involved performing the tests on a multiple redox zone model. The methodology was tested, and statistical validation of the recommended methodology was performed. The results of the tests showed that there is a statistical advantage for choosing a subgroup of the available parameters to optimize instead of the optimizing the whole available group. Therefore, it is recommended to perform a parameter sensitivity study prior to the optimization process to identify the suitable parameters to be chosen. The methodology suggests optimizing the oxidation-reduction species parameters first then calibrating the chlorinated ethenes model. The results of the tests also proved the advantage of the sequential optimization of the model parameters, therefore the parameters of the parent compound are optimized, updated in the daughter compound model, for which the parameters are then optimized so on. The test results suggested considering the concentrations of the daughter compounds when optimizing the parameters of the parent compounds. As for the observation weights, the tests suggest starting the applied observation weights during the optimization process at values of one and changing them if needed. Overall the proposed methodology proved to be very efficient. The optimization methodology yielded sets of model parameters capable of generating concentration profiles with great resemblance to the observed concentration profiles in the two chlorinated ethenes site models considered. / Ph. D.

This study explores the potential of vehicle trajectory data obtained from Wide Area Motion Imagery for modeling and analyzing traffic characteristics. The data in question is collected by PV Labs and also known as persistent wide-area video. This video, in combination with PVLab's integrated Tactical Content Management System's spatiotemporal capability, automatically identifies and captures every vehicle in the video view frame, storing each vehicle with a discrete ID, track ID, and time-stamped location. This unique data capture provides comprehensive vehicle trajectory information. This thesis explores the use of data collected by the PVLab's system for an approximate area of 4 square kilometers area in the CBD area of Hamilton, Canada for use in understanding traffic characteristics. The data was collected for two three-hour continuous periods, one in the morning and one in the evening of the same day. Like any other computer vision algorithm, this data suffers from false detection, no detection, and other inaccuracies caused by faulty image registration. Data filtering requirements to remove noisy trajectories and reduce error is developed and presented. A methodology for extracting microscopic traffic data (gap, relative velocity, acceleration, speed) from the vehicle trajectories is presented in details. This study includes the development of a data model for storing this type of large-scale spatiotemporal data. The proposed data model is a combination of two efficient trajectory data storing techniques, the 3-D schema and the network schema and was developed to store trajectory information along with associated microscopic traffic information. The data model is designed to run fast queries on trajectory information. A 15-minute sample of tracks was validated using manual extraction from imagery frames from the video. Microscopic traffic data is extracted from this trajectory data using customized GIS analysis. Resulting tracks were map-matched to roads and individual lanes to support macro and microscopic traffic characteristic extraction. The final processed dataset includes vehicles and their trajectories for an area of approximately 4-square miles that includes a dense and complex urban network of roads over two continuous three-hour periods. Two subsets of the data were extracted, cleaned, and processed for use in calibrating car-following sub-models used in microscopic simulations. The car-following model is one of the cornerstones of any simulation based traffic analysis. Calibrating and validating these models is essential for enhancing the ability of the model's capability of representing local traffic. Calibration efforts have previously been limited by the availability and accuracy of microscopic traffic data. Even datasets like the NGSIM data are restricted in either time or space. Trajectory data of all vehicles over a wide area during an extended period of time can provide new insight into microscopic models. Persistent wide-area imagery provides a source for this data. This study explores data smoothing required to handle measurement error and to prepare model input for calibration. Three car-following models : the GHR model, the linear Helly model, and the Intelligent Driver model are calibrated using this new data source. Two approaches were taken for calibrating model parameters. First, a least square method is used to estimate the best fit value for the model parameter that minimizes the global error between the observed and predicted values. The calibration results outline the limitation of both the WAMI data source and the models themselves. Existing model structures impose limitations on the parameter values. Models become unstable beyond these parameter values and these values may not be near global optima. Most of the car-following models were developed based upon some kinematic relation between driver reaction and expected stimuli of that response. For this reason, models in their current form are ill-suited for calibration with noisy microscopic data. On the other hand, the limitation of the WAMI data is the inability of obtaining an estimate of the measurement errors. With unknown measurement errors, any model development or calibration becomes questionable irrespective of the data smoothing or filtering technique undertaken. These findings indicate requirements for development of a new generation of car-following model that can accommodate noisy trajectory data for calibration of its parameters. / MS / The decision making process undertaken by transportation agencies for planning, evaluating, and operating transportation facilities relies on analyzing traffic and driver behavior in both aggregated and disaggregated manner. Different computational tools relying on representative models of aggregate traffic flow measures and/or driver behavior are used in the decision support system tools. Field data is used not only as an input for the computational tools but also to develop, calibrate, and validate the models representing a particular aspect of traffic and driver behavior. Different approaches have been undertaken to collect the data required for analyzing traffic and driver behavior. One of the applied approach is to collect trajectory (i.e. position, speed, acceleration) information of vehicles in the analysis zone. However, this data collection approach is often limited to relatively small stretch of a roadway for short duration due to high cost of collection and limitation of technology. As a result, the models developed and calibrated using these data often lack generalization power for different situation. This study explores the potential of a new data source to address the aforementioned limitations. The data used in this study collects the trajectory information for the whole population of vehicles in the study area by collecting wide-area (WAMI) video data. The data is collected by Canada based imaging solution company PV Labs. The collection area is relatively large to cover wide range of roadway types and traffic operation system. A framework has been developed to extract traffic flow measures from the trajectory data. The extracted traffic flow measures are then applied to calibrate the car-following model. The car-following model attempts to mimic the longitudinal movement of real-world drivers following another vehicle in front of them. The calibration results outline the limitations of the WAMI data. Although, this dataset is capable of capturing traffic measures for different driving condition, the lack of information about measurement error imposes limits on the direct application of the data for model calibration. Findings of this study can be applied for refinement of the video data capture technology and subsequent application in modelling traffic characteristics as well as development of new and calibration of existing driver behavior model.

Most modern product development utilizes computational models. With increasing demands on reducing the product development lead-time, it becomes more important to improve the accuracy and efficiency of simulations. In addition, to improve product performance, a lot of products are designed to be lighter and more flexible, thus more prone to nonlinear behaviour. Linear finite element (FE) models, which still form the basis of numerical models used to represent mechanical structures, may not be able to predict structural behaviour with necessary accuracy when nonlinear effects are significant. Nonlinearities are often localized to joints or boundary conditions. Including nonlinear behaviour in FE-models introduces more sources of uncertainty and it is often necessary to calibrate the models with the use of experimental data. This research work presents a model calibration method that is suitable for mechanical systems with structural nonlinearities. The methodology concerns pre-test planning, parameterization, simulation methods, vibrational testing and optimization. The selection of parameters for the calibration requires physical insights together with analyses of the structure; the latter can be achieved by use of simulations. Traditional simulation methods may be computationally expensive when dealing with nonlinear systems; therefore an efficient fixed-step state-space based simulation method was developed. To gain knowledge of the accuracy of different simulation methods, the bias errors for the proposed method as well as other widespread simulation methods were studied and compared. The proposed method performs well in comparison to other simulation methods. To obtain precise estimates of the parameters, the test data should be informative of the parameters chosen and the parameters should be identifiable. Test data informativeness and parameter identifiability are coupled and they can be assessed by the Fisher information matrix (FIM). To optimize the informativeness of test data, a FIM based pre-test planning method was developed and a multi-sinusoidal excitation was designed. The steady-state responses at the side harmonics were shown to contain valuable information for model calibration of FE-models representing mechanical systems with structural nonlinearities. In this work, model calibration was made by minimizing the difference between predicted and measured multi-harmonic frequency response functions using an efficient optimization routine. The steady-state responses were calculated using the extended multi-harmonic balance method. When the parameters were calibrated, a k-fold cross validation was used to obtain parameter uncertainty. The proposed model calibration method was validated using two test-rigs, one with a geometrical nonlinearity and one with a clearance type of nonlinearity. To attain high quality data efficiently, the amplitude of the forcing harmonics was controlled at each frequency step by an off-line force feedback algorithm. The applied force was then measured and used in the numerical simulations of the responses. It was shown in the validation results that the predictions from the calibrated models agree well with the experimental results. In summary, the presented methodology concerns both theoretical and experimental aspects as it includes methods for pre-test planning, simulations, testing, calibration and validation. As such, this research work offers a complete framework and contributes to more effective and efficient analyses on mechanical systems with structural nonlinearities.

Condições pedológicas, pluviosidade, alterações no uso e ocupação do solo, práticas de manejo de culturas e de preservação interferem diretamente na geração de sedimentos e na taxa de erosão. Em uma bacia hidrográfica esta geração de sedimentos excessiva pode resultar em problemas como o assoreamento dos corpos de água e de reservatórios. Nos reservatórios esta deposição de sedimentos pode representar perda de volume de reservação, promovida pelos sedimentos depositados no fundo da barragem. Com o tempo esta perda poderá vir a representar comprometimento da disponibilidade hídrica para irrigação, para o consumo humano, e para geração de energia. As obras de dragagem destes sedimentos são muito caras e resolvem somente os sintomas do problema e não suas causas. Desta forma as quantificações desse assoreamento e de seus deflagradores se mostram necessárias para o planejamento e gestão de recursos hídricos. A presente pesquisa se propõe a avaliar a aplicabilidade do modelo SWAT (Soil Water Assessment Tool) como ferramenta de estimativa de geração de sedimentos para reservatórios avaliando seus resultados, suas potencialidades e fragilidades para as condições locais e sugerir futuras pesquisas e/ou modificações no modelo. O modelo que foi desenvolvido originalmente pelo USDA (United States Departmente of Agriculture) para avaliar erosão e balanço hídrico de bacias hidrográficas, conta com um vasto número de aplicações no mundo, documentação e usuários. A área a ser estuda é bacia do reservatório do Lobo, que possuí uma área aproximada de 227 km² e localizado no município de Itirapina e de Brotas. Atualmente o reservatório é utilizado tanto para geração energética e como atração turística local. Dois cenários de simulação foram selecionados: Cenário 1 de 1977 até 1985 e Cenário 2 de 1996 até 2006. Para realizar as simulações foram utilizados a versão ArcSWAT 2005 version2.34 para realizar as simulações e o SWAT-CUP 4.3.1 para calibração.Os dados de entrada do modelo são: dados de chuvas e vazão diários obtidos da ANA (Agência Nacional das Águas), cartas de levantametno pedológico e cartas de uso e ocupação obtidas a partir de imagens CBERS e LANDSAT. Após calibração utilizando SUFI2 obteve-se o aporte de sedimentos na barragem para os dois cenários. Os resultados do primeiro cenário apresentaram influência negativa de dados inconsistentes de dados de vazão utilizados e limitações observadas na discretização do modelo nas representações do mosaico de uso e ocupação muito fragmentado para este cenário. O segundo cenário, no entanto obteve resultados satisfatórios comprovando a capacidade do modelo como ferramenta de avaliação de geração e aporte de sedimento em reservatórios. / The sediment yield and erosion rates are directly affected by pedologic conditions, precipitation, land and use changes, management and soil preservation practices. Excessive sediment yield in a watershed could result in difficulties caused by siltation processes in rivers and reservoirs. Sediment deposition at the bottom of reservoirs and dams may result in volume loss. In time such loss could become impairment of water availability for irrigation, human consumption and power generation. Sediment dredging is an expensive solution that will address only the symptoms and not the causes of the issue. Therefore assessing the amount of sediment and their causes is required for Water Resources Management and Planning. The present research aims to evaluate the applicability of the SWAT (Soil and Water Assessment Tool) as sediment generation and contribution estimation tool for reservoirs evaluating the results, assessing the model strengths and weakness for the local contitions and make sugestions for future research and/or model modifications. Originally developed by the USDA (United States Department of agriculture) to assess erosion and water balance of watersheds, this model has a large number of users, available documentation and registered applications across the world. The study area, with an approximate area of 227 km² is the Lobo reservoir watershed, located at the municipalities of Itirapina and Brotas. The reservoir is currently used for hydropower generation and as a tourist attraction. Two simulation scenarios were chosen: Scenario 1from 1977 to 1985 and Scenario 2 from 1996 until 2006. To perform the simulations the ArcSWAT 2005 version 2.34 was selected and the SWAT-CUP 4.3.1 for the calibration. The data input was: daily precipitation and discharge flow datasets from ANA (Agência Nacional das Águas), pedological survey chats and land use charts obtained from CBERS and LANDSAT imagery. After calibration using SUFI2, sediment yield and contribution at the dam was obtained for both scenarios. The first scenario results showed negative effects caused by inconsistent input data flow and limitations regarding model discretization on the model representation of highly fragmented land use. The second scenario, however achieved satisfactory results demonstrating the model ability as sediment yield and contribution in reservoir evaluation tool.

Models which describe the performance of physical process are essential for quality prediction, experimental planning, process control and optimization. Engineering models developed based on the underlying physics/mechanics of the process such as analytic models or finite element models are widely used to capture the deterministic trend of the process. However, there usually exists stochastic randomness in the system which may introduce the discrepancy between physics-based model predictions and observations in reality. Alternatively, statistical models can be used to develop models to obtain predictions purely based on the data generated from the process. However, such models tend to perform poorly when predictions are made away from the observed data points. This dissertation contributes to model enhancement research by integrating physics-based model and statistical model to mitigate the individual drawbacks and provide models with better accuracy by combining the strengths of both models. The proposed model enhancement methodologies including the following two streams: (1) data-driven enhancement approach and (2) engineering-driven enhancement approach. Through these efforts, more adequate models are obtained, which leads to better performance in system forecasting, process monitoring and decision optimization. Among different data-driven enhancement approaches, Gaussian Process (GP) model provides a powerful methodology for calibrating a physical model in the presence of model uncertainties. However, if the data contain systematic experimental errors, the GP model can lead to an unnecessarily complex adjustment of the physical model. In Chapter 2, we proposed a novel enhancement procedure, named as "Minimal Adjustment", which brings the physical model closer to the data by making minimal changes to it. This is achieved by approximating the GP model by a linear regression model and then applying a simultaneous variable selection of the model and experimental bias terms. Two real examples and simulations are presented to demonstrate the advantages of the proposed approach. Different from enhancing the model based on data-driven perspective, an alternative approach is to focus on adjusting the model by incorporating the additional domain or engineering knowledge when available. This often leads to models that are very simple and easy to interpret. The concepts of engineering-driven enhancement are carried out through two applications to demonstrate the proposed methodologies. In the first application where polymer composite quality is focused, nanoparticle dispersion has been identified as a crucial factor affecting the mechanical properties. Transmission Electron Microscopy (TEM) images are commonly used to represent nanoparticle dispersion without further quantifications on its characteristics. In Chapter 3, we developed the engineering-driven nonhomogeneous Poisson random field modeling strategy to characterize nanoparticle dispersion status of nanocomposite polymer, which quantitatively represents the nanomaterial quality presented through image data. The model parameters are estimated through the Bayesian MCMC technique to overcome the challenge of limited amount of accessible data due to the time consuming sampling schemes. The second application is to calibrate the engineering-driven force models of laser-assisted micro milling (LAMM) process statistically, which facilitates a systematic understanding and optimization of targeted processes. In Chapter 4, the force prediction interval has been derived by incorporating the variability in the runout parameters as well as the variability in the measured cutting forces. The experimental results indicate that the model predicts the cutting force profile with good accuracy using a 95% confidence interval. To conclude, this dissertation is the research drawing attention to model enhancement, which has considerable impacts on modeling, design, and optimization of various processes and systems. The fundamental methodologies of model enhancement are developed and further applied to various applications. These research activities developed engineering compliant models for adequate system predictions based on observational data with complex variable relationships and uncertainty, which facilitate process planning, monitoring, and real-time control.

Water distribution network models are used for different purposes. In this study, a model, used for daily operational issues is concerned. Models results should be consistent with actual conditions for sound decisions during operational studies. Adjusting model parameters according to site measurements in order to fit the model to obtain realistic results is known as calibration. Researchers have carried out numerous studies on calibration and developed various methods. In this study, an actual network (N8.3 Pressure Zone, Ankara) has been calibrated by two classical methods developed by Walski (1983) and Bhave (1988). The network parameter calibrated in this study is Hazen-Williams roughness coefficient, C-factor, and other parameters have been lumped in the C-factor. Results of the analysis showed that, C-factors have been found in a wide range.

Reproducing moisture retention behavior of the upper and lower vadose zone in shallow water table settings provides unique challenges for integrated (combined surface and groundwater) hydrological models. Field studies indicate that moisture retention in shallow water table settings is highly variably affected by antecedent state and air entrapment. The theory and vertical behavior of a recently developed integrated surface and groundwater model (IHM) is examined through comparisons to collected field data in West-Central Florida. The objectives of this study were to (1) Identify important considerations and behavior of the vadose zone for reproducing runoff, ET and recharge in shallow water table settings; (2) Develop a conceptual model that describes vertical soil moisture behavior while allowing for field scale variability; (3) Test the model against observations of the vertical processes; (4) Investigate the sensitivity of model parameters on model vs. observed vertical behavior, and (5) offer recommendations for improvements and parameterization for regional model application. Rigorous testing was made to better understand the robustness and/or limitations of the methodology of the IHM for upper and lower vadose zone. The results are also generally applicable and useful to the upper zone and lower zone conceptualization and parameterization of stand alone HSPF and perhaps other surface water models. Simulation results indicate IHM is capable of providing reasonable predictions of infiltration, depth to water table response, ET distributions from the upper soil, lower soil and water table, and recharge while incorporating field scale variability of soil and land cover properties.

Groundwater flow and transport in deep crystalline rock is an important area of research. This is partly due to its relevance for constructing a long term repository for storing radioactive spent nuclear fuel in deep bedrock. Understanding the behavior of flow and transport processes in deep crystalline rock is crucial in developing a sustainable solution to this problem. This study aims to increase the understanding of how channel network models (CNM) can be applied to represent groundwater flow and solute transport in sparsely fractured crystalline rock under site specific conditions. A main objective was to determine how to incorporate structural and hydrogeological site characterization data in the construction of the CNMs. In addition to this, the associated key parameters of the CNMs were investigated to gain further understanding of model site application. To that end, a scripting approach with the python scripting library Pychan3d was used to create alternative channel network representations of a field site. A conceptual discrete fracture network (DFN) model was constructed using field site data obtained from a structural model of the fractures present at the site of the Tracer Retention Understanding Experiments (TRUE) - Block Scale at the Äspö Hard Rock Laboratory (HRL). This conceptual model was used as a base for constructing two different alternatives, denoted respectively as sparse and dense, of a CNM. The sparse CNM consisted of a limited amount of channels for each fracture, while the dense CNM acted as a DFN proxy, taking the full extent of the fracture areas into account and creating a dense, large network of flow channels for each fracture. In order to verify the performance of the generated CNMs, a reproduction of tracer tests performed at the same specific field site was attempted using a particle tracking technique. In addition to this, long term predictions of solute transport without the interference of the pumps used during the tracer tests were done in order to estimate transport time distributions. Pychan3d and the scripting approach was successfully used to create CNMs respecting specific conditions from the TRUE-Block Scale site. The sparse CNM was found to give very adequate flow and transport responses in most cases and to be relatively easier to calibrate than its dense counterpart. The long term transport predictions at the site according to the models seem to follow a channelized pattern, with only a few select paths for transport. The difficulties encountered in matching the dense CNM with the tracer tests most likely stem from difficulties in flow calibration, as well as certain key parameters being assigned too generically.

Condições pedológicas, pluviosidade, alterações no uso e ocupação do solo, práticas de manejo de culturas e de preservação interferem diretamente na geração de sedimentos e na taxa de erosão. Em uma bacia hidrográfica esta geração de sedimentos excessiva pode resultar em problemas como o assoreamento dos corpos de água e de reservatórios. Nos reservatórios esta deposição de sedimentos pode representar perda de volume de reservação, promovida pelos sedimentos depositados no fundo da barragem. Com o tempo esta perda poderá vir a representar comprometimento da disponibilidade hídrica para irrigação, para o consumo humano, e para geração de energia. As obras de dragagem destes sedimentos são muito caras e resolvem somente os sintomas do problema e não suas causas. Desta forma as quantificações desse assoreamento e de seus deflagradores se mostram necessárias para o planejamento e gestão de recursos hídricos. A presente pesquisa se propõe a avaliar a aplicabilidade do modelo SWAT (Soil Water Assessment Tool) como ferramenta de estimativa de geração de sedimentos para reservatórios avaliando seus resultados, suas potencialidades e fragilidades para as condições locais e sugerir futuras pesquisas e/ou modificações no modelo. O modelo que foi desenvolvido originalmente pelo USDA (United States Departmente of Agriculture) para avaliar erosão e balanço hídrico de bacias hidrográficas, conta com um vasto número de aplicações no mundo, documentação e usuários. A área a ser estuda é bacia do reservatório do Lobo, que possuí uma área aproximada de 227 km² e localizado no município de Itirapina e de Brotas. Atualmente o reservatório é utilizado tanto para geração energética e como atração turística local. Dois cenários de simulação foram selecionados: Cenário 1 de 1977 até 1985 e Cenário 2 de 1996 até 2006. Para realizar as simulações foram utilizados a versão ArcSWAT 2005 version2.34 para realizar as simulações e o SWAT-CUP 4.3.1 para calibração.Os dados de entrada do modelo são: dados de chuvas e vazão diários obtidos da ANA (Agência Nacional das Águas), cartas de levantametno pedológico e cartas de uso e ocupação obtidas a partir de imagens CBERS e LANDSAT. Após calibração utilizando SUFI2 obteve-se o aporte de sedimentos na barragem para os dois cenários. Os resultados do primeiro cenário apresentaram influência negativa de dados inconsistentes de dados de vazão utilizados e limitações observadas na discretização do modelo nas representações do mosaico de uso e ocupação muito fragmentado para este cenário. O segundo cenário, no entanto obteve resultados satisfatórios comprovando a capacidade do modelo como ferramenta de avaliação de geração e aporte de sedimento em reservatórios. / The sediment yield and erosion rates are directly affected by pedologic conditions, precipitation, land and use changes, management and soil preservation practices. Excessive sediment yield in a watershed could result in difficulties caused by siltation processes in rivers and reservoirs. Sediment deposition at the bottom of reservoirs and dams may result in volume loss. In time such loss could become impairment of water availability for irrigation, human consumption and power generation. Sediment dredging is an expensive solution that will address only the symptoms and not the causes of the issue. Therefore assessing the amount of sediment and their causes is required for Water Resources Management and Planning. The present research aims to evaluate the applicability of the SWAT (Soil and Water Assessment Tool) as sediment generation and contribution estimation tool for reservoirs evaluating the results, assessing the model strengths and weakness for the local contitions and make sugestions for future research and/or model modifications. Originally developed by the USDA (United States Department of agriculture) to assess erosion and water balance of watersheds, this model has a large number of users, available documentation and registered applications across the world. The study area, with an approximate area of 227 km² is the Lobo reservoir watershed, located at the municipalities of Itirapina and Brotas. The reservoir is currently used for hydropower generation and as a tourist attraction. Two simulation scenarios were chosen: Scenario 1from 1977 to 1985 and Scenario 2 from 1996 until 2006. To perform the simulations the ArcSWAT 2005 version 2.34 was selected and the SWAT-CUP 4.3.1 for the calibration. The data input was: daily precipitation and discharge flow datasets from ANA (Agência Nacional das Águas), pedological survey chats and land use charts obtained from CBERS and LANDSAT imagery. After calibration using SUFI2, sediment yield and contribution at the dam was obtained for both scenarios. The first scenario results showed negative effects caused by inconsistent input data flow and limitations regarding model discretization on the model representation of highly fragmented land use. The second scenario, however achieved satisfactory results demonstrating the model ability as sediment yield and contribution in reservoir evaluation tool.