Full-motion driving simulators require efficient real-time high fidelity vehicle models in order to provide a more realistic vehicle response. Typically, multi-body models are used to represent the vehicle dynamics, but these have the unfortunate drawback of requiring the solution of a set of coupled differential algebraic equations (DAE). DAE's are not conducive to real-time implementation such as in a driving simulator, without a very expensive processing capability. The primary objective of this thesis is to show that multi-body models constructed from DAE's can be reasonably approximated with linear models using suspension elements that have nonlinear constitutive relationships. Three models were compared in this research, an experimental quarter-car test rig, a multi-body dynamics differential algebraic equation model, and a linear model with nonlinear suspension elements. Models constructed from differential algebraic equations are computationally expensive to compute and are difficult to realize for real-time simulations. Instead, a linear model with nonlinear elements was proposed for a more computationally efficient solution that would retain the nonlinearities of the suspension. Simplifications were made to the linear model with nonlinear elements to further reduce computation time for real-time simulation. The development process of each model is fully described in this thesis. Each model was excited with the same input and their outputs were compared. It was found that the linear model with nonlinear elements provides a reasonably good approximation of actual model with the differential algebraic equations. / Master of Science

High-quality short-term forecasts of wind speed are vital to making wind power a more reliable energy source. Gneiting et al. (2006) have introduced a model for the average wind speed two hours ahead based on both spatial and temporal information. The forecasts produced by this model are accurate, and subject to accuracy, the predictive distribution is sharp, i.e., highly concentrated around its center. However, this model is split into nonunique regimes based on the wind direction at an off-site location. This work both generalizes and improves upon this model by treating wind direction as a circular variable and including it in the model. It is robust in many experiments, such as predicting at new locations. This is compared with the more common approach of modeling wind speeds and directions in the Cartesian space and use a skew-t distribution for the errors. The quality of the predictions from all of these models can be more realistically assessed with a loss measure that depends upon the power curve relating wind speed to power output. This proposed loss measure yields more insight into the true value of each model's predictions. One method of evaluating time series forecasts, such as wind speed forecasts, is to test the null hypothesis of no difference in the accuracy of two competing sets of forecasts. Diebold and Mariano (1995) proposed a test in this setting that has been extended and widely applied. It allows the researcher to specify a wide variety of loss functions, and the forecast errors can be non-Gaussian, nonzero mean, serially correlated, and contemporaneously correlated. In this work, a similar unconditional test of forecast accuracy for spatial data is proposed. The forecast errors are no longer potentially serially correlated but spatially correlated. Simulations will illustrate the properties of this test, and an example with daily average wind speeds measured at over 100 locations in Oklahoma will demonstrate its use. This test is compared with a wavelet-based method introduced by Shen et al. (2002) in which the presence of a spatial signal at each location in the dataset is tested.

No description available.

Stochastic system identification is of great interest in the areas of control and communication. In stochastic system identification, a model of a dynamic system is determined based on given inputs and received outputs from the system, where stochastic uncertainties are also involved. The scope of the report is to consider continuous-time models used within control and communication and to estimate the model parameters from sampled data with high accuracy in a computational efficient way. Continuous-time models of systems controlled in a networked environment, stochastic closed-loop systems, and wireless channels are considered. The parameters of a transfer function based model for the process in a networked control system are first estimated by a covariance function based approach, relying upon the second order statistical properties of the output signal. Some other approaches for estimating the parameters of continuous-time models for processes in networked environments are also considered. Further, the parameters of continuous-time autoregressive exogenous models are estimated from closed-loop filtered data, where the controllers in the closed-loop are of proportional and proportional integral type, and where the closed-loop also contains a time-delay. Moreover, a stochastic differential equation is derived for Jakes's wireless channel model, describing the dynamics of a scattered electric field with the moving receiver incorporating a Doppler shift. / <p>Article I was still in manuscript form at the time of the defense.</p>

This report presents the descriptive data analysis and failure time modeling that can be used to find out the characteristics and pattern of failure time. Descriptive data analysis includes the mean, median, 1st quartile, 3rd quartile, frequency, standard deviation, skewness, kurtosis, minimum, maximum and range. Models like exponential distribution, gamma distribution, normal distribution, lognormal distribution, Weibull distribution and log-logistic distribution have been studied for failure time data. The data in this report comes from the South Texas Project that was collected during the last 40 years. We generated more than 1000 groups for STP failure time data based on Mfg Part Number. In all, the top twelve groups of failure time data have been selected as the study group. For each group, we were able to perform different models and obtain the parameters. The significant level and p-value were gained by Kolmogorov-Smirnov test, which is a method of goodness of fit test that represents how well the distribution fits the data. The In this report, Weibull distribution has been proved as the most appropriate model for STP dataset. Among twelve groups, eight groups come from Weibull distribution. In general, Weibull distribution is powerful in failure time modeling. / text

Bluetooth technology applications have improved travel time data collection efforts and allowed for collection of large data sets at a low cost per data unit. Mean travel times between pairs of points are available, but the primary value of this technique is the availability of the entire distribution of travel times throughout multiple days and time periods, allowing for a greater understanding of travel time variations and reliability. The use of these data for transportation planning, engineering and operations continues to expand. Previous applications of similar data sources have included travel demand and simulation model validation, work zone traffic patterns, transit ridership and reliability, pedestrian movement patterns, and before-after studies of transportation improvements. This thesis investigates the collection and analysis of Bluetooth-enabled travel time data along a multimodal arterial corridor in San Luis Obispo, California. Five BlueMAC devices collected multimodal travel time data in January and February 2016 along Los Osos Valley Road. These datasets were used to identify and process known sources of error such as occasions where vehicles using the roadway turn off and make an intermediate stop and multiple reads from the same vehicle; quantify travel time performance and reliability along arterial streets; and compare transit, bicycle, and pedestrian facility performance. Additionally, a travel time model was estimated based on segment characteristics and Bluetooth data to estimate average speeds and travel time distributions.

No description available.

As concerns about climate change increase, many people are calling for reductions in the use of fossil fuels and encouraging a shift to more sustainable and less polluting transportation modes. Cities and urban areas are more concerned because their population currently comprises over half of the world's population. Sustainable transportation modes such as cycling, walking, and use of public transit and electric vehicles can benefit the environment in many ways, including a reduction in toxic greenhouse gas (GHG) emissions and noise levels. In order to enhance the trend of using sustainable modes of transportation, tools, measures, and planning techniques similar to those used for vehicular transportation need to be developed. In this dissertation, we consider four problems in the context of different sustainable modes of transportation, namely, cycling, rail, public transit, and ridesharing. We develop different models to predict bike travel times for use in bike share systems (BSSs) using random forest (RF), least square boosting (LSBoost), and artificial neural network (ANN) techniques. We also use cycling Global Positioning System (GPS) data collected from 10 people (3 females and 7 males) to study cyclists' acceleration/deceleration behavior. Moreover, we develop a continuous rail transit simulator (RailSIM) intended for multi-modal energy-efficient routing applications. Finally, we propose a dynamic trip planning system that integrates ridesharing and public transit. The work done in this dissertation can help encouraging more people to move to more sustainable modes of transportation. / Doctor of Philosophy / As concerns about climate change increase, many people are calling for reductions in the use of fossil fuels and encouraging a shift to more sustainable and less polluting transportation modes. Cities and urban areas are more concerned because their population currently comprises over half of the world's population. Sustainable transportation modes such as cycling, walking, and use of public transit and electric vehicles can benefit the environment in many ways, including a reduction of toxic greenhouse gas (GHG) emissions and noise levels. In order to enhance the trend of using sustainable modes of transportation, tools, measures, and planning techniques similar to those used for vehicular transportation need to be developed. In this dissertation, we consider four problems in the context of different sustainable modes of transportation, namely, cycling, rail, public transit, and ridesharing. We develop different models to predict bike travel times in bike share systems (BSSs) using machine learning techniques. We also use cycling Global Positioning System (GPS) data collected from 10 people (3 females and 7 males) to study cyclists' acceleration/deceleration behavior. Moreover, we develop a continuous rail transit simulator (RailSIM) intended for multi-modal energy-efficient routing applications. Finally, we propose a dynamic trip planning system that integrates ridesharing and public transit. The work done in this dissertation can help encouraging more people to move to more sustainable modes of transportation.

Many important factors affect the spread of childhood infectious disease. To understand better the fundamental drivers of infectious disease spread, several researchers have estimated seasonal transmission coefficients using discrete-time models. This research addresses several shortcomings of the discrete-time approaches, including removing the need for the reporting interval to match the serial interval of the disease using infectious disease data from three major cities: New York City, London, and Bangkok. Using a simultaneous approach for optimization of differential equation systems with a Radau collocation discretization scheme and total variation regularization for the transmission parameter profile, this research demonstrates that seasonal transmission parameters can be effectively estimated using continuous-time models. This research further correlates school holiday schedules with the transmission parameter for New York City and London where previous work has already been done, and demonstrates similar results for a relatively unstudied city in childhood infectious disease research, Bangkok, Thailand.

Cette étude a pour objectif de modéliser la durée de vie d'un revêtement NiAlPt déposé sur un substrat monocristallin base Ni (AM1). De façon standard, la durée de vie des revêtements est évaluée en mesurant l'évolution de la masse d'un échantillon revêtu au cours d'un essai de cyclage thermique. Des modèles de durée de vie fondés sur ces mesures sont très bien adaptés aux revêtements pour lesquels la couche d'oxyde est peu adhérente, ce qui n'est pas le cas pour le revêtement NiAlPt objet de cette étude. D'autre part, il est impossible d'obtenir des courbes de gain de masse pour des conditions de chargement thermo-mécaniques complexes, tels que ceux supportés par une aube aéronautique en service. C'est pourquoi nous avons choisi d'étudier l'évolution de la microstructure du revêtement au cours du vieillissement et d'établir des liens entre cette évolution microstructurale et la durée de vie évaluée sur des essais de cyclage thermiques.La base d'essai réalisée regroupe des essais d'oxydation cycliques à différentes températures et fréquences de cyclage ainsi que des essais de fatigue mécano-thermique, en phase, hors-phase et complexes. Pour chaque condition testée, des essais interrompus ont permis d'estimer les évolutions microstructurales en fonction des conditions de chargement thermo-mécanique jusqu'à des temps relativement longs. Nous avons en particulier estimé les évolutions de rugosité de surface, d'épaisseur de la couche d'interdiffusion entre le revêtement et le superalliage, et la fraction de phase γ' transformée. Ces deux paramètres sont corrélés respectivement au maximum de la courbe de gain de masse et au nombre de cycle nécessaire pour atteindre un gain de masse nul, pour le système étudié en oxydation cyclique.Nous avons modélisé l'évolution de l'épaisseur de la couche d'interdiffusion en fonction des conditions de chargement thermo-mécaniques. La transformation de phase a été modélisée à l'aide d'un bilan de flux de matière dans le revêtement fonction de l'intégrité de la couche d'oxyde de croissance et de la rugosité de surface. Ces deux modèles ont permis d'estimer l'évolution microstructurale du revêtement en fonction des différents paramètres de chargement thermo-mécanique, et donc d'estimer une durée de vie basée sur ces critères. / This study aims at modeling the life time of a NiAlPt coating used at high temperature and deposited on a single crystal nickel-based superalloy AM1. Usually, the life time of coatings is estimated through the evaluation of the mass of coated samples (Net Mass Gain curves), during thermal cycling tests. Existing models are very suitable to describe the evolution of coating in the case of poorly adherent oxide layer. Unfortunately, this is not the case of the studied NiAlPt coating. Moreover, it is impossible to obtain experimentally the Net Mass gain curve of a sample subjected to thermo-mechanical loading, experienced by engine components. Thus, the strategy adopted in this study is to correlate the microstructure of the coating with its life time, evaluated during thermal cycling.The experimental work included cyclic oxidation tests at different temperatures, cycling frequencies and in-phase, out-of-phase and complex thermo-mechanical loadings. For each of the tested condition, interrupted tests allowed the characterization of the microstructure evolution as a function of the loading parameters up to relatively long ageing time. Especially, the roughness of the coating surface has been investigated as well as the interdiffusion zone (IDZ) thickness evolution or the fraction of γ' phase transformed within the coating. These two last parameters where found to be correlated respectively to the time needed to reach the maximum of the Net Mass Gain curve and the time needed to reach the zero mass gain of the curve, under thermal cycling conditions.A model for the interdiffusion (IDZ) thickness evolution was proposed as a function of the loading parameters. The phase transformation in the coating was modeled using a mass balance condition for the aluminum in the coating, including roughness of the surface and oxide scale integrity considerations. This model based on original criteria allowed the estimation of the life time of a coated turbine blade under service conditions.