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No description available.

This study investigates the cyclic deformation, fatigue behavior, and failure mechanisms for Ti-6Al-4V ELI (extra low interstitial) with and without mean strain/stress. Mean stress effects on fatigue behavior were studied using four strain ratios. Fatigue data generated was used to assess mean stress fatigue life prediction approaches, including stress-based methods such as Goodman, Gerber, Morrow, Walker and Kwofie; as well as strain-based models, such as Morrow, Smith-Watson-Topper, Walker, Kwofie, Ince-Glinka and a modified version of the Smith-Watson-Topper. The stress-based models did not yield reasonable results and data scatter was observed. The strain-based models offered better results, specifically the Morrow approach which provided more accurate fatigue life predictions. Fractography analysis determined that the influence of material defects on fatigue life had no major differences across all the strain ratios considered. Overall observations indicate that inclusions near the surface had great influence on the fatigue behavior.

The ability of an individual to withstand elevated head-to-toe gravitoinertial (+Gz) forces is determined by the capacity of their body to maintain sufficient head-level arterial pressure. Recent studies have shown a relationship between resting blood-vessel stiffness and an individual’s +Gz-tolerance, although the mechanisms behind this relationship are unclear. The aim of this project is to determine whether or not +Gz-tolerance is affected by a change inresting vasomotor tone. To evaluate this relationship, seven participants were asked to complete a +Gz-tolerance protocol using a human-use centrifugeon two different occasions. On both visits, gradual onset rate (0.1 G.s−1)and rapid onset rate (3.5 G.s−1) tests were done to evaluate the participants+Gz-tolerance. On one of the two visits, prior to the +Gz-tolerance testing,participants performed a 20-min cycle intervention to induce postexercisehypotension, with the aim of temporarily reducing participants’ resting bloodpressure and vasomotor tone. The cycling intervention was successful atinducing postexercise hypotension, as mean arterial pressure was significantlylower on the cycling visit (P<0.05). +Gz-tolerance was significantly lower(P<0.05) on the cycling visit compared with the non-cycling visit for both theGOR and ROR tests (absolute difference of 0.5 G and 0.25 G, respectively).The effect of the type of test on +Gz-tolerance was not influenced by the effectof the cycling intervention (P>0.05). Being the most documented mechanismlinked to postexercise hypotension, sustained vasodilation was assumed tohave occurred. This would have increased distensibility of the affected vessels,explaining the decrease in +Gz-tolerance. The decrease in +Gz-tolerance wassimilar for both tests, indicating that the baroreflex was not affected by thecycling intervention. Assuming that vasodilation occurred, this study showedthat a decrease in resting vasomotor tone decreased +Gz-tolerance, indicatingthe importance of this variable in the relationship between resting blood-vesselstiffness and an individual’s +Gz-tolerance.

Engine control becomes increasingly important in newer cars. It is therefore interesting to investigate if a relatively new control method as Model Predictive Control (MPC) can be useful in engine control in the future. One of the advantages of MPC is that it can handle contraints explicitly. In this thesis basics on turbocharged engines and the underlying theory of MPC is presented. Based on a nonlinear mean value engine model, linearized at multiple operating points, we then implement both a linear and a nonlinearMPC strategy and highlight implementation issues. The implemented MPC controllers calculate optimal wastegate position in order to track a requested torque curve and still make sure that the constraints on turbocharger speed and minimum and maximum opening of the wastegate are fulfilled.

Missing data is a common issue in data analysis. Numerous techniques have been proposed to deal with the missing data problem. Imputation is the most popular strategy for handling the missing data. Imputation for data analysis is the process to replace the missing values with any plausible values. Two most frequent imputation techniques cited in literature are the single imputation and the multiple imputation. The multiple imputation, also known as the golden imputation technique, has been proposed by Rubin in 1987 to address the missing data. However, the inconsistency is the major problem in the multiple imputation technique. The single imputation is less popular in missing data research due to bias and less variability issues. One of the solutions to improve the single imputation technique in the basic regression model: the main motivation is that, the residual is added to improve the bias and variability. The residual is drawn by normal distribution assumption with a mean of 0, and the variance is equal to the residual variance. Although new methods in the single imputation technique, such as stochastic regression model, and hot deck imputation, might be able to improve the variability and bias issues, the single imputation techniques suffer with the uncertainty that may underestimate the R-square or standard error in the analysis results. The research reported in this thesis provides two imputation solutions for the single imputation technique. In the first imputation procedure, the wild bootstrap is proposed to improve the uncertainty for the residual variance in the regression model. In the second solution, the predictive mean matching (PMM) is enhanced, where the regression model is taking the main role to generate the recipient values while the observations in the donors are taken from the observed values. Then the missing values are imputed by randomly drawing one of the observations in the donor pool. The size of the donor pool is significant to determine the quality of the imputed values. The fixed size of donor is used to be employed in many existing research works with PMM imputation technique, but might not be appropriate in certain circumstance such as when the data distribution has high density region. Instead of using the fixed size of donor pool, the proposed method applies the radius-based solution to determine the size of donor pool. Both proposed imputation procedures will be combined with the Gaussian mixture model framework to preserve the original data distribution. The results reported in the thesis from the experiments on benchmark and artificial data sets confirm improvement for further data analysis. The proposed approaches are therefore worthwhile to be considered for further investigation and experiments.

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No description available.

In many hypothesis testing problems such as one-sample and two-sample test problems, the test statistics are degenerate U-statistics. One of the challenges in practice is the computation of U-statistics for a large sample size. Besides, for degenerate U-statistics, the limiting distribution is a mixture of weighted chi-squares, involving the eigenvalues of the kernel of the U-statistics. As a result, it’s not straightforward to construct the rejection region based on this asymptotic distribution. In this research, we aim to reduce the computation complexity of degenerate U-statistics and propose an easy-to-calibrate test statistic by using the divide-and-conquer method. Specifically, we randomly partition the full n data points into kn even disjoint groups, and compute U-statistics on each group and combine them by averaging to get a statistic Tn. We proved that the statistic Tn has the standard normal distribution as the limiting distribution. In this way, the running time is reduced from O(n^m) to O( n^m/km_n), where m is the order of the one sample U-statistics. Besides, for a given significance level , it’s easy to construct the rejection region. We apply our method to the goodness of fit test and two-sample test. The simulation and real data analysis show that the proposed test can achieve high power and fast running time for both one and two-sample tests.

The electromagnetic (EM) bias is the largest source of error in the TOPEX/Poseidon and Jason-1 satellite sea surface height (SSH) estimates. Due to incomplete understanding of the physical processes which cause the bias, current operational models are based on empirical relationships between the bias wind speed and significant wave height. These models reduce RMS estimation errors of the EM bias to approximately 4 cm. To improve EM bias estimation the correlation between the bias and RMS long wave slope is studies using data from tower-based experiments in the Gulf of Mexico and Bass Straight, Australia. Models based on significant wave height and RMS slope are more accurate than models based on wave height and wind speed by at least 50% in RMS error between predicted and ground truth bias values. Nonparametric models have been proposed as a method to reduce the variability of EM bias estimates. Using tower data, nonparametric models developed from wind speed and significant wave height measurements are shown to provide some improvement over parametric models. It is also shown that the historical discrepancy between satellite and tower EM bias measurements is reduced by nonparametric modeling. A validity study of rough surface scattering models is conducted for surfaces with Gaussian and power law power spectra. Models in the study include physical optics (PO), geometrical optics, small perturbation method, and small slope approximation. Due to the prevalence of the PO approximation, particular emphasis is placed on the development of a validity criterion for the PO model. An empirical study of the PO approximation shows that the validity of the model is more accurately described by the RMS wave slope than the classic surface curvature criterion for surfaces with a Gaussian power spectrum. For surfaces with a power law PSD, the accuracy of the PO approximation is related to the significant slope (RMS surface height/wavelength of the dominant spectral peak). The validity of other models in the study are also shown to be well approximated by bounds on surface slope. An EM bias model is derived using the physical optics scattering model, hydrodynamic modulation, and non-Gaussian long wave surface statistics. Using a modulation transfer function, the hydrodynamic modulation of small wave heights is shown to be linearly related to the long wave RMS slope. The resulting EM bias model expresses the relative bias as a function of the long wave surface parameters RMS wave slope, surface skewness, and tilt modulation. Coefficients of the long wave parameters are determined by the short ocean waves, and provide insight into the physical mechanisms that cause the bias. From measured values of the ocean surface profile, estimated values of the bias are computed from the bias model. A comparison of these estimated values with in situ EM bias measurements shows a strong correlation between the estimated and measured values. Nadir and off-nadir measurements of the EM bias collected during the BYU Off-Nadir Experiment (Y-ONE) are presented. The in situ measurements are compared with bias estimates computed from an off-nadir generalization of the nadir EM bias model. From theoretical and experimental bias measurements a model of the angular dependence of the bias is developed as a function of the normalized bias at nadir.