Finite element analysis on Titan STL3 OTR tires

Finite element tire modeling can be a challenging process, due to the overall complexities within the tire and the many variables that are required to produce capable predictive simulations. Utilizing tools from Abaqus finite element software, adequate predictive simulations that represent actual operational conditions can be made possible. Many variables that result from complex geometries and materials, multiple loading conditions, and surface contact can be incorporated into modeling simulations. This thesis outlines modeling practices used to conduct analysis on specific tire variants of the STL3 series OTR tire line, produced by Titan Tire. Finite element models were created to represent an inflated tire and rim assembly, supporting a 30,000 lb load while resting on a flat surface. Simulations were conducted with reinforcement belt cords at variable angles in order to understand how belt cord arrangement affects tire components and stiffness response.

SAMPLING BIAS IN EVALUATING THE PROBABILITY OF SEISMICALLY INDUCED SOIL LIQUEFACTION WITH SPT & CPT CASE HISTORIES

Several deterministic and probabilistic methods are used to evaluate the probability of seismically induced liquefaction of a soil. The probabilistic models usually possess some uncertainty in that model and uncertainties in the parameters used to develop that model. These model uncertainties vary from one statistical model to another. Most of the model uncertainties are epistemic, and can be addressed through appropriate knowledge of the statistical model. One such epistemic model uncertainty in evaluating liquefaction potential using a probabilistic model such as logistic regression is sampling bias. Sampling bias is the difference between the class distribution in the sample used for developing the statistical model and the true population distribution of liquefaction and non-liquefaction instances. Recent studies have shown that sampling bias can significantly affect the predicted probability using a statistical model. To address this epistemic uncertainty, a new approach was developed for evaluating the probability of seismically-induced soil liquefaction, in which a logistic regression model in combination with Hosmer-Lemeshow statistic was used. This approach was used to estimate the population (true) distribution of liquefaction to non-liquefaction instances of standard penetration test (SPT) and cone penetration test (CPT) based most updated case histories. Apart from this, other model uncertainties such as distribution of explanatory variables and significance of explanatory variables were also addressed using KS test and Wald statistic respectively. Moreover, based on estimated population distribution, logistic regression equations were proposed to calculate the probability of liquefaction for both SPT and CPT based case history. Additionally, the proposed probability curves were compared with existing probability curves based on SPT and CPT case histories.

MICROWAVE ABSORPTION PROPERTIES OF TIRES

The waste tire is belonging to insoluble high polymer elastic materials. It takes hundreds of years to resolve the macromolecules of waste tire into the standard which does not pollute the environment. More and more waste tires are air stored which causes space occupation and mosquito-breeding in the places that will spread diseases. The disposal methods include landfill, stockpiles, dumping and incising into particles. However, all these methods are not technically and economically efficient. The trend for the development of waste tire treatment processes is low cost, on-site, and high product recovery at high energy efficiency. In this project, microwave energy has been applied for treatment of the waste tire in laboratory scale. Experimental conditions were varied in order to find the optimum processing parameters such as temperature and atmosphere. The microwave absorption capability of waste tire rubber was investigated by measuring its dielectric properties from room temperature to 800°C in stagnant air and pure nitrogen atmospheres, respectively, at both 915 and 2466MHz.The dielectric parameters data increase steadily at temperatures below 400°C. At temperatures above 400°C, the relative dielectric loss factor and relative dielectric constant begin to decrease. This is due to the solid phase of tire rubber begins to transform to gas phase and the release of volatiles. The calculations of microwave half-power depth and penetration depth of waste tire rubber show that the pyrolysis process significantly improves the microwave absorption capability of the waste tire rubber at low temperatures. The calculated reflection loss of the waste tire rubber suggests that its maximum microwave absorption can be obtained when the rubber has a thickness of 25mm at 915MHz. The sample dimension has a significant effect on the overall performance of microwave absorption in waste tire during pyrolysis and thus on the efficiency of microwave waste tire rubber pyrolysis.

BIAS-CORRECTED MAXIMUM LIKELIHOOD ESTIMATION OF THE PARAMETERS OF THE WEIGHTED LINDLEY DISTRIBUTION

This report discusses the calculation of analytic second-order bias techniques for the maximum likelihood estimates (for short, MLEs) of the unknown parameters of the distribution in quality and reliability analysis. It is well-known that the MLEs are widely used to estimate the unknown parameters of the probability distributions due to their various desirable properties; for example, the MLEs are asymptotically unbiased, consistent, and asymptotically normal. However, many of these properties depend on an extremely large sample sizes. Those properties, such as unbiasedness, may not be valid for small or even moderate sample sizes, which are more practical in real data applications. Therefore, some bias-corrected techniques for the MLEs are desired in practice, especially when the sample size is small. Two commonly used popular techniques to reduce the bias of the MLEs, are ‘preventive’ and ‘corrective’ approaches. They both can reduce the bias of the MLEs to order O(n−2), whereas the ‘preventive’ approach does not have an explicit closed form expression. Consequently, we mainly focus on the ‘corrective’ approach in this report. To illustrate the importance of the bias-correction in practice, we apply the bias-corrected method to two popular lifetime distributions: the inverse Lindley distribution and the weighted Lindley distribution. Numerical studies based on the two distributions show that the considered bias-corrected technique is highly recommended over other commonly used estimators without bias-correction. Therefore, special attention should be paid when we estimate the unknown parameters of the probability distributions under the scenario in which the sample size is small or moderate.