D.Ing. (Mechanical Engineering) / The most common data sought during a wind tunnel test program are the forces and moments acting on an airframe, (or any other test article). The most common source of this data is the internal strain gauge balance. Balances are six degree of freedom force transducers that are required to be of small size and of high strength and stiffness. They are required to deliver the highest possible levels of accuracy and reliability. There is a focus in both the USA and in Europe to improve the performance of balances through collaborative research. This effort is aimed at materials, design, sensors, electronics calibration systems and calibration analysis methods. Recent developments in the use of statistical methods, including modern design of experiments, have resulted in improved balance calibration models. Research focus on the calibration of six component balances has moved to the determination of the uncertainty of measurements obtained in the wind tunnel. The application of conventional statistically-based approaches to the determination of the uncertainty of a balance measurement is proving problematical, and to some extent an impasse has been reached. The impasse is caused by the rapid expansion of the problem size when standard uncertainty determination approaches are used in a six-degree of freedom system that includes multiple least squares regression and iterative matrix solutions. This thesis describes how the uncertainty of loads reported by a six component balance can be obtained by applying a direct simulation of the end-to-end data flow of a balance, from calibration through to installation, using a Monte Carlo Simulation. It is postulated that knowledge of the error propagated into the test environment through the balance will influence the choice of calibration model, and that an improved model, compared to that determined by statistical methods without this knowledge, will be obtained. Statistical approaches to the determination of a balance calibration model are driven by obtaining the best curve-fit statistics possible. This is done by adding as many coefficients to the modelling polynomial as can be statistically defended. This thesis shows that the propagated error will significantly influence the choice of polynomial coefficients. In order to do this a Performance Weighted Efficiency (PWE) parameter is defined. The PWE is a combination of the curve-fit statistic, (the back calculated error for the chosen polynomial), a value representing the overall prediction interval for the model(CI_rand), and a value representing the overall total propagated uncertainty of loads reported by the installed balance...

No description available.

The purpose of this study is to examine the geothermal potential, sustainability, and reinjection possibility of Edremit geothermal field. In order to investigate this, a numerical model consisting of a hot and cold water aquifer system is established. A two dimensional cross sectional model is set to simulate this geothermal system. Different pressure and temperature values are applied to the nodes at the boundaries to perform a steady state calibration which minimizes the computed results and observed values obtained from the near well logs. After the calibration, three alternative scenarios are proposed and the response of the pressure and temperature to these conditions is evaluated. At first the water is pumped from the wells of Yagci, Derman, Entur and ED-3 seperately at a mass rate of 5 kg/s and energy rate of 4.182 x 105 J/s. Then, in scenario 2 the water is pumped at the same rate from all the wells mentioned in the first scenario together. For the third scenario another well is opened to the geothermal system and 80% of the pumped water (temperature being 200C) is injected to the system from the wells while all the wells mentioned are working. The results of these scenarios are utilized to evaluate the reservoir in terms of its response to different production and reinjection conditions. Interpretation of the reservoir response in view of the pressure and temperature declines emphasize that such a simulation study can be applied to assess potential and sustainability of the geothermal systems.

Often predictor variables in regression models are measured with errors. This is known as an errors-in-variables (EIV) problem. The statistical analysis of the data ignoring the EIV is called naive analysis. As a result, the variance of the errors is underestimated. This affects any statistical inference that may subsequently be made about the model parameter estimates or the response prediction. In some cases (e.g. quadratic polynomial models) the parameter estimates and the model prediction is biased. The errors can occur in different ways. These errors are mainly classified into classical (i.e. occur in observational studies) or Berkson type (i.e. occur in designed experiments). This thesis addresses the problem of the Berkson EIV and their effect on the statistical analysis of data fitted using linear and nonlinear models. In particular, the case when the errors are dependent and have heterogeneous variance is studied. Both analytical and empirical tools have been used to develop new approaches for dealing with this type of errors. Two different scenarios are considered: mixture experiments where the model to be estimated is linear in the parameters and the EIV are correlated; and bioassay dose-response studies where the model to be estimated is nonlinear. EIV following Gaussian distribution, as well as the much less investigated non-Gaussian distribution are examined. When the errors occur in mixture experiments both analytical and empirical results showed that the naive analysis produces biased and inefficient estimators for the model parameters. The magnitude of the bias depends on the variances of the EIV for the mixture components, the model and its parameters. First and second Scheffé polynomials are used to fit the response. To adjust for the EIV, four different approaches of corrections are proposed. The statistical properties of the estimators are investigated, and compared with the naive analysis estimators. Analytical and empirical weighted regression calibration methods are found to give the most accurate and efficient results. The approaches require the error variance to be known prior to the analysis. The robustness of the adjusted approaches for misspecified variance was also examined. Different error scenarios of EIV in the settings of concentrations in bioassay dose-response studies are studied (i.e. dependent and independent errors). The scenarios are motivated by real-life examples. Comparisons between the effects of the errors are illustrated using the 4-prameter Hill model. The results show that when the errors are non-Gaussian, the nonlinear least squares approach produces biased and inefficient estimators. An extension of the well-known simulation-extrapolation (SIMEX) method is developed for the case when the EIV lead to biased model parameters estimators, and is called Berkson simulation-extrapolation (BSIMEX). BSIMEX requires the error variance to be known. The robustness of the adjusted approach for misspecified variance is examined. Moreover, it is shown that BSIMEX performs better than the regression calibration methods when the EIV are dependent, while the regression calibration methods are preferable when the EIV are independent.