Modern gas turbine engines run at extremely high temperatures which require the high pressure turbine blades to be extensively cooled in order to reach life requirements. This must be done using the minimum amount of coolant in order to reduce the negative impacts on the cycle efficiency. In the design process the cooling configuration and stress distribution must be carefully considered before verification of the design is conducted. Improvements to all three of these blade design areas are presented in this thesis which investigates internal cooling systems in the form of ribbed, radial passages and leading edge impingement systems. The effect of rotation on the heat transfer distribution in ribbed radial passages is investigated. An engine representative triple-pass serpentine passage, typical of a gas turbine mid-chord HP blade passage, is simulated using common industrial RANS CFD methodology with the results compared to those from the RHTR, a rotating experimental facility. The simulations are found to perform well under stationary conditions with the rotational cases proving more challenging. Further study and simulations of radial passages are undertaken in order to understand the salient flow and heat transfer features found, namely the inlet velocity profile and rib orientation relative to the mainstream flow. A consistent rib direction gives improved heat transfer characteristics whilst careful design of inlet conditions could give an optimised heat transfer distribution. The effect of rotation on the heat transfer distribution in leading edge impingement systems is investigated. As for the radial passages, RANS CFD simulations are compared and validated against experimental data from a rotating heat transfer rig. The simulations provide accurate average heat transfer levels under stationary and rotating conditions. The full target surface heat transfer in an engine realistic leading edge impingement system is investigated. Experimental data is compared to RANS CFD simulations. Experimental results are in line with previous studies and the simulations provide reasonable heat transfer predictions. A new method of combined thermal and mechanical analysis is presented and validated for a leading edge impingement system. Conjugate CFD simulations are used to provide a metal temperature distribution for a mechanical analysis. The effect of changes to the geometry and temperature profile on stress levels are studied and methods to improve blade stress levels are presented. The thermal FEA model is used to quantify the effect of HTC alterations on different surfaces within a leading edge impingement system, in terms of both temperature and stress distributions. These are then used to provide improved target HTC distributions in order to increase blade life. A new method using Gaussian process regression for thermal matching is presented and validated for a leading edge impingement case. A simplified model is matched to a full conjugate CFD solution to test the method's quality and reliability. It is then applied to two real engine blades and matched to data from thermal paint tests. The matches obtained are very close, well within experimental accuracy levels, and offer consistency and speed improvements over current methodologies.

MoSiB alloys show promise as the next-generation turbine blade material due to their high-temperature strength and oxidation resistance afforded by a protective borosilicate surface layer. Powder processing and reactive synthesis of these alloys has proven to be a viable method and offers several advantages over conventional melt processing routes. Microstructures obtained have well-dispersed intermetallics in a continuous matrix of molybdenum solid-solution (Mo-ss). However, bend testing of pure Mo and Mo-ss samples has shown that, while the powder processing route can produce ductile Mo metal, the hardening effect of Si and B in solid-solution renders the matrix brittle. Testing at elevated temperatures (200°C) was performed in order to determine the ductile-to-brittle transition temperature of the metal as an indication of ductility. Methods of ductilizing the Mo-ss matrix such as annealing and alloying additions have been investigated.

The dissertation focuses on estimating the uncertainty associated with stress/strain prediction procedures from dynamic test data used in turbine blade analysis. An accurate prediction of the maximum response levels for physical components during in-field operating conditions is essential for evaluating their performance and life characteristics, as well as for investigating how their behavior critically impacts system design and reliability assessment. Currently, stress/strain inference for a dynamic system is based on the combination of experimental data and results from the analytical/numerical model of the component under consideration. Both modeling challenges and testing limitations, however, contribute to the introduction of various sources of uncertainty within the given estimation procedure, and lead ultimately to diminished accuracy and reduced confidence in the predicted response. The objective of this work is to characterize the uncertainties present in the current response estimation process and provide a means to assess them quantitatively. More specifically, proposed in this research is a statistical methodology based on a Bayesian-network representation of the modeling process which allows for a statistically rigorous synthesis of modeling assumptions and information from experimental data. Such a framework addresses the problem of multi-directional uncertainty propagation, where standard techniques for unidirectional propagation from inputs' uncertainty to outputs' variability are not suited. Furthermore, it allows for the inclusion within the analysis of newly available test data that can provide indirect evidence on the parameters of the structure's analytical model, as well as lead to a reduction of the residual uncertainty in the estimated quantities. As part of this work, key uncertainty sources (i.e., material and geometric properties, sensor measurement and placement, as well as noise due data processing limitations) are investigated, and their impact upon the system response estimates is assessed through sensitivity studies. The results are utilized for the identification of the most significant contributors to uncertainty to be modeled within the developed Bayesian inference scheme. Simulated experimentation, statistically equivalent to specified real tests, is also constructed to generate the data necessary to build the appropriate Bayesian network, which is then infused with actual experimental information for the purpose of explaining the uncertainty embedded in the response predictions and quantifying their inherent accuracy.

Thesis: M.S., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 1978 / Includes bibliographical references. / by Song Y. Chung. / M.S. / M.S. Massachusetts Institute of Technology, Department of Aeronautics and Astronautics

Reliability prediction of components operating in complex systems has historically been conducted in a statistically isolated manner. Current physics-based, i.e. mechanistic, component reliability approaches focus more on component-specific attributes and mathematical algorithms and not enough on the influence of the system. The result is that significant error can be introduced into the component reliability assessment process. The objective of this study is the development of a framework that infuses the influence of the system into the process of conducting mechanistic-based component reliability assessments. The formulated framework consists of six primary steps. The first three steps, identification, decomposition, and synthesis, are qualitative in nature and employ system reliability and safety engineering principles for an appropriate starting point for the component reliability assessment. The most unique steps of the framework are the steps used to quantify the system-driven local parameter space and a subsequent step using this information to guide the reduction of the component parameter space. The local statistical space quantification step is accomplished using two newly developed multivariate probability tools: Multi-Response First Order Second Moment and Taylor-Based Inverse Transformation. Where existing joint probability models require preliminary statistical information of the responses, these models combine statistical information of the input parameters with an efficient sampling of the response analyses to produce the multi-response joint probability distribution. Parameter space reduction is accomplished using Approximate Canonical Correlation Analysis (ACCA) employed as a multi-response screening technique. The novelty of this approach is that each individual local parameter and even subsets of parameters representing entire contributing analyses can now be rank ordered with respect to their contribution to not just one response, but the entire vector of component responses simultaneously. The final step of the framework is the actual probabilistic assessment of the component. Variations of this final step are given to allow for the utilization of existing probabilistic methods such as response surface Monte Carlo and Fast Probability Integration. The framework developed in this study is implemented to conduct the finite-element based reliability prediction of a gas turbine airfoil involving several failure responses. The framework, as implemented resulted in a considerable improvement to the accuracy of the part reliability assessment and an increased statistical understanding of the component failure behavior.