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Comparative Assessment of Direct and Indirect Probabilistic Methods for Thermomechanical Analysis of Structural Components in Gas Turbines

[+] Author Affiliations
Vitali V. Volovoi, Mark Waters, Dimitri N. Mavris

Georgia Institute of Technology, Atlanta, GA

Paper No. GT2003-38510, pp. 155-162; 8 pages
doi:10.1115/GT2003-38510
From:
  • ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference
  • Volume 4: Turbo Expo 2003
  • Atlanta, Georgia, USA, June 16–19, 2003
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-3687-8 | eISBN: 0-7918-3671-1
  • Copyright © 2003 by ASME

abstract

Various sources of uncertainty greatly affect the life of structural components of gas turbines. Probabilistic approaches provide a means to evaluate these uncertainties; however, the accuracy of these approaches often remains unknown. Published quantitative studies of the effectiveness of various uncertainty quantification techniques are usually based on very simple examples. This is contrasted by the large-size finite element models that are used for complex geometries of critical structural parts such as turbine blades or nozzles. In such real-life applications the expenses of the “function calls” (runs of these models) preclude systematic studies of probabilistic methods. These expenses are attributed not only to the actual runs of the model, but to the difficulties in parametrically changing the model as well. Such a “complexity gap” leads to a justifiable concern over whether the trends identified in academic studies are relevant to these industrial applications. As a result, structural engineers end up with the number of function calls that they can afford rather than what would be needed for the required level of accuracy. The present effort intends to bridge this gap by studying a mid level problem: a simplified notional finite element model of a gas turbine component is presented. Despite its simplicity, the model is designed to reflect the major features of more realistic models. The parametric changes of the model are fully automated, which allows for performing an extensive set of benchmark tests that help to determine the relative merits of various existing probabilistic techniques for component life assessment. Several meta-modeling techniques are investigated and their performance compared based on direct sampling methods. In this context, various Design of Experiments (DoE) methods are studied. The results are used to construct the Response Surface Equations (RSE) as well as the kriging models. It is emphasized that changes in the relative locations of the critical points induced by variation of independent parameters can critically affect the overall fidelity of the modeling; the means of remedying such a degradation in precision are discussed. Finally, it is shown that when the ranges of independent variables are large, kriging generally provides precision that is an order of magnitude better than RSE for the same DoE.

Copyright © 2003 by ASME
Topics: Gas turbines

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