0

Full Content is available to subscribers

Subscribe/Learn More  >

Uncertainty Quantification, Rare Events and Mission Optimization: Stochastic Variations of Metal Temperature During a Transient

[+] Author Affiliations
F. Montomoli, D. Amirante, N. Hills, M. Massini

University of Surrey, Guildford, UK

S. Shahpar

Rolls-Royce plc, Derby, UK

Paper No. GT2014-25398, pp. V05CT16A008; 11 pages
doi:10.1115/GT2014-25398
From:
  • ASME Turbo Expo 2014: Turbine Technical Conference and Exposition
  • Volume 5C: Heat Transfer
  • Düsseldorf, Germany, June 16–20, 2014
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-4573-8
  • Copyright © 2014 by Rolls-Royce plc

abstract

Gas turbines are designed to follow specific missions and the metal temperature is usually predicted with deterministic methods. However, in real life the mission is subjected to strong variations which can affect the thermal response of the components. This paper presents a stochastic analysis of the metal temperature variations during a gas turbine transient.

A Monte Carlo Method (MCM) with Meta Model is used to evaluate the probability distribution of the stator disk temperature. The MCM is applied to a series of CFD simulations of a stator well, whose geometry is modified according to the deformations predicted during the engine cycle by a coupled thermo-mechanical analysis of the metal components. It is shown that even considering a narrow band for the stochastic output, +/− σ, the transient thermal gradients can be up to two orders of magnitude greater than those obtained with a standard deterministic analysis. Moreover, a small variation in the tail of the input probability density function, a rare event, can have serious consequences on the uncertainty level of the temperature.

Rare events although inevitable they are not usually considered during the design phase. In this paper it is shown for the first time that is possible to mitigate their effect, minimizing the maximum standard deviation induced by the tail of the input PDF. The mission optimization reduces the maximum standard deviation by 15% and the mean standard deviation of about 12%. The maximum thermal gradients are also reduced by 10%, although this was not the parameter used as the goal in the optimisation study.

Copyright © 2014 by Rolls-Royce plc

Figures

Tables

Interactive Graphics

Video

Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In