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Uncertainty Quantification: A Stochastic Method for Heat Transfer Prediction Using LES

[+] Author Affiliations
M. Carnevale

University of Cambridge, Cambridge, UKUniversity of Florence, Florence, Italy

F. Montomoli

University of Cambridge, Cambridge, UKUniversity of Surrey, Guildford, UK

A. D’Ammaro

University of Cambridge, Cambridge, UK

S. Salvadori

University of Florence, Florence, Italy

Paper No. GT2012-68142, pp. 59-69; 11 pages
  • ASME Turbo Expo 2012: Turbine Technical Conference and Exposition
  • Volume 4: Heat Transfer, Parts A and B
  • Copenhagen, Denmark, June 11–15, 2012
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-4470-0
  • Copyright © 2012 by ASME


In Computational Fluid Dynamics (CFD) is possible to identify namely two uncertainties: epistemic, related to the turbulence model, and aleatoric, representing the random-unknown conditions such as the boundary values and or geometrical variations. In the field of epistemic uncertainty, Large Eddy Simulation (LES and DES) is the state of the art in terms of turbulence closures to predict the heat transfer in internal channels. The problem concerning the stochastic variations and how to include these effects in the LES studies is still open.

In this paper, for the first time in literature, a stochastic approach is proposed to include these variations in LES. By using a classical Uncertainty Quantification approach, the Probabilistic Collocation Method is coupled to Numerical Large Eddy Simulation (NLES) in a duct with pin fins. The Reynolds number has been chosen as a stochastic variable with a normal distribution. It is representative of the uncertainties associated to the operating conditions, i.e. velocity and density, and geometrical variations such as the pin fin diameter. This work shows that by assuming a Gaussian distribution for the value of Reynolds number of +/−25%, is possible to define the probability to achieve a specified heat loading under stochastic conditions, which can affect the component life by more than 100%.

The same method, applied to a steady RANS, generates a different level of uncertainty. This procedure proves that the uncertainties related to the unknown conditions, aleatoric, and those related to the physical model, epistemic, are strongly interconnected. This result has directed consequences in the Uncertainty Quantification science and not only in the gas turbine world.

Copyright © 2012 by ASME



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