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Probabilistic Optimization of Two-Phase Flow Using Bayesian Models

[+] Author Affiliations
Kenji Miki, Arun Subramaniyan, Madhusudan Pai, Preetham Balasubramanyam

General Electric, Global Research Center, Niskayuna, NY

Paper No. GT2014-26490, pp. V02BT45A018; 13 pages
doi:10.1115/GT2014-26490
From:
  • ASME Turbo Expo 2014: Turbine Technical Conference and Exposition
  • Volume 2B: Turbomachinery
  • Düsseldorf, Germany, June 16–20, 2014
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 978-0-7918-4561-5
  • Copyright © 2014 by ASME

abstract

Gas-liquid two-phase flows are encountered in a variety of applications such as turbo-machinery flows, gas-turbines, ram-jet and scram-jets, automotive engines and aircraft engines. Designing systems to control such flows is enormously challenging owing to the addition of new non-dimensional groups that characterize the two-phase flow system compared to a single-phase flow. Additionally, two-phase flows can exhibit non-linear hydrodynamic instabilities that determine the overall behavior of the system.

In this study, we choose a generic two-phase flow configuration that exhibits known complexities in realistic two-phase flow systems. The goal of the study is to optimize the geometry of the two-phase flow configuration with minimal computational cost. We propose a probabilistic approach to model the stochastic system and optimize the two-phase flow system under uncertain inputs. The potential benefits of the approach are highlighted along with future directions for using probabilistic design techniques to optimize two-phase flow systems.

Copyright © 2014 by ASME

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