Application of the Stochastic Field Method to two phase flow

Application of the Stochastic Field Method to two phase flow The Stochastic Field Method was introduced in the field of combustion and represents a Eulerian Monte‐Carlo technique. It was first transfered to the field of multiphase flow in [1] facing a problem in nuclear technology. Within this work it is applied to cavitating flows in the automotive sector. Both phases, the continuous and the dispersed phase, are observed using a Eulerian perspective which fits the architecture of CFD solvers resulting in efficient computations. Instead of solving the behaviour of individual bubbles, as in Lagrangian Particle Methods, variations of volume fractions of the dispersed phase in each computational mesh cell are considered. A stochastic term is introduced into the transport equations for the volume fractions of the dispersed phase via a Wiener process. Thus sampling over several time steps provides the probability density function of volume fraction in each mesh cell. The implementation is carried out by compiling user‐functions to a commercial CFD code. A test case representing an injection system in an automotive application is presented. Moreover strategies to implement the effect of coalescence are shown. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Proceedings in Applied Mathematics & Mechanics Wiley

Application of the Stochastic Field Method to two phase flow

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Publisher
Wiley
Copyright
Copyright © 2017 Wiley Subscription Services
ISSN
1617-7061
eISSN
1617-7061
D.O.I.
10.1002/pamm.201710299
Publisher site
See Article on Publisher Site

Abstract

The Stochastic Field Method was introduced in the field of combustion and represents a Eulerian Monte‐Carlo technique. It was first transfered to the field of multiphase flow in [1] facing a problem in nuclear technology. Within this work it is applied to cavitating flows in the automotive sector. Both phases, the continuous and the dispersed phase, are observed using a Eulerian perspective which fits the architecture of CFD solvers resulting in efficient computations. Instead of solving the behaviour of individual bubbles, as in Lagrangian Particle Methods, variations of volume fractions of the dispersed phase in each computational mesh cell are considered. A stochastic term is introduced into the transport equations for the volume fractions of the dispersed phase via a Wiener process. Thus sampling over several time steps provides the probability density function of volume fraction in each mesh cell. The implementation is carried out by compiling user‐functions to a commercial CFD code. A test case representing an injection system in an automotive application is presented. Moreover strategies to implement the effect of coalescence are shown. (© 2017 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Journal

Proceedings in Applied Mathematics & MechanicsWiley

Published: Jan 1, 2017

References

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