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Kriging meta-model assisted calibration of computational fluid dynamics models

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journal contribution
posted on 14.06.2016, 11:09 by Olumayowa T. Kajero, Rex B. Thorpe, Tao Chen, Bo Wang, Yuan Yao
Computational fluid dynamics (CFD) is a simulation technique widely used in chemical and process engineering applications. However, computation has become a bottleneck when calibration of CFD models with experimental data (also known as model parameter estimation) is needed. In this research, the kriging meta-modelling approach (also termed Gaussian process) was coupled with expected improvement (EI) to address this challenge. A new EI measure was developed for the sum of squared errors (SSE) which conforms to a generalised chi-square distribution and hence existing normal distribution-based EI measures are not applicable. The new EI measure is to suggest the CFD model parameter to simulate with, hence minimising SSE and improving match between simulation and experiments. The usefulness of the developed method was demonstrated through a case study of a single-phase flow in both a straight-type and a convergent-divergent-type annular jet pump, where a single model parameter was calibrated with experimental data.

History

Citation

AIChE Journal, 2016, 62 (12), pp. 4308–4320

Author affiliation

/Organisation/COLLEGE OF SCIENCE AND ENGINEERING/Department of Mathematics

Version

AM (Accepted Manuscript)

Published in

AIChE Journal

Publisher

Wiley on behalf of American Institute of Chemical Engineers (AIChE)

issn

0001-1541

eissn

1547-5905

Acceptance date

27/05/2016

Copyright date

2016

Available date

03/06/2017

Publisher version

http://onlinelibrary.wiley.com/doi/10.1002/aic.15352/abstract

Notes

The file associated with this record is under a 12-month embargo from publication in accordance with the publisher's self-archiving policy. The full text may be available through the publisher links provided above.

Language

en

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