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Title: Kriging meta-model assisted calibration of computational fluid dynamics models
Authors: Kajero, Olumayowa T.
Thorpe, Rex B.
Chen, Tao
Wang, Bo
Yao, Yuan
First Published: 3-Jun-2016
Publisher: Wiley on behalf of American Institute of Chemical Engineers (AIChE)
Citation: AIChE Journal, 2016, 62 (12), pp. 4308–4320
Abstract: 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.
DOI Link: 10.1002/aic.15352
ISSN: 0001-1541
eISSN: 1547-5905
Version: Post-print
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2016 American Institute of Chemical Engineers (AIChE). Deposited with reference to the publisher’s archiving policy available on the SHERPA/RoMEO website.
Description: 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.
Appears in Collections:Published Articles, Dept. of Mathematics

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