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Kriging meta-model assisted calibration of computational fluid dynamics models

Kajero, OT, Thorpe, RB and Chen, T (2016) Kriging meta-model assisted calibration of computational fluid dynamics models AIChE Journal, 62 (2). pp. 4308-4320.

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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.

Item Type: Article
Subjects : Chemical & Process Engineering
Divisions : Faculty of Engineering and Physical Sciences > Chemical and Process Engineering
Authors :
AuthorsEmailORCID
Kajero, OTUNSPECIFIEDUNSPECIFIED
Thorpe, RBUNSPECIFIEDUNSPECIFIED
Chen, TUNSPECIFIEDUNSPECIFIED
Date : 21 June 2016
Identification Number : 10.1002/aic.15352
Copyright Disclaimer : This is the peer reviewed version of the following article: Kajero, OT, Thorpe, RB and Chen, T (2016) Kriging meta-model assisted calibration of computational fluid dynamics models. AIChE Journal, which has been published in final form at http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1547-5905. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Related URLs :
Depositing User : Symplectic Elements
Date Deposited : 02 Jun 2016 08:29
Last Modified : 22 Nov 2016 15:24
URI: http://epubs.surrey.ac.uk/id/eprint/810901

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