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Kriging meta‐model assisted calibration of computational fluid dynamics models
Author(s) -
Kajero Olumayowa T.,
Thorpe Rex B.,
Chen Tao,
Wang Bo,
Yao Yuan
Publication year - 2016
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.15352
Subject(s) - computational fluid dynamics , kriging , calibration , bottleneck , metamodeling , gaussian process , computation , measure (data warehouse) , computer science , fluid dynamics , mathematics , experimental data , gaussian , process (computing) , algorithm , mathematical optimization , mechanics , statistics , data mining , machine learning , physics , quantum mechanics , programming language , embedded system , operating system
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‐modeling 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 generalized 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 minimizing 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. © 2016 American Institute of Chemical Engineers AIChE J , 62: 4308–4320, 2016