Imperial College London
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DrMichaelBluck

Faculty of Engineering, Department of Mechanical Engineering

Reader in Nuclear Engineering
 
 
 
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Contact

 

+44 (0)20 7594 7055m.bluck

 
 
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Location

 

658City and Guilds BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Duan:2019:10.1016/j.ijheatfluidflow.2019.108497,
author = {Duan, Y and Cooling, C and Ahn, JS and Jackson, C and Flint, A and Eaton, M and Bluck, M},
doi = {10.1016/j.ijheatfluidflow.2019.108497},
journal = {International Journal of Heat and Fluid Flow},
title = {Using a Gaussian process regression inspired method to measure agreement between the experiment and CFD simulations},
url = {http://dx.doi.org/10.1016/j.ijheatfluidflow.2019.108497},
volume = {80},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - This paper presents a Gaussian process regression inspired method to measure the agreement between experiment and computational fluid dynamics (CFD) simulation. Because of misalignments between experimental and numerical outputs in spatial or parameter space, experimental data are not always suitable for quantitative assessing the numerical models. In this proposed method, the cross-validated Gaussian process regression (GPR) model, trained based on experimental measurements, is used to mimic the measurements at positions where there are no experimental data. The agreement between an experiment and the simulation is mimicked by the agreement between the simulation and GPR models. The statistically weighted square error is used to provide tangible information for the local agreement. The standardised Euclidean distance is used for assessing the overall agreement.The method is then used to assess the performance of four scale-resolving CFD methods, such as URANS k-ω-SST, SAS-SST, SAS-KE, and IDDES-SST, in simulating a prism bluff-body flow. The local statistically weighted square error together with standardised Euclidean distance provide additional insight, over and above the qualitative graphical comparisons. In this example scenario, the SAS-SST model marginally outperformed the IDDES-SST and better than the other two other, according to the distance to the validated GPR models.
AU  - Duan,Y
AU  - Cooling,C
AU  - Ahn,JS
AU  - Jackson,C
AU  - Flint,A
AU  - Eaton,M
AU  - Bluck,M
DO  - 10.1016/j.ijheatfluidflow.2019.108497
PY  - 2019///
SN  - 0142-727X
TI  - Using a Gaussian process regression inspired method to measure agreement between the experiment and CFD simulations
T2  - International Journal of Heat and Fluid Flow
UR  - http://dx.doi.org/10.1016/j.ijheatfluidflow.2019.108497
UR  - http://hdl.handle.net/10044/1/74684
VL  - 80
ER  -
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