Using a Gaussian process regression inspired method to measure agreement between the experiment and CFD simulations
| Autor: | Adam Flint, Matthew D. Eaton, Yu Duan, C.M. Cooling, Ji Soo Ahn, Michael J. Bluck, Christopher Jackson |
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| Rok vydání: | 2019 |
| Předmět: |
Fluid Flow and Transfer Processes
business.industry Mechanical Engineering Flow (psychology) Experimental data 02 engineering and technology Computational fluid dynamics Parameter space 0915 Interdisciplinary Engineering Condensed Matter Physics 01 natural sciences Measure (mathematics) 0901 Aerospace Engineering 010305 fluids & plasmas Euclidean distance 020303 mechanical engineering & transports 0203 mechanical engineering Kriging 0103 physical sciences Ground-penetrating radar Mechanical Engineering & Transports business Algorithm 0913 Mechanical Engineering Mathematics |
| Zdroj: | International Journal of Heat and Fluid Flow. 80:108497 |
| ISSN: | 0142-727X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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