Optimization of Turbulence Model Parameters Using the Global Search Method Combined with Machine Learning

Konstantin Barkalov, Ilya Lebedev, Marina Usova, Daria Romanova, Daniil Ryazanov and Sergei Strijhak
Additional contact information
Konstantin Barkalov: Department of Mathematical Software and Supercomputing Technologies, Lobachevsky University, 603022 Nizhni Novgorod, Russia
Ilya Lebedev: Department of Mathematical Software and Supercomputing Technologies, Lobachevsky University, 603022 Nizhni Novgorod, Russia
Marina Usova: Department of Mathematical Software and Supercomputing Technologies, Lobachevsky University, 603022 Nizhni Novgorod, Russia
Daria Romanova: Ivannikov Institute for System Programming of the Russian Academy of Sciences, 109004 Moscow, Russia
Daniil Ryazanov: Ivannikov Institute for System Programming of the Russian Academy of Sciences, 109004 Moscow, Russia
Sergei Strijhak: Ivannikov Institute for System Programming of the Russian Academy of Sciences, 109004 Moscow, Russia

Mathematics, 2022, vol. 10, issue 15, 1-20

Abstract: The paper considers the slope flow simulation and the problem of finding the optimal parameter values of this mathematical model. The slope flow is modeled using the finite volume method applied to the Reynolds-averaged Navier–Stokes equations with closure in the form of the k − ω S S T turbulence model. The optimal values of the turbulence model coefficients for free surface gravity multiphase flows were found using the global search algorithm. Calibration was performed to increase the similarity of the experimental and calculated velocity profiles. The Root Mean Square Error (RMSE) of derivation between the calculated flow velocity profile and the experimental one is considered as the objective function in the optimization problem. The calibration of the turbulence model coefficients for calculating the free surface flows on test slopes using the multiphase model for interphase tracking has not been performed previously. To solve the multi-extremal optimization problem arising from the search for the minimum of the loss function for the flow velocity profile, we apply a new optimization approach using a Peano curve to reduce the dimensionality of the problem. To speed up the optimization procedure, the objective function was approximated using an artificial neural network. Thus, an interdisciplinary approach was applied which allowed the optimal values of six turbulence model parameters to be found using OpenFOAM and Globalizer software.

Keywords: global optimization; artificial neural network; function approximation; finite volume method; CFD; OpenFOAM; interFoam (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/10/15/2708/pdf (application/pdf)
https://www.mdpi.com/2227-7390/10/15/2708/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:10:y:2022:i:15:p:2708-:d:876956

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().