Optimization of Drop Characteristics in a Carrier Cooled Gas Stream Using ANSYS and Globalizer Software Systems on the PNRPU High-Performance Cluster

Kalyulin, Stanislav L.; Shavrina, Evgenya V.; Modorskii, V. Ya.; Barkalov, Konstantin; Gergel, Victor

doi:10.1007/978-3-319-67035-5_24

Cited by 9 publications

(1 citation statement)

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“…We will consider the loss function ( 5) as the objective function ϕ(y) in the global optimization problem (4). The problems considered are characterized by the fact that the objective function ϕ(y) is not defined analytically; there is only an algorithm for computing its values at the points of the domain D. In this case, one search trial corresponds to one computation according to the model and is a time-consuming operation [75,76].…”

Section: Global Optimization Problem Statementmentioning

confidence: 99%

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

et al. 2022

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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−ωSST 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.

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Section: Global Optimization Problem Statementmentioning

confidence: 99%