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

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−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.

Compensation of Atmospheric Turbulence Phase Distortion Using Neural Network Adaptive Optics

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Estimating the Critical Velocity of the Incipient Motion of Particles on the Cuttings Bed Surface: An Experimental and Theoretical Analysis

“…Compared to other models, the incipient velocity model is easier to understand, providing a more direct guide for on-site engineers to optimize the discharge process. In recent years, in the field of fluid mechanics, machine learning methods have been used to process and analyze large amounts of experimental or simulated data to extract patterns or regularities related to the behavior of solid-liquid two-phase flows [22][23][24]. This combination can help researchers better understand and predict the characteristics of solid-liquid two-phase flows.…”