Eventos Anais de eventos
COBEM 2017
24th ABCM International Congress of Mechanical Engineering
MACHINE LEARNING TECHNIQUES FOR ACCURACY IMPROVEMENT OF RANS SIMULATIONS
Submission Author:
Matheus Cruz , RJ
Co-Authors:
Roney Thompson, Raphael David Aquilino Bacchi, luiz sampaio, Matheus Cruz
Presenter: Matheus Cruz
doi://10.26678/ABCM.COBEM2017.COB17-1241
Abstract
There is a wide number of applications where the flow is turbulent. Since Direct Numerical Simulation (DNS) and experiments are prohibitively expensive, the use of Reynolds Average Navier-Stokes (RANS) models is a necessity. However, the obtained models from this approach have low accuracy. This fact justifies the high demand for better models. In this work, a technique that uses machine learning, by means of neural networks, is used to correct the $\kappa$-$\omega$SST RANS model considering the DNS data as ideal. The methodologies available in the literature employ the Reynolds stress tensor as the main quantity to be corrected. Once this entity is corrected, the velocity field is recalculated by the RANS transport equations. Consequently, the obtained velocity field gets closer to DNS results. It is proposed, as a new methodology, the correction of the divergent of the Reynolds stress tensor, because it is the only part that is computed in the mean linear momentum balance. This divergence can be calculated from the mean velocity and pressure fields, which are well converged, using the mean linear momentum equation. The results obtained so far have demonstrated that the divergent correction of the RANS turbulent stress field is able to reconstruct mean velocity fields.
Keywords
machine learning, Random Forest, OpenFOAM, DNS, Square Duct
