Progressive augmentation of turbulence models for flow separation by multi-case computational fluid dynamics driven surrogate optimization

TY - JOUR

T1 - Progressive augmentation of turbulence models for flow separation by multi-case computational fluid dynamics driven surrogate optimization

AU - Amarloo, Ali

AU - Rincón, Mario Javier

AU - Reclari, Martino

AU - Abkar, Mahdi

PY - 2023/12/1

Y1 - 2023/12/1

N2 - In the field of data-driven turbulence modeling, the consistency of the a posteriori results and generalizability are the most critical aspects of new models. In this study, we combine a multi-case surrogate optimization technique with a progressive augmentation approach to enhance the performance of the popular k − ω shear stress transport (SST) turbulence model in the prediction of flow separation. We introduce a separation factor into the transport equation of a turbulent specific dissipation rate (ω) to correct the underestimation of the turbulent viscosity by the k − ω SST model in the case of flow separation for two-dimensional cases. The new model is optimized based on their performance on the training cases including periodic hills and curved backward-facing step flow. Simulation of the channel flow is likewise included in the optimization process to guarantee that the original performance of k − ω SST is preserved in the absence of separation. The new model is verified on multiple unseen cases with different Reynolds numbers and geometries. Results show a significant improvement in the prediction of the recirculation zone, velocity components, and distribution of the friction coefficient in both training and testing cases, where flow separation is expected. The performance of the new models on the test case with no separation shows that they preserve the successful performance of k − ω SST when flow separation is not expected.

AB - In the field of data-driven turbulence modeling, the consistency of the a posteriori results and generalizability are the most critical aspects of new models. In this study, we combine a multi-case surrogate optimization technique with a progressive augmentation approach to enhance the performance of the popular k − ω shear stress transport (SST) turbulence model in the prediction of flow separation. We introduce a separation factor into the transport equation of a turbulent specific dissipation rate (ω) to correct the underestimation of the turbulent viscosity by the k − ω SST model in the case of flow separation for two-dimensional cases. The new model is optimized based on their performance on the training cases including periodic hills and curved backward-facing step flow. Simulation of the channel flow is likewise included in the optimization process to guarantee that the original performance of k − ω SST is preserved in the absence of separation. The new model is verified on multiple unseen cases with different Reynolds numbers and geometries. Results show a significant improvement in the prediction of the recirculation zone, velocity components, and distribution of the friction coefficient in both training and testing cases, where flow separation is expected. The performance of the new models on the test case with no separation shows that they preserve the successful performance of k − ω SST when flow separation is not expected.

UR - http://www.scopus.com/inward/record.url?scp=85181060658&partnerID=8YFLogxK

U2 - 10.1063/5.0174470

DO - 10.1063/5.0174470

M3 - Journal article

SN - 1070-6631

VL - 35

JO - Physics of Fluids

JF - Physics of Fluids

IS - 12

M1 - 125154

ER -