Subgrid-scale stress parameterization for anisotropic turbomachinery flow as deduced from stereoscopic particle image velocimetry measurements

Abstract

The present study searches for parametric subgrid-scale models for the turbomachinery flow based on single-plane stereoscopic particle image velocimetry measurements. A group of deformation tensors are used as the predictors to parameterize the subgrid-scale stress tensor as the response. Missing terms in the rotation rate tensor are completed with a novel approach which is based on geometrical relations between deformation and subgrid-scale tensors. The optimal relations for parametric subgrid-scale models are shaped after minimizing the square of error between modeled and real SGS stresses. The most superior and inferior one-predictor models are based on strain rate and rotation rate tensors, respectively. The two-predictor models show the most efficient prediction of subgrid-scale stress where the correlation coefficient is up to 43% larger than that of the classical Smagorinsky model. The three- to five-predictor models are inefficient since they give minor enhancement of correlation coefficient with a considerably larger computational cost.