In this paper, an elastic constitutive model based on the Eulerian corotational rate of the logarithmic strain tensor is proposed. Using this model, the large deformation of a closed cycle containing tension, shear, compression and inverse shear is analyzed. Since the deformation path includes a closed cycle and the material is considered as an isotropic elastic material, the normal and shear c...

This study presents a novel synthetic inflow generator capable of producing random field matching realistic set two-point statistics with minimal input. The method is based on two main elements. first element procedure to infer covariance tensors from readily available data (e.g., freestream velocity, boundary layer thickness, and turbulence intensity) by preliminary Reynolds-averaged Navier–St...

The properties of the mean momentum balance in turbulent boundary layer, pipe and channel flows are explored both experimentally and theoretically. Available highquality data reveal a dynamically relevant four-layer description that is a departure from the mean profile four-layer description traditionally and nearly universally ascribed to turbulent wall flows. Each of the four layers is charac...

In this work, we propose using an ensemble Kalman method to learn a nonlinear eddy viscosity model, represented as tensor basis neural network, from velocity data. Data-driven turbulence models have emerged promising alternative traditional for providing closure mapping the mean velocities Reynolds stresses. Most data-driven in category need full-field stress data training, which not only place...

Three Reynolds-Stress Models (RSMs) have been benchmarked on industrial configurations with aeronautical applications. The models are first compared a zero-pressure-gradient boundary layer, which highlights the differences in near-wall approaches of models. Results then analyzed for Skåre & Krogstad adverse-pressure-gradient layer and Common Research Model (CRM) aircraft two Reynolds numbers. B...