In this paper, a turbulence model based on deep neural network is developed for turbulent flow around airfoil at high Reynolds numbers. According to the data got from Spalart-Allmaras (SA) model, we build that maps features eddy viscosity. The then used replace SA mutually couple with CFD solver. We suitable data-driven mainly inputs, outputs and loss function of model. A feature selection meth...

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...

The optimal placement of sensors for the estimation of turbulence model parameters in computational fluid dynamics is presented. The information entropy (IE), applied on the posterior uncertainty of the model parameters inferred from Bayesian analysis, is used as a scalar measure of uncertainty. Using an asymptotic approximation, the IE depends on nominal values of the CFD model and prediction ...

In this paper, a performance prediction method is proposed for the design of stratospheric propeller. The Spalart–Allmaras (S–A) model was used to calculate airfoil FX63, and polynomial fitting utilized establish database lift drag coefficient. A computational fluid dynamics (CFD) applied at different altitudes prove feasibility method. CFD results were compared with vortex theory prediction; r...

Numerical investigation was performed on NACA 0015 which is a symmetric airfoil. Pressure distribution and then lift and drag forces are verified. Changing of ground clearance was a considerable point. Also the angle of attack was changed from 0° to 10°. Pressure coefficient reaches its higher amounts on the wing lower surface when the ground clearance diminishes. Increment of the angle of atta...

This work presents a comparative study of Unsteady Reynolds–Averaged Navier–Stokes (URANS), Detached Eddy Simulations (DES) and Delayed (DDES) turbulence modeling approaches by performing numerical investigation with the ANSYS-FLUENT software package on full-scale model Jetstream 31 aircraft. The lift drag coefficients obtained from different models are compared flight test data, wind tunnel da...

Karthik Duraisamy Assistant Professor, Department of Aerospace Engineering, University of Michigan, Ann Arbor, MI 48104 Abstract A modeling paradigm is developed to augment predictive models of turbulence by effectively utilizing limited data generated from physical experiments. The key components of our approach involve inverse modeling to infer the spatial distribution of model discrepancies,...

From the simplest models to complex deep neural networks, modeling turbulence with machine learning techniques still offers multiple challenges. In this context, present contribution proposes a robust strategy using patch-based training learn turbulent viscosity from flow velocities, and demonstrates its efficient use on Spallart-Allmaras model. Training datasets are generated for past two-dime...