Separation Prediction of Large Separation with Reynolds Stress Models

2013 ◽  
Author(s):  
Michael E. Olsen ◽  
Randolph P. Lillard ◽  
Scott M. Murman ◽  
Melissa B. Rivers ◽  
Kurtis R. Long ◽  
...  
Keyword(s):  
Reynolds Stress ◽  
Large Separation ◽  
Stress Models

scholarly journals Reynolds stress models of convection in convective cores

2006 ◽  
Vol 2 (S239) ◽  
pp. 77-79 ◽  
Author(s):  
Ian W Roxburgh ◽  
Friedrich Kupka
Keyword(s):  
Reynolds Stress ◽  
Algebraic Closure ◽  
Spherical Geometry ◽  
Turbulent Convection ◽  
Closure Models ◽  
Non Local ◽  
Stress Models

AbstractWe investigate the properties of non-local Reynolds stress models of turbulent convection in a spherical geometry. Regularity at the centre r=0 places constraints on the behaviour of 3rd order moments. Some of the down-gradient and algebraic closure models have inconsistent behaviour at r=0. A combination of down-gradient and algebraic closures gives a consistent prescription that can be used to model convection in stellar cores.

scholarly journals Development and Use of Machine-Learnt Algebraic Reynolds Stress Models for Enhanced Prediction of Wake Mixing in Low-Pressure Turbines

2019 ◽  
Vol 141 (4) ◽  
Author(s):  
H. D. Akolekar ◽  
J. Weatheritt ◽  
N. Hutchins ◽  
R. D. Sandberg ◽  
G. Laskowski ◽  
...  
Keyword(s):  
Reynolds Stress ◽  
Gene Expression Programming ◽  
A Priori ◽  
Low Pressure ◽  
Navier Stokes ◽  
Flow Features ◽  
Turbulence Closures ◽  
Rans Models ◽  
Development Analysis ◽  
Stress Models

Nonlinear turbulence closures were developed that improve the prediction accuracy of wake mixing in low-pressure turbine (LPT) flows. First, Reynolds-averaged Navier–Stokes (RANS) calculations using five linear turbulence closures were performed for the T106A LPT profile at isentropic exit Reynolds numbers 60,000 and 100,000. None of these RANS models were able to accurately reproduce wake loss profiles, a crucial parameter in LPT design, from direct numerical simulation (DNS) reference data. However, the recently proposed kv2¯ω transition model was found to produce the best agreement with DNS data in terms of blade loading and boundary layer behavior and thus was selected as baseline model for turbulence closure development. Analysis of the DNS data revealed that the linear stress–strain coupling constitutes one of the main model form errors. Hence, a gene-expression programming (GEP) based machine-learning technique was applied to the high-fidelity DNS data to train nonlinear explicit algebraic Reynolds stress models (EARSM), using different training regions. The trained models were first assessed in an a priori sense (without running any RANS calculations) and showed much improved alignment of the trained models in the region of training. Additional RANS calculations were then performed using the trained models. Importantly, to assess their robustness, the trained models were tested both on the cases they were trained for and on testing, i.e., previously not seen, cases with different flow features. The developed models improved prediction of the Reynolds stress, turbulent kinetic energy (TKE) production, wake-loss profiles, and wake maturity, across all cases.

scholarly journals Study on bubble-induced turbulence in pipes and containers with Reynolds-stress models

2022 ◽  
Author(s):  
Yixiang Liao ◽  
Tian Ma
Keyword(s):  
Numerical Simulation ◽  
Reynolds Stress ◽  
Large Scale ◽  
Bubbly Flow ◽  
Reynolds Stress Model ◽  
Viscosity Models ◽  
Stress Components ◽  
Specific Source ◽  
Stress Models ◽  
Made In

AbstractBubbly flow still represents a challenge for large-scale numerical simulation. Among many others, the understanding and modelling of bubble-induced turbulence (BIT) are far from being satisfactory even though continuous efforts have been made. In particular, the buoyancy of the bubbles generally introduces turbulence anisotropy in the flow, which cannot be captured by the standard eddy viscosity models with specific source terms representing BIT. Recently, on the basis of bubble-resolving direct numerical simulation data, a new Reynolds-stress model considering BIT was developed by Ma et al. (J Fluid Mech, 883: A9 (2020)) within the Euler—Euler framework. The objective of the present work is to assess this model and compare its performance with other standard Reynolds-stress models using a systematic test strategy. We select the experimental data in the BIT-dominated range and find that the new model leads to major improvements in the prediction of full Reynolds-stress components.

scholarly journals Reynolds-averaged Navier–Stokes equations with explicit data-driven Reynolds stress closure can be ill-conditioned

2019 ◽  
Vol 869 ◽  
pp. 553-586 ◽  
Author(s):  
Jinlong Wu ◽  
Heng Xiao ◽  
Rui Sun ◽  
Qiqi Wang
Keyword(s):  
Reynolds Stress ◽  
Condition Number ◽  
Stokes Equations ◽  
Plane Channel ◽  
Channel Flows ◽  
Data Driven ◽  
Navier Stokes ◽  
Rans Equations ◽  
Reynolds Averaged Navier Stokes ◽  
Stress Models

Reynolds-averaged Navier–Stokes (RANS) simulations with turbulence closure models continue to play important roles in industrial flow simulations. However, the commonly used linear eddy-viscosity models are intrinsically unable to handle flows with non-equilibrium turbulence (e.g. flows with massive separation). Reynolds stress models, on the other hand, are plagued by their lack of robustness. Recent studies in plane channel flows found that even substituting Reynolds stresses with errors below 0.5 % from direct numerical simulation databases into RANS equations leads to velocities with large errors (up to 35 %). While such an observation may have only marginal relevance to traditional Reynolds stress models, it is disturbing for the recently emerging data-driven models that treat the Reynolds stress as an explicit source term in the RANS equations, as it suggests that the RANS equations with such models can be ill-conditioned. So far, a rigorous analysis of the condition of such models is still lacking. As such, in this work we propose a metric based on local condition number function for a priori evaluation of the conditioning of the RANS equations. We further show that the ill-conditioning cannot be explained by the global matrix condition number of the discretized RANS equations. Comprehensive numerical tests are performed on turbulent channel flows at various Reynolds numbers and additionally on two complex flows, i.e. flow over periodic hills, and flow in a square duct. Results suggest that the proposed metric can adequately explain observations in previous studies, i.e. deteriorated model conditioning with increasing Reynolds number and better conditioning of the implicit treatment of the Reynolds stress compared to the explicit treatment. This metric can play critical roles in the future development of data-driven turbulence models by enforcing the conditioning as a requirement on these models.

Applications of EARSM Turbulence Models to Internal Flows

2012 ◽  
Author(s):  
O. Z. Mehdizadeh ◽  
L. Temmerman ◽  
B. Tartinville ◽  
Ch. Hirsch
Keyword(s):  
Reynolds Stress ◽  
High Performance ◽  
Eddy Viscosity ◽  
Computational Cost ◽  
Turbulence Models ◽  
Industrial Applications ◽  
Mean Flow ◽  
Viscosity Models ◽  
Stress Models ◽  
Corner Flows

Turbulence modeling remains an active CFD development front for turbomachinery as well as for general industrial applications. While DNS and even LES still seem out of reach within the typical industrial design cycle due to their high computational cost, RANS-based models remain the workhorse of CFD. Currently, the most widely used models are Linear Eddy-Viscosity Models (LEVM), despite their known limitations for certain flow complexities. Therefore, extending the reliability of eddy-viscosity models to more complex flows without significantly increasing the computational cost can immediately contribute to more reliable CFD results for wider range of applications. This, in turn, can further reduce the need for costly tests and consequently can reduce the product development cost. A promising approach to achieve this goal is using Explicit Algebraic Reynolds Stress Models (EARSM), obtained through a simplification of the full Differential Reynolds Stress Models (DRSM), and can be perceived as an extension of LEVMs by including the non-linear relation between the turbulence stress tensor, the mean-flow gradient and the turbulence scales. These models are thus less demanding than DRSM, yet capable of capturing more complex turbulence features, compared to LEVM, such as anisotropy in the normal stresses. This may be particularly important in corner flows, for instance, in the hub-blade regions or in diffusers. This work explores the application of EARSM models to a double diffuser and a high-performance centrifugal compressor stage (HPCC). The results are compared to available experimental data [1,2] showing the importance of including the anisotropy of turbulence in the model, particularly in presence of turbulent corner flows in a diffuser. Furthermore, the EARSM results are also compared to results from the commonly used SST turbulence model. The CFD comparison includes details of the flow structure in the diffuser, where the most noticeable impact from the use of EARSM turbulence models is expected.

Sign in / Sign up

Export Citation Format

Share Document