Separated Flow Prediction Around a 6:1 Prolate Spheroid Using Reynolds Stress Models

2015 ◽  
pp. 39-60 ◽  
Author(s):  
Yair Mor-Yossef
Keyword(s):  
Reynolds Stress ◽  
Separated Flow ◽  
Prolate Spheroid ◽  
Flow Prediction ◽  
Stress Models

Separated flow surface pressure fluctuations and pressure-velocity correlations on prolate spheroid

AIAA Journal ◽  
2000 ◽  
Vol 38 ◽  
pp. 266-274
Author(s):  
Michael C. Goody ◽  
Roger L. Simpson ◽  
Christopher J. Chesnakas
Keyword(s):  
Surface Pressure ◽  
Pressure Fluctuations ◽  
Separated Flow ◽  
Prolate Spheroid ◽  
Flow Surface

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.

Sign in / Sign up

Export Citation Format

Share Document