Effects of Turbulent Reynolds Number on Turbulent Scalar Flux Modeling in Premixed Flames Using Reynolds-Averaged Navier–Stokes Simulations

2015 ◽  
Vol 67 (11) ◽  
pp. 1187-1207 ◽  
Author(s):  
Nilanjan Chakraborty ◽  
R. S. Cant
Keyword(s):  
Reynolds Number ◽  
Premixed Flames ◽  
Navier Stokes ◽  
Scalar Flux ◽  
Turbulent Reynolds Number ◽  
Reynolds Averaged Navier Stokes

Numerical investigation of vortex-induced vibration of an elastically mounted circular cylinder with One-degree of freedom at high Reynolds number using different turbulent models

2018 ◽  
Vol 233 (2) ◽  
pp. 443-453 ◽  
Author(s):  
Niaz Bahadur Khan ◽  
Zainah Ibrahim
Keyword(s):  
Reynolds Number ◽  
Flow Direction ◽  
Cross Flow ◽  
Navier Stokes ◽  
Two Dimensional ◽  
Detached Eddy Simulation ◽  
Eddy Simulation ◽  
Flow Around Cylinder ◽  
Reynolds Averaged Navier Stokes ◽  
Turbulent Models

This study presents numerical investigation for flow around cylinder at Reynolds number = 104 using different turbulent models. Numerical simulations have been conducted for fixed cylinder case at Reynolds number = 104 and for cylinder free to oscillate in cross-flow direction, at Reynolds number O (104), mass–damping ratio = 0.011 and range of frequency ratio wt = 0.4–1.4 using two-dimensional Reynolds-averaged Navier–Stokes equations. In the literature, the study has been conducted using detached eddy simulation, large eddy simulation and direct numerical simulation which are comparatively expensive in terms of computational cost. This study utilizes the Reynolds-averaged Navier–Stokes shear stress transport k-ω and realizable k-ε models to investigate the flow around fixed cylinder and flow around cylinder constrained to oscillate in cross-flow direction only. Hydrodynamic coefficients, vortex mode shape and maximum amplitude ( Ay/ D) extracted from this study are compared with detached eddy simulation, large eddy simulation and direct numerical simulation results. Results obtained using two-dimensional Reynolds-averaged Navier–Stokes shear stress transport k-ω model are encouraging, while realizable k-ε model is unable to capture the entire response branches. In addition, broad range of “lock-in” region is observed due to delay in capturing the transition from upper to lower branch during two-dimensional realizable k-ε analyses.

Flow and Heat Transfer in an Internally Ribbed Duct With Rotation: An Assessment of Large Eddy Simulations and Unsteady Reynolds-Averaged Navier-Stokes Simulations

2004 ◽  
Vol 127 (2) ◽  
pp. 306-320 ◽  
Author(s):  
A. K. Saha ◽  
Sumanta Acharya
Keyword(s):  
Heat Transfer ◽  
Reynolds Number ◽  
Nusselt Number ◽  
Density Ratio ◽  
Large Eddy Simulations ◽  
Navier Stokes ◽  
Flow And Heat Transfer ◽  
Number Ratio ◽  
Large Eddy ◽  
Reynolds Averaged Navier Stokes

Large eddy simulations (LES) and unsteady Reynolds averaged Navier-Stokes (URANS) simulations have been performed for flow and heat transfer in a rotating ribbed duct. The ribs are oriented normal to the flow and arranged in a staggered configuration on the leading and trailing surfaces. The LES results are based on a higher-order accurate finite difference scheme with a dynamic Smagorinsky model for the subgrid stresses. The URANS procedure utilizes a two equation k-ε model for the turbulent stresses. Both Coriolis and centrifugal buoyancy effects are included in the simulations. The URANS computations have been carried out for a wide range of Reynolds number (Re=12,500-100,000), rotation number (Ro=0-0.5) and density ratio (Δρ∕ρ=0-0.5), while LES results are reported for a single Reynolds number of 12,500 without and with rotation (Ro=0.12,Δρ∕ρ=0.13). Comparison is made between the LES and URANS results, and the effects of various parameters on the flow field and surface heat transfer are explored. The LES results clearly reflect the importance of coherent structures in the flow, and the unsteady dynamics associated with these structures. The heat transfer results from both LES and URANS are found to be in reasonable agreement with measurements. LES is found to give higher heat transfer predictions (5–10% higher) than URANS. The Nusselt number ratio (Nu∕Nu0) is found to decrease with increasing Reynolds number on all walls, while they increase with the density ratio along the leading and trailing walls. The Nusselt number ratio on the trailing and sidewalls also increases with rotation. However, the leading wall Nusselt number ratio shows an initial decrease with rotation (till Ro=0.12) due to the stabilizing effect of rotation on the leading wall. However, beyond Ro=0.12, the Nusselt number ratio increases with rotation due to the importance of centrifugal-buoyancy at high rotation.

Machine learning based Reynolds averaged simulation of backward-facing step flows at different Reynolds numbers

2021 ◽  
pp. 2150430
Author(s):  
Junjie Wu ◽  
Jiahua Li ◽  
Xiang Qiu ◽  
Xilin Xie ◽  
Yulu Liu
Keyword(s):  
Machine Learning ◽  
Reynolds Number ◽  
Turbulence Model ◽  
Surrogate Model ◽  
Stokes Equations ◽  
Reynolds Numbers ◽  
Navier Stokes ◽  
Backward Facing Step ◽  
Simulation Accuracy ◽  
Reynolds Averaged Navier Stokes

To address the closure problem of Reynolds-averaged Navier–Stokes in numerical simulations of turbulence, the method of solving Reynolds-averaged Navier–Stokes equations based on artificial neural network is introduced in this paper. We establish the nonlinear mapping relationship between the average flow field and the steady-state eddy viscosity field. The machine learning (ML) surrogate model for the shear stress transport turbulence model is constructed. The solution process of replacing the original turbulence model equations with the predicted field variables is realized by coupling the ML algorithm with the CFD solver. The classical backward facing step problem is selected in our study to verify the simulation accuracy of the surrogate model. The comparative analysis is carried out on the six backward facing step flows simulations at different Reynolds numbers. The results of simulations show that the testing flows with the Reynolds numbers closest training datasets Reynolds numbers can obtain the best simulation accuracy. Then for the Reynolds number that is lower than the training datasets, the simulation accuracy will decrease as the Reynolds number decreases. On the contrary, the simulation accuracy of the test flow will increase as the Reynolds number increases. These results indicate the feasibility of the ML surrogate model to simulate at higher Reynolds number. It shows the great potential of applying ML algorithms to Reynolds-averaged Navier–Stokes simulation (RANS) turbulence model and also provides a new idea for industrial simulations of turbulent flows.

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