scholarly journals Reynolds Stress Model for Viscoelastic Drag-Reducing Flow Induced by Polymer Solution

Polymers ◽  
2019 ◽  
Vol 11 (10) ◽  
pp. 1659 ◽  
Author(s):  
Wang
Keyword(s):  
Polymer Solution ◽  
Reynolds Stress ◽  
Reynolds Numbers ◽  
Reynolds Stress Model ◽  
Computational Time ◽  
Stress Model ◽  
Turbulent Model ◽  
Mean Velocity ◽  
Conformation Tensor ◽  
Computational Ability

Viscoelasticity drag-reducing flow by polymer solution can reduce pumping energy of pipe flow significantly. One of the simulation manners is direct numerical simulation (DNS). However, the computational time is too long to accept in engineering. Turbulent model is a powerful tool to solve engineering problems because of its fast computational ability. However, its precision is usually low. To solve this problem, we introduce DNS to provide accurate data to construct a high-precision turbulent model. A Reynolds stress model for viscoelastic polymer drag-reducing flow is established. The rheological behavior of the drag-reducing flow is described by the Giesekus constitutive Equation. Compared with the DNS data, mean velocity, mean conformation tensor, drag reduction, and stresses are predicted accurately in low Reynolds numbers and Weissenberg numbers but worsen as the two numbers increase. The computational time of the Reynolds stress model (RSM) is only 1/120,960 of DNS, showing the advantage of computational speed.

Modeling of Cube Array Roughness: RANS, LES, and DNS

2021 ◽  
Author(s):  
Samuel Altland ◽  
Haosen H. A. Xu ◽  
Xiang I. A. Yang ◽  
Robert Kunz
Keyword(s):  
Reynolds Stress ◽  
Significant Degree ◽  
Reynolds Stress Model ◽  
Stress Model ◽  
Mean Velocity ◽  
Flow Features ◽  
Drag Partition ◽  
Flow Phenomena ◽  
Rans Models ◽  
Packing Densities

Abstract Flow over arrays of cubes is an extensively studied model problem for rough wall turbulent boundary layers. While considerable research has been performed in computationally investigating these topologies using DNS and LES, the ability of sublayer-resolved RANS to predict the bulk flow phenomena of these systems is relatively unexplored, especially at low and high packing densities. Here, RANS simulations are conducted on six different packing densities of cubes in aligned and staggered configurations. The packing densities investigated span from what would classically be defined as isolated, up to those in the d-type roughness regime, filling in the gap in the present literature. Three different sublayer-resolved turbulence closure models were tested for each case; a low Reynolds number k-ε model, the Menter k-ω SST model, and a full Reynolds stress model. Comparisons of the velocity fields, secondary flow features, and drag coefficients are made between the RANS results and existing LES and DNS results. There is a significant degree of variability in the performance of the various RANS models across all comparison metrics. However, the Reynolds stress model demonstrated the best accuracy in terms of the mean velocity profile as well as drag partition across the range of packing densities.

Simulations of Channel Flows With Effects of Spanwise Rotation or Wall Injection Using a Reynolds Stress Model

2000 ◽  
Vol 123 (1) ◽  
pp. 2-10 ◽  
Author(s):  
Bruno Chaouat
Keyword(s):  
Experimental Data ◽  
Reynolds Stress ◽  
Reynolds Stress Model ◽  
Channel Flows ◽  
Fluid Injection ◽  
Turbulent Regime ◽  
Stress Model ◽  
Mean Velocity ◽  
Wall Injection ◽  
Spanwise Rotation

Simulations of channel flows with effects of spanwise rotation and wall injection are performed using a Reynolds stress model. In this work, the turbulent model is extended for compressible flows and modified for rotation and permeable walls with fluid injection. Comparisons with direct numerical simulations or experimental data are discussed in detail for each simulation. For rotating channel flows, the second-order turbulence model yields an asymmetric mean velocity profile as well as turbulent stresses quite close to DNS data. Effects of spanwise rotation near the cyclonic and anticyclonic walls are well observed. For the channel flow with fluid injection through a porous wall, different flow developments from laminar to turbulent regime are reproduced. The Reynolds stress model predicts the mean velocity profiles, the transition process and the turbulent stresses in good agreement with the experimental data. Effects of turbulence in the injected fluid are also investigated.

Flow-Field Prediction in Submerged and Confined Jet Impingement Using the Reynolds Stress Model

1999 ◽  
Vol 121 (4) ◽  
pp. 255-262 ◽  
Author(s):  
G. K. Morris ◽  
S. V. Garimella ◽  
J. A. Fitzgerald
Keyword(s):  
Reynolds Number ◽  
Flow Field ◽  
Reynolds Stress ◽  
Reynolds Numbers ◽  
Reynolds Stress Model ◽  
Nozzle Diameter ◽  
Stress Model ◽  
Target Plate ◽  
Plate Spacing ◽  
Numerical Predictions

The flow field of a normally impinging, axisymmetric, confined and submerged liquid jet is predicted using the Reynolds Stress Model in the commercial finite-volume code FLUENT. The results are compared with experimental measurements and flow visualizations and are used to describe the position of the recirculating toroid in the outflow region which is characteristic of the confined flow field. Changes in the features of the recirculation pattern due to changes in Reynolds number, nozzle diameter, and nozzle-to-target plate spacing are documented. Results are presented for nozzle diameters of 3.18 and 6.35 mm, at jet Reynolds numbers in the range of 2000 to 23,000, and nozzle-to-target plate spacings of 2, 3, and 4 jet diameters. Up to three interacting vortical structures are predicted in the confinement region at the smaller Reynolds numbers. The center of the primary recirculation pattern moves away from the centerline of the jet with an increase in Reynolds number, nozzle diameter, and nozzle-to-target plate spacing. The computed flow patterns were found to be in very good qualitative agreement with experiments. The radial location of the center of the primary toroid was predicted to within ±40 percent and ±3 percent of the experimental position for Re = 2000–4000 and Re = 8500–23000, respectively. The magnitude of the centerline velocity of the jet after the nozzle exit was computed with an average error of 6 percent. Reasons for the differences between the numerical predictions at Re = 2000–4000 and experiments are discussed. Predictions of the flow field using the standard high-Reynolds number k-ε and renormalization group theory (RNG) k-ε models are shown to be inferior to Reynolds stress model predictions.

Prediction of strongly curved turbulent duct flows with Reynolds stress model

AIAA Journal ◽  
1997 ◽  
Vol 35 ◽  
pp. 91-98
Author(s):  
Jiang Luo ◽  
Budugur Lakshminarayana
Keyword(s):  
Reynolds Stress ◽  
Reynolds Stress Model ◽  
Stress Model

A Robust Implementation of a Reynolds Stress Model for Turbomachinery Applications in a Coupled Solver Environment

2020 ◽  
Author(s):  
David Roos Launchbury ◽  
Luca Mangani ◽  
Ernesto Casartelli ◽  
Francesco Del Citto
Keyword(s):  
Reynolds Stress ◽  
Turbulence Modeling ◽  
Computational Cost ◽  
Reynolds Stress Model ◽  
Tip Vortex ◽  
Stability And Convergence ◽  
Stress Model ◽  
Robust Implementation ◽  
Flow Phenomena ◽  
The Stability

Abstract In the industrial simulation of flow phenomena, turbulence modeling is of prime importance. Due to their low computational cost, Reynolds-averaged methods (RANS) are predominantly used for this purpose. However, eddy viscosity RANS models are often unable to adequately capture important flow physics, specifically when strongly anisotropic turbulence and vortex structures are present. In such cases the more costly 7-equation Reynolds stress models often lead to significantly better results. Unfortunately, these models are not widely used in the industry. The reason for this is not mainly the increased computational cost, but the stability and convergence issues such models usually exhibit. In this paper we present a robust implementation of a Reynolds stress model that is solved in a coupled manner, increasing stability and convergence speed significantly compared to segregated implementations. In addition, the decoupling of the velocity and Reynolds stress fields is addressed for the coupled equation formulation. A special wall function is presented that conserves the anisotropic properties of the model near the walls on coarser meshes. The presented Reynolds stress model is validated on a series of semi-academic test cases and then applied to two industrially relevant situations, namely the tip vortex of a NACA0012 profile and the Aachen Radiver radial compressor case.

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