Reynolds Stress Transport Equation Model Research Articles

Prediction of Polymer Drag-Reduction Turbulent Channel Flow with a Reynolds Stress Transport Equation Model

Prediction of Polymer Drag-Reduction Turbulent Channel Flow with a Reynolds Stress Transport Equation Model

Implicit Algebraic Heat Flux Model Linked up with Elliptic Blending Reynolds Stress Transport Equation Model

Implicit Algebraic Heat Flux Model Linked up with Elliptic Blending Reynolds Stress Transport Equation Model

Numerical simulation of strongly swirling turbulent flows in a liquid-liquid hydrocyclone using the Reynolds stress transport equation model

The Reynolds stress transport equation model (DSM) is used to predict the strongly swirling turbulent flows in a liquid-liquid hydrocyclone, and the predictions are compared with LDV measurements. Predictions properly give the flow behavior observed in experiments, such as the Rankinevortex structure and double peaks near the inlet region in tangential velocity profile, the downward flow near the wall and upward flow near the core in axial velocity profiles. In the inlet or upstream region of the hydrocyclone, the reverse flow near the axis is well predicted, but in the region with smaller cone angle and cylindrical section, there are some discrepancies between the model predictions and the LDV measurements. Predictions show that the pressure is small in the near-axis region and increases to the maximum near the wall. Both predictions and measurements indicate that the turbulence in hydrocyclones is inhomogeneous and anisotropic.

Calculation of Fully-Developed Turbulent Flow in Rectangular Ducts With Nonuniform Wall Roughness

The predictive capabilities of four transport-type turbulence models are analyzed by comparing predictions with experimental data for fully-developed flow in (1) a rectangular duct with a step change in roughness on one wall (Case 1), and (2) a square duct with one rib-roughened wall (Case 2). The models include the Demuren-Rodi (DR) k-ε model, the Sugiyama et al. (S) k-ε model, the Launder-Li (LL) Reynolds stress transport equation model, and the differential stress (DS) model proposed recently by the authors. For the first flow situation (Case 1), the results show that the DS model yields improved agreement between predicted and measured primary and secondary mean velocity distributions in comparison to the DR and LL models. For the second flow situation (Case 2), the DS model is superior to the DR and S models for predicting experimentally observed mean velocity, turbulence kinetic energy, and Reynolds stress anisotropy behavior, especially in the vicinity of a corner formed by the juncture of adjacent smooth and rough walls. The results are analyzed in order to explain why the DR model leads to the formation of a spurious secondary flow cell near this corner that is not present in the experimental flow.

Some developments in computational modeling of turbulent flows

In this paper, some recent developments of two turbulence closure schemes at ICOMP, NASA Lewis will be discussed. One is the Reynolds-stress algebraic equation model and the other is the Reynolds-stress transport equation model. Various model constraints required by the rapid distortion theory, the invariant theory and the realizability principle, etc. will be described in the model development. The models discussed are for high-turbulent Reynolds number flows, so that the near-wall turbulence and the low-Reynolds-number turbulence are not discussed here.

Predictions of axisymmetric and two-dimensional impinging turbulent jets

Abstract The k − e turbulence model and a version of a second-moment closure, modified to include the effect of pressure reflections from a solid surface, have been used as the basis of predictions of the flow that results from the orthogonal impingement of circular and two-dimensional (2-D) jets on a flat surface. Comparison of model predictions has been made with velocity measurements obtained in the stagnation and wall jet regions of the impinging flows. Results, in general, confirm the superiority of the Reynolds stress transport equation model for predicting mean and fluctuating velocities within the latter regions of such flows. In particular, modifications to the second-moment closure to account for the influence of the surface in distorting the fluctuating pressure field away from the wall successfully predict the damping of normal-to-wall velocity fluctuations throughout the impinging flows. In contrast, results derived from the eddy-viscosity-based approach do not, in general, accurately reproduce experimental observations.

A Calculation Method for Developing Turbulent Flow in Rectangular Ducts of Arbitrary Aspect Ratio

This paper describes a full Reynolds stress transport equation model for predicting developing turbulent flow in rectangular ducts. The pressure-strain component of the model is based on a modified form of the Launder, Reece and Rodi pressure-strain model and the use of a linear wall damping function. Predictions based on this model are compared with predictions referred to high Reynolds number and low Reynolds number k–ε transport equation models and with experimental data taken in square and rectangular ducts. The results indicate that the proposed model yields improved predictions of primary flow development and Reynolds stress behavior in a square duct. The proposed model also yields Reynolds stress anisotropy and secondary flow levels that are compatible and agree well with experiment, without recourse to a quadratic damping function to model near-wall pressure-strain behavior.

Calculation of Confined Swirling Flows With a Second Moment Closure

A Reynolds stress transport equation model and the k–ε turbulence model have been applied to the calculation of a confined, strongly swirling flow. A comparison of the results with measurement shows clearly the superiority of the transport equation model. It reproduces the major features of the flow including the strong stabilizing effects of the swirl on the shear stresses and the calculated axial and circumferential components of mean velocity are in reasonable agreement with measured profiles. The corresponding normal stresses are, however, overpredicted but previously suggested modifications to the ε-equation to account for rotation did not bring any improvement. The k–ε model does not contain any mechanism to describe the stabilizing effects of swirling motion and as a consequence it performs poorly; large discrepancies exist between the measured and calculated mean velocity field.