A Comparison of Some Recent Eddy-Viscosity Turbulence Models

1996 ◽  
Vol 118 (3) ◽  
pp. 514-519 ◽  
Author(s):  
F. R. Menter
Keyword(s):  
Experimental Data ◽  
Finite Difference ◽  
Pressure Gradient ◽  
Eddy Viscosity ◽  
State Of The Art ◽  
Turbulence Models ◽  
Adverse Pressure Gradient ◽  
Gradient Flows ◽  
Grid Convergence ◽  
Sst Model

The performance of recently developed eddy-viscosity turbulence models, including the author’s SST model, is evaluated against a number of attached and separated adverse pressure gradient flows. The results are compared in detail against experimental data, as well as against the standard k-ε model. Grid convergence was established for all computations. The study involves four different, state-of-the-art finite difference (finite volume) codes.

Study of Turbulence Models in Flows Through Adverse Pressure Gradient System

2005 ◽  
Author(s):  
E. Karunakaran ◽  
V. Ganesan
Keyword(s):  
Experimental Data ◽  
Flow Field ◽  
Pressure Gradient ◽  
Numerical Simulations ◽  
Three Dimensional ◽  
Turbulence Models ◽  
Adverse Pressure Gradient ◽  
Reynolds Stress Model ◽  
Gradient System ◽  
Field Development

This paper is concerned with the study of performance of popular turbulence models used in the CFD analysis. Turbulence models considered for evaluation include the eddy viscosity models and the Reynolds stress model. The recent k-ε-v2-f model recommended for a flow with separation is also studied. Evaluation of the turbulence models in the present study focuses on a three-dimensional flow field development with adverse pressure gradient and flows that simulate wall-bounded turbulence. Numerical calculations are performed using SIMPLE based algorithm. Nowadays, decelerating flow in a diffuser is assessed by numerical simulations and the validation is done with experimental results. A comparison of the numerical results and the experimental data are presented. The main objective of the comparison is to obtain information on how well the numerical simulations representing the flow field with the standard turbulence models, are able to reproduce the experimental data.

scholarly journals Data-driven augmentation of turbulence models for adverse pressure gradient flows

2017 ◽  
Author(s):  
Anand Pratap Singh ◽  
Racheet Matai ◽  
Asitav Mishra ◽  
Karthikeyan Duraisamy ◽  
Paul A. Durbin
Keyword(s):  
Pressure Gradient ◽  
Turbulence Models ◽  
Adverse Pressure Gradient ◽  
Data Driven ◽  
Gradient Flows

scholarly journals Impact of Turbulence Models on the Air Flow in a Confined Rectangular Space

2020 ◽  
pp. 46-53
Author(s):  
Jakub Mularski ◽  
Amit Arora ◽  
Muhammad Azam Saeed ◽  
Łukasz Niedźwiecki ◽  
Samrand Saeidi
Keyword(s):  
Experimental Data ◽  
Air Flow ◽  
Turbulence Models ◽  
The Other ◽  
Experimental Measurements ◽  
Wall Region ◽  
Sst Model ◽  
Computational Fluid Dynamics Cfd ◽  
The Impact ◽  
The Given

The paper regards the impact of four different turbulence models on the air flow pattern in a confined rectangular space. The following approaches are analyzed. The Baseline (BSL) Reynolds model, the Speziale-Sarkar-Gatzki (SSG) Reynolds model, the Menter's shear-stress transport (SST) model and the basic k-ε model. Computational fluid dynamics (CFD) results are compared with the experimental measurements in four different planes. The Reynolds number for the given conditions is equal to 5000. The k-ε model yielded the most accurate results with regard to the experimental data but its reliability decreased near the wall region. With respect to the other models, it was also found that the k-ε approach generated the least circulating flow.

Characterizing developing adverse pressure gradient flows subject to surface roughness

2009 ◽  
Vol 48 (4) ◽  
pp. 663-677 ◽  
Author(s):  
Brian Brzek ◽  
Donald Chao ◽  
Özden Turan ◽  
Luciano Castillo
Keyword(s):  
Surface Roughness ◽  
Pressure Gradient ◽  
Adverse Pressure Gradient ◽  
Gradient Flows

Structural Parameters and Prediction of Adverse Pressure Gradient Turbulent Flows: An Improved k-ε Model

1995 ◽  
Vol 117 (3) ◽  
pp. 424-432 ◽  
Author(s):  
G. Chukkapalli ◽  
O¨. F. Turan
Keyword(s):  
Pressure Gradient ◽  
Turbulent Flows ◽  
Reynolds Stress ◽  
Eddy Viscosity ◽  
Structural Parameters ◽  
Adverse Pressure Gradient ◽  
Reynolds Stress Model ◽  
Model Representation ◽  
Stress Model ◽  
Kinetic Diffusion

A modified k-ε model is proposed to predict complex, adverse pressure gradient, turbulent diffuser flows. The need for an eddy viscosity is eliminated by using three structural parameters. A fuller treatment of the rate of kinetic diffusion terms is incorporated with a Reynolds stress model representation. A thorough evaluation is given of the three structural parameters in three decreasing and one increasing adverse pressure gradient diffuser flows leading to a three-layer representation. The results indicate the need for better modeling of the ε-equation.

Turbulence modeling effects on the aerodynamic characterizations of a NASCAR Generation 6 racecar subject to yaw and pitch changes

2019 ◽  
Vol 233 (14) ◽  
pp. 3600-3620 ◽  
Author(s):  
Chen Fu ◽  
C Patrick Bounds ◽  
Christian Selent ◽  
Mesbah Uddin
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Shear Stress ◽  
Eddy Viscosity ◽  
Turbulence Models ◽  
Flow Region ◽  
Adverse Pressure Gradient ◽  
Navier Stokes ◽  
Shear Stress Transport ◽  
Reynolds Averaged Navier Stokes

The characterization of a racecar’s aerodynamic behavior at various yaw and pitch configurations has always been an integral part of its on-track performance evaluation in terms of lap time predictions. Although computational fluid dynamics has emerged as the ubiquitous tool in motorsports industry, a clarity is still lacking about the prediction veracity dependence on the choice of turbulence models, which is central to the prediction variability and unreliability for the Reynolds Averaged Navier–Stokes simulations, which is by far the most widely used computational fluid dynamics methodology in this industry. Subsequently, this paper presents a comprehensive assessment of three commonly used eddy viscosity turbulence models, namely, the realizable [Formula: see text] (RKE), Abe–Kondoh–Nagano [Formula: see text], and shear stress transport [Formula: see text], in predicting the aerodynamic characteristics of a full-scale NASCAR Monster Energy Cup racecar under various yaw and pitch configurations, which was never been explored before. The simulations are conducted using the steady Reynolds Averaged Navier–Stokes approach with unstructured trimmer cells. The tested yaw and pitch configurations were chosen in consultation with the race teams such that they reflect true representations of the racecar orientations during cornering, braking, and accelerating scenarios. The study reiterated that the prediction discrepancies between the turbulence models are mainly due to the differences in the predictions of flow recirculation and separation, caused by the individual model’s effectiveness in capturing the evolution of adverse pressure gradient flows, and predicting the onset of separation and subsequent reattachment (if there be any). This paper showed that the prediction discrepancies are linked to the computation of the turbulent eddy viscosity in the separated flow region, and using flow-visualizations identified the areas on the car body which are critical to this analysis. In terms of racecar aerodynamic performance parameter predictions, it can be reasonably argued that, excluding the prediction of the %Front prediction, shear stress transport is the best choice between the three tested models for stock-car type racecar Reynolds Averaged Navier–Stokes computational fluid dynamics simulations as it is the only model that predicted directionally correct changes of all aerodynamic parameters as the racecar is either yawed from the 0° to 3° or pitched from a high splitter-ground clearance to a low one. Furthermore, the magnitude of the shear stress transport predicted delta force coefficients also agreed reasonably well with test results.

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