On the performance of RANS turbulence models in predicting static stall over airfoils at high Reynolds numbers

Purpose This paper aims to conduct, a detailed investigation of various Reynolds averaged Navier–Stokes (RANS) models to study their performance in attached and separated flows. The turbulent flow over two airfoils, namely, National Advisory Committee for Aeronautics (NACA)-0012 and National Aeronautics and Space Administration (NASA) MS(1)-0317 with a static stall setup at a Reynolds number of 6 million, is chosen to investigate these models. The pre-stall and post-stall regions, which are in the range of angles of attack 0°–20°, are simulated. Design/methodology/approach RANS turbulence models with the Boussinesq approximation are the most commonly used cost-effective models for engineering flows. Four RANS models are considered to predict the static stall of two airfoils: Spalart–Allmaras (SA), Menter’s k – ω shear stress transport (SST), k – kL and SA-Bas Cakmakcioglu modified (BCM) transition model. All the simulations are performed on an in-house unstructured-grid compressible flow solver. Findings All the turbulence models considered predicted the lift and drag coefficients in good agreement with experimental data for both airfoils in the attached pre-stall region. For the NACA-0012 airfoil, all models except the SA-BCM over-predicted the stall angle by 2°, whereas SA-BCM failed to predict stall. For the NASA MS(1)-0317 airfoil, all models predicted the lift and drag coefficients accurately for attached flow. But the first three models showed even further delayed stall, whereas SA-BCM again did not predict stall. Originality/value The numerical results at high Re obtained from this work, especially that of the NASA MS(1)-0317, are new to the literature in the knowledge of the authors. This paper highlights the inability of RANS models to predict the stall phenomenon and suggests a need for improvement in modeling flow physics in near- and post-stall flows.

Simulation of mixing flows with different temperatures in a T-junction using OpenFOAM code

This paper aims to evaluate the frequency of velocity and temperature fluctuations in the mixing region using OpenFOAM code. Turbulent mixing of fluids at different temperatures can lead to temperature fluctuations at the pipe material. These fluctuations, or thermal striping, inducing cyclical thermal stresses and resulting thermal fatigue, may cause unexpected failure of pipe material. Therefore, an accurate characterization of temperature fluctuations is important in order to estimate the lifetime of pipe material. Thermal fatigue of the coolant circuits of nuclear power plants is one of the major issues in nuclear safety. To investigate thermal fatigue damage, the OECD/NEA-Vattenfall T-Junction Benchmark was initiated to test the ability of state-of-the-art Computational Fluid Dynamics (CFD) codes to predict the important parameters affecting high-cycle thermal fatigue in mixing tees. In this study, to simulate the standard problem described above, the OpenFOAM code is used, which is an open integrated platform for numerical simulation of problems in continuum mechanics. At the first with Salome-meca code, a computational grid was created, consisting of about 450,000 nodes, and k-eps model and RANS models were used to simulate turbulence. OpenFOAM code results were compared with the available experimental results. The results were found to be in well-agreement with the experimental results in terms of amplitude and frequency of temperature and velocity fluctuations.

How Turbulence Models Perform in Simulating Pipeflow Response to Targeted Wall-Shapes?

This study evaluates how Reynolds-Averaged-Navier-Stokes (RANS) models perform in simulating the characteristics of mean three-dimensional perturbed flows in pipes with targeted wall-shapes. Capturing such flow features using turbulence models is still challenging at high Reynolds numbers. The principal objective of this investigation is to evaluate which of the well-established RANS models can best predict the flow response and recovery characteristics in perturbed pipes at moderate and high Reynolds numbers (10000-158000). First, the flow profiles at various axial locations are compared between simulations and experiments. This is followed by assessing the well-known mean pipeflow scaling relations. The good agreement between our computationally predicted data using Standard k-epsilon model and those of experiments indicated that this model can accurately capture the pipeflow characteristics in response to introduced perturbation with smooth sinusoidal axial variations.

A curated dataset for data-driven turbulence modelling

The recent surge in machine learning augmented turbulence modelling is a promising approach for addressing the limitations of Reynolds-averaged Navier-Stokes (RANS) models. This work presents the development of the first open-source dataset, curated and structured for immediate use in machine learning augmented corrective turbulence closure modelling. The dataset features a variety of RANS simulations with matching direct numerical simulation (DNS) and large-eddy simulation (LES) data. Four turbulence models are selected to form the initial dataset: k-ε, k-ε-ϕt-f, k-ω, and k-ω SST. The dataset consists of 29 cases per turbulence model, for several parametrically sweeping reference DNS/LES cases: periodic hills, square duct, parametric bumps, converging-diverging channel, and a curved backward-facing step. At each of the 895,640 points, various RANS features with DNS/LES labels are available. The feature set includes quantities used in current state-of-the-art models, and additional fields which enable the generation of new feature sets. The dataset reduces effort required to train, test, and benchmark new corrective RANS models. The dataset is available at 10.34740/kaggle/dsv/2637500.

Mathematical Methodology and Metallurgical Application of Turbulence Modelling: A Review

This paper focusses on three main numerical methods, i.e., the Reynolds-Averaged Navier-Stokes (RANS), Large Eddy Simulation (LES), and Direct Numerical Simulation (DNS) methods. The formulation and variation of different RANS methods are evaluated. The advantage and disadvantage of RANS models to characterize turbulent flows are discussed. The progress of LES with different subgrid scale models is presented. Special attention is paid to the inflow boundary condition for LES modelling. Application and limitation of the DNS model are described. Different experimental techniques for model validation are given. The consistency between physical experimentation/modelling and industrial cases is discussed. An emphasis is placed on the model validation through physical experimentation. Subsequently, the application of a turbulence model for three specific flow problems commonly encountered in metallurgical process, i.e., bubble-induced turbulence, supersonic jet transport, and electromagnetic suppression of turbulence, is discussed. Some future perspectives for the simulation of turbulent flow are formulated.

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

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.

Study On the Applicability of URANS, LES and Hybrid LES/RANS Models for Prediction of Hydrodynamics of Cyclone Separator

Cyclone separators are an integral part of many industrial processes. A good understanding of the flow features is paramount to efficiently use them. The turbulent fluid flow characteristics are modelled using URANS, LES and hybrid LES/RANS turbulent models. The hybrid LES/RANS approaches, namely DES (Detached Eddy Simulation), DDES (Delayed Detached Eddy Simulation) and IDDES (Improved Delayed Detached Eddy Simulation) based on the k - $\omega$ SST RANS approaches are explored. The study is carried out for three different inlet velocities (v = 8, 16:1, and 32 m=s). The results from hybrid LES/RANS models are shown to be in good agreement with the experimental data available in the literature. Reduction in computational time and mesh size are the two main benefits of using hybrid LES/RANS models over the traditional LES methods. The Reynolds stresses are observed in order to understand the redistribution of turbulent energy in the flow field. The velocity profiles and vorticity quantities are explored to obtain a better understanding of the behaviour of fluid flow in cyclone separators.