Performance Of Turbulence Models Research Articles

Parallel implementation and performance of super-resolution generative adversarial network turbulence models for large-eddy simulation

Super-resolution (SR) generative adversarial networks (GANs) are promising for turbulence closure in large-eddy simulation (LES) due to their ability to accurately reconstruct high-resolution data from low-resolution fields. Current model training and inference strategies are not sufficiently mature for large-scale, distributed calculations due to the computational demands and often unstable training of SR-GANs, which limits the exploration of improved model structures, training strategies, and loss-function definitions. Integrating SR-GANs into LES solvers for inference-coupled simulations is also necessary to assess their a posteriori accuracy, stability, and cost. We investigate parallelization strategies for SR-GAN training and inference-coupled LES, focusing on computational performance and reconstruction accuracy. We examine distributed data-parallel training strategies for hybrid CPU–GPU node architectures and the associated influence of low-/high-resolution subbox size, global batch size, and discriminator accuracy. Accurate predictions require training subboxes that are sufficiently large relative to the Kolmogorov length scale. Care should be placed on the coupled effect of training batch size, learning rate, number of training subboxes, and discriminator’s learning capabilities. We introduce a data-parallel SR-GAN training and inference library for heterogeneous architectures that enables exchange between the LES solver and SR-GAN inference at runtime. We investigate the predictive accuracy and computational performance of this arrangement with particular focus on the overlap (halo) size required for accurate SR reconstruction. Similarly, a posteriori parallel scaling for efficient inference-coupled LES is constrained by the SR subdomain size, GPU utilization, and reconstruction accuracy. Based on these findings, we establish guidelines and best practices to optimize resource utilization and parallel acceleration of SR-GAN turbulence model training and inference-coupled LES calculations while maintaining predictive accuracy.

Comparative study of turbulence models in CFD for transonic flow over the ONERA M6 wing

Transonic flight presents significant challenges due to complex phenomena such as shock waves and turbulent boundary layer interactions, often leading to flow separation and instabilities. This study aims to evaluate the performance of various turbulence models in predicting these phenomena using Computational Fluid Dynamics (CFD) with ANSYS-Fluent 2020R1. The ONERA M6 wing model is analyzed under specific flow conditions (angle of attack, α = 3.06°, Mach number, M∞ = 0.8395, and Reynolds number, Re = 11.72 × 10^6) to assess five turbulence models: Spalart–Allmaras, k-ε Standard, k-ε Realizable, k-ω Standard, and k-ω SST. Results from the simulations closely matched experimental data, with all models demonstrating a margin of error within 5% compared to NASA’s CFD benchmarks. Notably, the Spalart–Allmaras and k-ω SST models showed superior accuracy in predicting shock formation and pressure distribution, highlighting their potential for enhanced predictive capabilities in aerodynamic analysis. This study demonstrates the effectiveness of ANSYS-Fluent in aerodynamic performance evaluation and suggests pathways for advancing aircraft design efficiency and safety through improved turbulence modeling.

Improving turbulence modeling for gas turbine blades: A novel approach to address flow transition and stagnation point anomalies

Accurate prediction of temperature and Heat Transfer Coefficient (HTC) distributions over gas turbine blades is crucial for the design process and life assessment of these components. Numerical studies of flow over gas turbine blades face significant challenges in accurately simulating two complex phenomena: (1) the transition of flow from laminar to turbulent, and (2) stagnation point flow at the leading edge. Many turbulence models tend to overpredict the temperature on turbine blades, leading to incorrect identification of hot-spot regions and, consequently, erroneous estimations of blade life. This paper investigates the performance of various turbulence models in simulating flow and heat transfer over gas turbine vanes. The study includes three full turbulence models, i.e., Spalart-Allmaras (SA), Shear Stress Transport k−ω (SST-kw), and v2−f (V2F), as well as two transitional models, i.e., Transition SST (Trans-SST) and k−kL−ω (k-kl-w). Simulation results indicate that the v2−f, Trans-SST, and k−kL−ω models can detect flow transition. However, the transition length and onset location predicted by the Trans-SST and k−kL−ω models do not align with experimental data. Conversely, the v2−f model suffers from over-predictions at the leading edge due to stagnation point anomaly. To address these issues and due to capacities of the V2F model, this study proposes two modifications to enhance the performance of the V2F model. First, the production term of turbulent kinetic energy is redefined to mitigate the stagnation point anomaly. Second, the model is recalibrated to improve the prediction of flow transition. The new model, named the Production Modified V2F (PMV2F) model, shows promising results in predicting temperature and heat transfer coefficients.

Heat transfer characteristics of a turbulent swirl jet issuing from a circular nozzle

Experimental and numerical studies on the flow field and cooling performance of a swirling jet, impinging on a flat surface is presented. A new nozzle that resembles the swirl injector of a liquid propellant rocket engine is used. This study considers different parameters including swirl number(S), Reynolds number(Re), normalised orifice to target spacing (Z/D) and confinement. The performance of various RANS and LES turbulence models is assessed using the data obtained from present experimental studies and that reported in literature. RNG k-ϵ turbulent model is successful in predicting heat transfer in non swirling flow. In the case of swirling flow, LES WALEM(Wall Adapting Local Eddy viscosity Model) predicts the heat transfer better than the other models. The performance of Swirling Impinging Jets(SIJ) and Cylindrical Impinging Jets (CIJ) is compared. At higher Z/D, introduction of swirl results in reduction in heat transfer and this is because of the increased spreading of the jet and reduction in velocity of the jet impinging on the target. Better performance is achieved at lower Z/D when a cylindrical jet is introduced with a small amount of swirl. For Z/D = 2 and S = 0.2, the peak Nu increases by 10 % and average Nu in the stagnation region increases by 6 % in comparison to CIJ. The position of the peak Nu moves away from the axis of the jet and there is better uniformity in Nu in the stagnation region. For Z/D = 2 swirling flow creates secondary peak(which usually occurs at high Re)even at low Re. At very low Z/D, top wall confinement causes increase in both peak heat transfer and average heat transfer in the stagnation region.

Assessing Turbulence Model Performance in OpenFOAM for Natural Convection Simulations

Abstract Natural convection processes are pivotal in various engineering applications, necessitating accurate and reliable computational fluid dynamics (CFD) models for their simulation. This study evaluates the efficacy of different turbulence models implemented in OpenFOAM for a natural convection scenario, aiming to identify the most suitable model for capturing complex thermal and fluid dynamic behaviors. We compared several turbulence models, including the k − ε, k − ω, and k − ω SST, using a benchmark thermal convection case. Our methodology involved setting up the simulations to reflect realistic thermal gradients and boundary conditions, followed by a rigorous analysis of temperature distribution, vertical velocity profiles, and computational efficiency. The results indicate that the k − ω SST achieved the lowest averaged root mean square error (RMSE) values for temperature (0.076) and vertical velocity (0.03) while also requiring the fewest convergence iterations (314) compared to k − ε (411) and k − ω (1474). These findings demonstrate that the k − ω SST model is a suitable compromise between accuracy and computational cost for engineering applications. This study underscores the importance of selecting an appropriate turbulence model in OpenFOAM to enhance the accuracy of natural convection simulations, which can significantly influence design and safety considerations in engineering systems.

Evaluation of turbulence models for the prediction of flow properties in vegetated channels

The performance of turbulence models was investigated to predict the flow and turbulence features of the vegetated channel using computational fluid dynamics (CFD). The Ansys Fluent, CFD software was implemented for the numerical studies. The flow was three-dimensional, incompressible, steady, and turbulent. Ten turbulence models, provided by Ansys Fluent, were implemented for the comparative study. The numerical model was validated against an experimental study conducted in the literature. The numerical studies show that the Renormalization group k–ε model is the most successful model for predicting the flow characteristics of the vegetated channel with a Root Mean Square Error (RMSE) value of 0.2752. At the same time, the Reynolds Stress Model gives the least successful predictive performance, indicated by an RMSE value of 0.4302. Moreover, the Spalart–Allmaras (S–A) model offers the shortest computation time with a value of 6652.393 s, whereas the Shear Stress Transport k–ω model proves to be the most time-consuming with a value of 11 952.219 s. The velocity of water flow in a channel is not uniform as it is slower at the surface of leaves and faster in the free zones. The maximum velocity is observed in the middle section of the channel, below the leaf, and between the roots with the value of u = 0.1158 m/s. Furthermore, the characteristics of turbulence in a channel are influenced by several factors such as channel geometry, flow velocity, and vegetation distribution. As a result, the presence of vegetation in a channel affects the flow and turbulence characteristics of the water significantly.

Lessons Learnt from the Simulations of Aero-Engine Ground Vortex

With the startup of the aero-engine, the ground vortex is formed between the ground and the engine intake. The ground vortex leads to total pressure and swirl distortion, which reduces the performance of the engine. The inhalation of the dust and debris through a ground vortex can erode the fan blade, block the seals and degrade turbine cooling performance. As the diameter of the modern fan blade becomes larger, the clearance between the intake lip and the ground surface is smaller, which enhances the strength of the ground vortex. Though considerable numerical studies have been conducted with the predictions of the ground vortex, it is noted that the accurate simulation of the ground vortex is still a tough task. This paper presents authors’ simulation work of the ground vortex into an intake model with different crosswind speeds. This paper tackles the challenge with a parametric study to provide useful guidelines on how to obtain a good match with the experimental data. The influence of the mesh density, performance of different turbulence models and how the boundary layer thickness affects the prediction results are conducted and analysed. The detailed structure of the flow field with ground vortex is presented, which can shed light on the experimental observations. A number of suggestions are presented that can pave the road to the accurate flow field simulations with strong vorticities.

Continuous Eddy Simulation (CES) of Transonic Shock-Induced Flow Separation

Reynolds-averaged Navier–Stokes (RANS), large eddy simulation (LES), and hybrid RANS-LES, first of all wall-modeled LES (WMLES) and detached eddy simulation (DES) methods, are regularly applied for wall-bounded turbulent flow simulations. Their characteristic advantages and disadvantages are well known: significant challenges arise from simulation performance, computational cost, and functionality issues. This paper describes the application of a new simulation approach: continuous eddy simulation (CES). CES is based on exact mathematics, and it is a minimal error method. Its functionality is different from currently applied simulation concepts. Knowledge of the actual amount of flow resolution enables the model to properly adjust to simulations by increasing or decreasing its contribution. The flow considered is a high Reynolds number complex flow, the Bachalo–Johnson axisymmetric transonic bump flow, which is often applied to evaluate the performance of turbulence models. A thorough analysis of simulation performance, computational cost, and functionality features of the CES model applied is presented in comparison with corresponding features of RANS, DES, WMLES, and wall-resolved LES (WRLES). We conclude that CES performs better than RANS, DES, WMLES, and even WRLES at a little fraction of computational cost applied for the latter methods. CES is independent of usual functionality requirements of other methods, which offers relevant additional advantages.

Comparison of RANS and LES turbulent flow models in a real stenosis

This study focuses on the performance of various turbulence models in predicting hemodynamic variables within a patient-specific geometry of the Brachiocephalic trunk exhibiting a severe stenosis. Numerical simulations employing Reynolds-averaged Navier-Stokes (RANS) and Large Eddy Simulation (LES) models were conducted, comparing four RANS with two LES models. Results indicated significant differences in turbulent structures between RANS and LES models, with the k-ω RANS model closely approximating LES throughout the cardiac cycle. The velocity and Turbulent Kinetic Energy (TKE) results were consistent in the stenosis region due to the proximity to the inlet and plug flow conditions. However, notable disparities were observed in bifurcation and outlet regions. Time-Averaged Wall Shear Stress (TAWSS) comparisons revealed that the k-ω-based models provided closer agreement with LES, particularly in atherosclerosis-prone areas. The study highlights the limitations and strengths of each turbulence model, emphasizing the importance of model selection in simulating complex cardiovascular conditions. While RANS models demonstrated computational efficiency, their accuracy varied across regions. The effectiveness of the k-ω model in capturing the intricacies of such a complex flow field suggests its potential as a reliable and accurate tool for simulating physiological conditions with strong curvature and advanced stenosis.

Comparison of Eddy Viscosity Models for High Turbulence Nozzle Guide Vane Flows

Abstract In this paper we investigate the performance of eddy viscosity turbulence models for high-pressure nozzle guide vane (NGV) flows with engine-realistic turbulence. Using metrics such as integrated kinetic energy (KE) loss and mixing rate, we compare simulation results using turbulence models with high-fidelity experimental data from fully-cooled NGVs, operating at engine-matched conditions of Reynolds number and Mach. For the widely-used k–ω shear stress transport (SST) model, the aerodynamic and thermal wakes of the NGVs were undermixed. We compare simulation results with those using other common turbulence models including the standard k–ω, baseline k–ω, Spalart–Allmaras, and baseline explicit algebraic Reynolds stress model. Alternative turbulence models formulations are explored, with an emphasis on modifications to the implementation of the shear stress limiter. We also investigate the sensitivity of models to the specified inlet turbulence intensity. We demonstrate that predictions of integrated KE loss, as well as the decay trends of the aerodynamic and thermal wakes are a closer match to experimental data for the baseline algebraic Reynolds stress model than for other turbulence models in more common use for NGV predictions.

Turbulence models performance to predict fluid mechanics and heat transfer characteristics of fluids flow in micro-scale channels

The predictability of the mathematical models on the details and the phenomenology for microscale flow conditions is an open topic in the literature. In this sense, this work evaluates the capacity of the following turbulence models: standard k − ε , RNG k − ε , k − ω standard, k − ω SST , Realizable k − ε , and Low-Re k − ε to predict the fluid mechanics and heat transfer characteristics of low Global Warming Potential (GWP) fluid flow in a 1.1 mm ID microchannel. These turbulence models are evaluated for Reynolds Numbers up to 104. The numerical results for velocity profile, friction factors, and Nusselt Numbers are validated with analytical and experimental data published in previous works for R134a, R1234yf, R1234ze(E), and R600a. Parametric behaviors of pressure drop and heat transfer coefficient are presented and analyzed. The results indicate that each of the models describes the qualitative behavior of flow and heat transfer processes. On the other hand, the quantitative results indicate that the Low-Re k − ε , k − ω , and k − ω SST models demonstrate an acceptable prediction of some variable’s behavior. Numerically, the Low-Re k − ε model presents an accurate prediction with the lowest mean absolute.

Experimental and numerical study on flow field characteristics of a combustion chamber with double-stage counter-rotating swirlers

To discover a good predictive turbulent model and reveal the mechanism of turbulent mixing enhancement, a new combustion chamber equipped with a double-stage counter-rotating swirler with vane angles of both stages as 45° is proposed and the flow field characteristics are explored by combining 2D-PIV experiment and Reynolds averaged numerical simulation (RANS). Firstly, the performance of various turbulence models in predicting the flow field characteristics is evaluated by comparing them with experimental results. The study concludes that the RNG k-ε turbulence model exhibits superior performance in predicting velocity distribution, recirculation zone length, and vorticity distribution. Hence, it is selected to analyze the influence of the Reynolds number (Re) of swirlers on the flow field characteristics. In this study, seven Re numbers ranged from 5425 to 54,245 were selected for analysis. Through comparison, it appears that changes in Re number have a strengthening effect on the flow field at various locations, including the recirculation zone, wall surface, and shear layer. This suggests that Re number has an influence on turbulence or boundary layer behavior. Interestingly, there seems to be a critical value for Re number of 21,698, which may be related to a competition between Re number and wall effects.

Numerical Simulation of Supersonic Turbulent Separated Flows Based on k–ω Turbulence Models with Different Compressibility Corrections

The accurate prediction of supersonic turbulent separated flows involved in aerospace vehicles is a great challenge for current numerical simulations. Based on the k–ω equations, several different compressibility corrections are incorporated in turbulence models to improve their prediction capabilities. Two benchmark test cases, namely the ramped cavity and the compression corner, are adopted for the numerical validation. Detailed comparisons between simulations and experiments are conducted to evaluate the effect of compressibility corrections on turbulence models. The computed results indicate that compressibility corrections have a significant impact on turbulence model performance. The compressibility correction, considering the effects of both dilatation dissipation and pressure dilatation, is suitable for the prediction of compressible free shear layers, but it may have a negative impact on the prediction of low-speed flows in the near-wall region due to the severe underprediction of the wall skin friction coefficient. In comparison, the compressibility correction only considering the effects of dilatation dissipation is conservative, with decreased predictability of free shear layers in supersonic flows, although it improves the predictions of the original models without corrections.

CFD simulation of pollutant dispersion using anisotropic models: Application to an urban like environment under neutral and stable atmospheric conditions

Modeling the atmospheric dispersion of pollutants emitted from different types of sources under various atmospheric conditions is an essential prerequisite for risk assessment studies and emergency preparedness. In this study, we evaluate the performance of different turbulence models and velocity-scalar correlation models implemented in the Code_Saturne. The evaluation is done with observations of four trial cases of the Mock Urban Setting Test (MUST) campaign in an urban type environment and in neutral and stable atmospheric conditions. For all of the trials studied, the CFD model with a first-order closure model (k−ɛ) predicts 61.1% of the concentrations within a factor of two of the observations, which is higher than the percentage of predicted points (58.8%) when a second-order closure model (Rij−ɛ) is used. Overall, the CFD model underestimates observed concentrations, regardless of the turbulence model used. For the trial with slightly stable conditions, the results show that the k−ɛ model combined with an algebraic SGDH model predicts 75% concentrations within a factor of two of the observations. The performance of the k−ɛ model is compared to that of the Rij−ɛ model when used with the algebraic SGDH, GGDH models and with the scalar flux transport equation (DFM model). Under slightly stable atmospheric conditions, the DFM model predicts 69% of concentrations within a factor of two of observations, showing promise for modeling under these conditions, despite its relatively high computational cost.

Comparison of various turbulence models in wind tunnels

Wind tunnel test is widely used in aviation, automobile, construction and other fields to simulate the force and flow field distribution of objects in the wind field. However, due to the existence of complex flow phenomena such as turbulence, the accuracy of the wind tunnel test is affected to a certain extent. Therefore, it is of great significance to study the performance of various turbulence models in the wind tunnel to improve the accuracy of the wind tunnel test. This study compares and analyzes the performance of turbulence models in wind tunnel experiments. Based on various turbulence models, numerical simulation methods are employed to simulate and calculate the flow field in wind tunnel experiments, and the results are compared. Through the comparison and analysis, it is found that different turbulence models exhibit different performance in simulating wind tunnel experiments. Among them, the RSM model demonstrates better accuracy and stability, without the presence of boundary layer effects. The purpose of this research is to evaluate and analyze the applicability of various turbulence models in wind tunnel experiments, provide references and guidance for flow field simulations in wind tunnel testing. However, limitations of this study lie in the constraints of the models and computational methods used, and further research and exploration are needed to address these limitations.

Economic particulate transport performance analysis of k-epsilon models in highly concentrated slurry through pipelines

The paper analyzed the economic transporting performance of k-epsilon turbulence models using the Eulerian two-fluid approach in transporting highly concentrated fine particulate slurry through horizontal pipelines using Kaushal et al. (2005) experimental data of glass beads slurry of 125 μm mean diameter for volumetric concentration ranging 30%–52% and flow velocity ranging 2–5 m/s. The primary components of economical slurry transport are Specific Energy consumption (SEC) and pressure drop; both have been examined in the performance of different κ-ϵ models and are yet to be unlighted on these parameters. The CFD model found the excellent performance of all k-epsilon models with slightly more effectiveness in the case of the Realizable κ-ϵ model. The analyzed and validated CFD model was subsequently used in examining the parameters of slurry flow, such as the secondary phase velocity, concentration distribution, and SEC analysis for the range of fine particles 125, 150, and 212 µm. The plotted contours from CFD are useful in visualizing slurry flow parameters at intermediates inlet values for slurry inlet flow velocity ranging 2–6 m/s and inlet volumetric concentration ranging 30%–55% for fine particles. Analysis revealed that the highly concentrated fine particles slurry transported at optimum volumetric concentration range of 40–45% are economical slurry transport.

Progressive augmentation of Reynolds stress tensor models for secondary flow prediction by computational fluid dynamics driven surrogate optimisation

Generalisability and the consistency of the a posteriori results are the most critical points of view regarding data-driven turbulence models. This study presents a progressive improvement of turbulence models using simulation-driven Bayesian optimisation with Kriging surrogates where the optimisation of the models is achieved by a multi-objective approach based on duct flow quantities. We aim for the augmentation of secondary-flow prediction capability in the linear eddy-viscosity model k−ω SST without violating its original performance on canonical cases e.g. channel flow. Progressively data-augmented explicit algebraic Reynolds stress models (PDA-EARSMs) for k−ω SST are obtained enabling the prediction of secondary flows that the standard model fails to predict. The new models are tested on channel flow cases guaranteeing that they preserve the successful performance of the original k−ω SST model. Subsequently, numerical verification is performed for various test cases. Regarding the generalisability of the new models, results of unseen test cases demonstrate a significant improvement in the prediction of secondary flows and streamwise velocity. These results highlight the potential of the progressive approach to enhance the performance of data-driven turbulence models for fluid flow simulation while preserving the robustness and stability of the solver.

Assessment of advanced RANS models to predict the heat transfer for supercritical fluids in vertical tubes

The extreme thermophysical property changes of fluids near the pseudocritical point lead to strong buoyancy and acceleration effects, posing challenges for turbulence modelling. This paper aims to assess the performance and applicability of various turbulence models, with the intention of providing a useful reference for quickly selecting appropriate models and for models’ further improvement. Additional Favre-averaged turbulence models were implemented through user-defined scalars in ANSYS Fluent and compared to published experimental and high-fidelity simulation data. This study found that the tested turbulence models have different sensitivities to molecular Prandtl numbers, buoyancy effects, and flow acceleration. For forced convection, all turbulence models can be applied in the case of the flow acceleration number Kv<1.37×10−8, and the ZXY model has the widest application range. None of the tested turbulence models showed generalizability under mixed convective conditions. The Lien-PSA model qualitatively reproduced the wall temperature trends for all tested cases.

On the Measure of the Heat Transfer Performance of RANS Turbulence Models in Single Round Jet Impingement

The jet impingement phenomenon is one of the processes which can enhance heat transfer. Due to its complex nature, it has been the subject of many experimental and numerical analyses in which researchers have tried to quantify and qualitatively describe it. However, the lack of crucial information regarding procedures, accuracy, geometry settings, boundary conditions, etc. makes it challenging to compare the results, validate turbulence models, and reproduce data. In this publication, the authors show a consistent and systematic numerical analysis of round and turbulent jet impingement based on RANS turbulence models. The results have been calculated for various geometrical configurations, Reynolds number values, and turbulence models, and compared with experimental and numerical data available in the literature to show their similarities and differences. It led to unique data collection, which was used in the novel quantitative analysis and helped lead to proposing the measure of the heat transfer performance of a particular turbulence model. Such a measure has not been reported so far. The measure exhibited that no turbulence model is suitable for all analyzed parameters. Quantitative comparisons enable recommendations of turbulence models for analyzed cases which have the potential to accelerate the design process of devices and could be a source of suggestions for other researchers.

Turbulence Model Effects on Vortex Interaction Prediction on a Multiswept Delta Wing

In this study, vortical flows around a generic triple-delta wing geometry were investigated at transonic speeds. The focus of this work was the investigation of vortex interaction at medium to high angles of attack. To this end, numerical simulations were performed at two different Mach numbers, M=0.5 and M=0.85. To assess the performance of different turbulence models in predicting the complex flowfield, comparisons with experimental data were performed. In addition to the integral forces and moments, surface pressures from pressure-sensitive paint and velocity field data from particle image velocimetry were used. Across all investigated test cases, the best agreement with experimental results was achieved by the Menter shear stress transport turbulence model.