scholarly journals Hybrid Turbulence Models: Recent Progresses and Further Researches

2019 ◽  
Vol 130 ◽  
pp. 01013
Author(s):  
Hariyo Priambudi Setyo Pratomo ◽  
Fandi Dwiputra Suprianto ◽  
Teng Sutrisno
Keyword(s):  
Turbulent Flows ◽  
Turbulence Modeling ◽  
A Priori ◽  
Computational Cost ◽  
Turbulence Models ◽  
Turbulence Scale ◽  
Research Activities ◽  
Daunting Task ◽  
Active Research ◽  
Inherent Nature

Turbulence simulation remains one of the active research activities in computational engineering. Along with the increase in computing power and the prime motivation of improving the accuracy of statistical turbulence modeling approaches and reducing the expensive computational cost of both direct numerical and large turbulence scale- resolving simulations, various hybrid turbulence models being capable of capturing unsteadiness in the turbulence are now accessible. Nevertheless this introduces the daunting task to select an appropriate method for different cases as one can not know a priori the inherent nature of the turbulence. It is the aim of this paper to address recent progresses and further researches within a branch of the hybrid RANS-LES models examined by the first author as simple test cases but generating complex turbulent flows are available from experimentation. In particular, failure of a seamless hybrid formulation not explicitly dependent on the grid scale is discussed. From the literature, it is practical that at least one can go on with confidence when choosing a potential hybrid model by intuitively distinguishing between strongly and weakly unstable turbulent flows.

Nonlinear Versus Linear Stress-Strain Relations in Engine Turbulence Modeling Under Swirl and Squish Flow Conditions

2008 ◽  
Vol 130 (6) ◽  
Author(s):  
Mirko Baratta ◽  
Andrea E. Catania ◽  
Stefano d’Ambrosio
Keyword(s):  
Turbulent Flows ◽  
Eddy Viscosity ◽  
Turbulence Modeling ◽  
Swirling Flow ◽  
Local Equilibrium ◽  
Computational Cost ◽  
Turbulence Models ◽  
Stress Strain ◽  
Accuracy Order ◽  
Wall Boundary Conditions

A general form of the stress-strain constitutive relation was introduced for the application of two nonlinear k-ε turbulence models, namely, the algebraic Reynolds stress model of Gatski and Speziale (1993, “On Explicit Algebraic Stress Models for Complex Turbulent Flows,” J. Fluid Mech., 254, pp. 59–78) and the cubic model of Lien et al. (1996, “Low Reynolds Number Eddy-Viscosity Modeling Based on Non-Linear Stress-Strain/Vorticity Relations,” Proceedings of Third Symposium on Engineering Turbulence Modeling and Measurements, Crete, Greece), to the numerical analysis of flow fields in a test engine with flat-piston and bowl-in-piston arrangements, under swirling and no-swirling flow motored conditions. The model capabilities in capturing turbulent flow features were compared to those of the upgraded linear RNG k-ε model, which was previously indicated as a good compromise between accuracy and computational cost. Evaluations were made on the basis of the predicted flow evolution throughout the whole engine cycle, as well as of the comparison between the numerical and experimental results. Furthermore, the effect of the stress-strain relationship on the predicted averaged turbulence quantities and anisotropy-invariant values were examined, in addition to the sensitivity of the nonlinear models to the mesh quality. Finally, prospects concerning possible improvements of turbulence eddy-viscosity models were presented. The predictions were made by a newly developed CFD code embedding various accuracy-order finite-volume discretization schemes. Modified wall boundary conditions with respect to the conventional logarithmic-function approach were used, so as to make the local equilibrium hypothesis virtually ineffective.

Review of Recent Advances in the Study of Unsteady Turbulent Internal Flows

1995 ◽  
Vol 48 (4) ◽  
pp. 189-212 ◽  
Author(s):  
G. J. Brereton ◽  
R. R. Mankbadi
Keyword(s):  
Turbulent Flows ◽  
Turbulence Modeling ◽  
Unsteady Flows ◽  
Turbulence Models ◽  
Research Area ◽  
Transition To Turbulence ◽  
Measurement Technology ◽  
Internal Flows ◽  
Recent Advances ◽  
The Many

Turbulent flow which undergoes organized temporal unsteadiness is a subject of great importance to unsteady aerodynamic and thermodynamic devices. Of the many classes of unsteady flows, those bounded by rigid smooth walls are particularly amenable to fundamental studies of unsteady turbulence and its modeling. These flows are presently being given increased attention as interest grows in the prospect of predicting non-equilibrium turbulence and because of their relevance to turbulence–acoustics interactions, in addition to their importance as unsteady flows in their own right. It is therefore timely to present a review of recent advances in this area, with particular emphasis placed on physical understanding of the turbulent processes in these flows and the development of turbulence models to predict them. A number of earlier reviews have been published on unsteady turbulent flows, which have tended to focus on specific aspects of certain flows. This review is intended to draw together, from the diverse literature on the subject, information on fundamental aspects of these flows which are relevant to improved understanding and development of predictive models. Of particular relevance are issues of instability and transition to turbulence in reciprocating flows, the robustness of coherent structures in wall-bounded flows to forced perturbations (in contrast to the relative ease of manipulation in free shear flows), unsteady scalar transport, improved measurement technology, recent contributions to target data for model testing and the quasi-steady and non-steady rapid distortion approaches to turbulence modeling in these flows. The present article aims to summarize recent contributions to this research area, with a view to consolidating comprehension of the well-known basics of these flows, and drawing attention to critical gaps in information which restrict our understanding of unsteady turbulent flows.

Sensitization of the SST Turbulence Model to Rotation and Curvature by Applying the Spalart–Shur Correction Term

2009 ◽  
Vol 131 (4) ◽  
Author(s):  
Pavel E. Smirnov ◽  
Florian R. Menter
Keyword(s):  
Turbulent Flows ◽  
Turbulence Model ◽  
Transport Model ◽  
Correction Term ◽  
Computational Cost ◽  
Turbulence Models ◽  
Reynolds Stress Transport Model ◽  
Wide Range ◽  
Sst Model ◽  
The One

A rotation-curvature correction suggested earlier by Spalart and Shur (1997, “On the Sensitization of Turbulence Models to Rotation and Curvature,” Aerosp. Sci. Technol., 1(5), pp. 297–302) for the one-equation Spalart–Allmaras turbulence model is adapted to the shear stress transport model. This new version of the model (SST-CC) has been extensively tested on a wide range of both wall-bounded and free shear turbulent flows with system rotation and/or streamline curvature. Predictions of the SST-CC model are compared with available experimental and direct numerical simulations (DNS) data, on the one hand, and with the corresponding results of the original SST model and advanced Reynolds stress transport model (RSM), on the other hand. It is found that in terms of accuracy the proposed model significantly improves the original SST model and is quite competitive with the RSM, whereas its computational cost is significantly less than that of the RSM.

Investigation of the Accuracy of RANS Models to Predict the Flow Through a Low-Pressure Turbine

2016 ◽  
Vol 138 (12) ◽  
Author(s):  
R. Pichler ◽  
R. D. Sandberg ◽  
V. Michelassi ◽  
R. Bhaskaran
Keyword(s):  
Linear Models ◽  
Turbulence Modeling ◽  
A Priori ◽  
Flow Characteristics ◽  
Turbulence Models ◽  
Low Pressure ◽  
Transport Equations ◽  
Compressible Turbulence ◽  
Low Pressure Turbine ◽  
Rans Models

In the present paper, direct numerical simulation (DNS) data of a low-pressure turbine (LPT) are investigated in light of turbulence modeling. Many compressible turbulence models use Favre-averaged transport equations of the conservative variables and turbulent kinetic energy (TKE) along with other modeling equations. First, a general discussion on the turbulence modeling error propagation prescribed by transport equations is presented, leading to the terms that are considered to be of interest for turbulence model improvement. In order to give turbulence modelers means of validating their models, the terms appearing in the Favre-averaged momentum equations are presented along pitchwise profiles at three axial positions. These three positions have been chosen such that they represent regions with different flow characteristics. General trends indicate that terms related with thermodynamic fluctuations and Favre fluctuations are small and can be neglected for most of the flow field. The largest errors arise close to the trailing edge (TE) region where vortex shedding occurs. Finally, linear models and the scope for their improvement are discussed in terms of a priori testing. Using locally optimized turbulence viscosities, the improvement potential of widely used models is shown. On the other hand, this study also highlights the danger of pure local optimization.

A Methodology for Simulating Compressible Turbulent Flows

2005 ◽  
Vol 73 (3) ◽  
pp. 405-412 ◽  
Author(s):  
Hermann F. Fasel ◽  
Dominic A. von Terzi ◽  
Richard D. Sandberg
Keyword(s):  
Turbulent Flows ◽  
Compressible Flows ◽  
Bluff Body ◽  
Turbulence Modeling ◽  
Flow Behavior ◽  
Flow Simulation ◽  
Turbulence Models ◽  
Navier Stokes ◽  
Fine Grid ◽  
Local Flow

A flow simulation Methodology (FSM) is presented for computing the time-dependent behavior of complex compressible turbulent flows. The development of FSM was initiated in close collaboration with C. Speziale (then at Boston University). The objective of FSM is to provide the proper amount of turbulence modeling for the unresolved scales while directly computing the largest scales. The strategy is implemented by using state-of-the-art turbulence models (as developed for Reynolds averaged Navier-Stokes (RANS)) and scaling of the model terms with a “contribution function.” The contribution function is dependent on the local and instantaneous “physical” resolution in the computation. This physical resolution is determined during the actual simulation by comparing the size of the smallest relevant scales to the local grid size used in the computation. The contribution function is designed such that it provides no modeling if the computation is locally well resolved so that it approaches direct numerical simulations (DNS) in the fine-grid limit and such that it provides modeling of all scales in the coarse-grid limit and thus approaches a RANS calculation. In between these resolution limits, the contribution function adjusts the necessary modeling for the unresolved scales while the larger (resolved) scales are computed as in large eddy simulation (LES). However, FSM is distinctly different from LES in that it allows for a consistent transition between RANS, LES, and DNS within the same simulation depending on the local flow behavior and “physical” resolution. As a consequence, FSM should require considerably fewer grid points for a given calculation than would be necessary for a LES. This conjecture is substantiated by employing FSM to calculate the flow over a backward-facing step and a plane wake behind a bluff body, both at low Mach number, and supersonic axisymmetric wakes. These examples were chosen such that they expose, on the one hand, the inherent difficulties of simulating (physically) complex flows, and, on the other hand, demonstrate the potential of the FSM approach for simulations of turbulent compressible flows for complex geometries.

scholarly journals Turbulence Modeling Effects on the CFD Predictions of Flow over a Detailed Full-Scale Sedan Vehicle

Fluids ◽  
2019 ◽  
Vol 4 (3) ◽  
pp. 148 ◽  
Author(s):  
Chunhui Zhang ◽  
Charles Patrick Bounds ◽  
Lee Foster ◽  
Mesbah Uddin
Keyword(s):  
Turbulence Modeling ◽  
Computational Cost ◽  
Turbulence Models ◽  
Relative Magnitude ◽  
Full Scale ◽  
Navier Stokes ◽  
Detached Eddy Simulation ◽  
Eddy Simulation ◽  
Rans Turbulence Models ◽  
Rans Models

In today’s road vehicle design processes, Computational Fluid Dynamics (CFD) has emerged as one of the major investigative tools for aerodynamics analyses. The age-old CFD methodology based on the Reynolds Averaged Navier–Stokes (RANS) approach is still considered as the most popular turbulence modeling approach in automotive industries due to its acceptable accuracy and affordable computational cost for predicting flows involving complex geometries. This popular use of RANS still persists in spite of the well-known fact that, for automotive flows, RANS turbulence models often fail to characterize the associated flow-field properly. It is even true that more often, the RANS approach fails to predict correct integral aerodynamic quantities like lift, drag, or moment coefficients, and as such, they are used to assess the relative magnitude and direction of a trend. Moreover, even for such purposes, notable disagreements generally exist between results predicted by different RANS models. Thanks to fast advances in computer technology, increasing popularity has been seen in the use of the hybrid Detached Eddy Simulation (DES), which blends the RANS approach with Large Eddy Simulation (LES). The DES methodology demonstrated a high potential of being more accurate and informative than the RANS approaches. Whilst evaluations of RANS and DES models on various applications are abundant in the literature, such evaluations on full-car models are relatively fewer. In this study, four RANS models that are widely used in engineering applications, i.e., the realizable k - ε two-layer, Abe–Kondoh–Nagano (AKN) k - ε low-Reynolds, SST k - ω , and V2F are evaluated on a full-scale passenger vehicle with two different front-end configurations. In addition, both cases are run with two DES models to assess the differences between the flow predictions obtained using RANS and DES.

Statistical Properties of Subgrid-Scale Turbulence Models

2021 ◽  
Vol 53 (1) ◽  
pp. 255-286
Author(s):  
Robert D. Moser ◽  
Sigfried W. Haering ◽  
Gopal R. Yalla
Keyword(s):  
Turbulent Flows ◽  
A Priori ◽  
Turbulence Models ◽  
Statistical Characteristics ◽  
Scale Model ◽  
Subgrid Scale ◽  
Dissipation Energy ◽  
Eddy Simulation ◽  
Scale Turbulence ◽  
Subgrid Stress

This review examines large eddy simulation (LES) models from the perspective of their a priori statistical characteristics. The most well-known statistical characteristic of an LES subgrid-scale model is its dissipation (energy transfer to unresolved scales), and many models are directly or indirectly formulated and tuned for consistency of this characteristic. However, in complex turbulent flows, many other subgrid statistical characteristics are important. These include such quantities as mean subgrid stress, subgrid transport of resolved Reynolds stress, and dissipation anisotropy. Also important are the statistical characteristics of models that account for filters that do not commute with differentiation and of the discrete numerical operators in the LES equations. We review the known statistical characteristics of subgrid models to assess these characteristics and the importance of their a priori consistency. We hope that this analysis will be helpful in continued development of LES models.

Nonlinear Versus Linear Stress-Strain Relations in Engine Turbulence Modeling Under Swirl and Squish Flow Conditions

2004 ◽  
Author(s):  
Mirko Baratta ◽  
Andrea E. Catania ◽  
Stefano d’Ambrosio
Keyword(s):  
Turbulence Modeling ◽  
Swirling Flow ◽  
Nonlinear Models ◽  
Local Equilibrium ◽  
Computational Cost ◽  
Turbulence Models ◽  
Stress Strain ◽  
Accuracy Order ◽  
Wall Boundary Conditions ◽  
Flow Features

A general form of the stress-strain constitutive relation was introduced for the application of two nonlinear k-ε turbulence models, namely, the ARS model of Gatski and Speziale ([1]) and the Cubic model of Lien et al. ([2]), to the numerical analysis of flow fields in a test engine with flat-piston and bowl-in-piston arrangements, under swirling or no-swirling flow motored conditions. The model capabilities in capturing turbulent flow features were compared to those of the upgraded linear RNG k-ε model which was previously indicated as a good compromise between accuracy and computational cost ([3]). Evaluations were made on the basis of the predicted flow evolution throughout the whole engine cycle, as well as of the comparison between numerical and experimental results. Furthermore, the effect of the stress-strain relationship on the predicted averaged turbulence quantities and anisotropy invariant values were examined, in addition to the sensitivity of the nonlinear models to the mesh quality. Finally, prospects concerning possible improvements of turbulence Eddy Viscosity Models (EVM) were presented. The predictions were made by a newly developed CFD code embedding various accuracy-order finite-volume discretization schemes. Modified wall boundary conditions with respect to the conventional logarithmic-function approach were used, so as to give negligible importance to the local equilibrium hypothesis.

A Comparison of the Linear and Nonlinear k–ε Turbulence Models in Combustors

1993 ◽  
Vol 115 (1) ◽  
pp. 93-102 ◽  
Author(s):  
C. C. Hwang ◽  
Genxing Zhu ◽  
M. Massoudi ◽  
J. M. Ekmann
Keyword(s):  
Fluid Dynamics ◽  
Turbulent Flows ◽  
Turbulence Modeling ◽  
Turbulence Models ◽  
Swirling Flows ◽  
Secondary Flows ◽  
Swirling Jets ◽  
Computational Fluid Dynamics Cfd ◽  
Linear And Nonlinear ◽  
Inlet Conditions

In swirling turbulent flows, the structure of turbulence is nonhomogeneous and anisotropic and it has been observed that the assumptions leading to the formulation of the k-ε model, which is used very often in many engineering applications, are inadequate for highly swirling flows. Furthermore, even with the various modifications made to the k-ε model, it is still not capable of describing secondary flows in noncircular ducts and it cannot predict non-zero normal-Reynolds-stress differences. Recently Speziale (1987) has developed a nonlinar k-ε model, which extends the range of validity of the standard k-ε model while maintaining most of the interesting features of the k-ε model; for example, the ease of application in existing Computational Fluid Dynamics (CFD) codes. In this work, we will use the nonlinear k-ε closure to model the turbulence in combustors. The particular combustor geometries selected for this study are (i) the flow in a round pipe entering an expansion into another coaxial round pipe, and (ii) the flow in two confined co-axial swirling jets. The results show that there are no significant differences in the performance of the two models. It is speculated that the inlet conditions for k and ε may play as crucial a role in achieving predicted accuracy as turbulence modeling details. Also it is possible that weaknesses in the performance of the modeled equations for k and ε may have masked differences in the two models.

scholarly journals Scientific multi-agent reinforcement learning for wall-models of turbulent flows

2021 ◽  
Author(s):  
H. Jane Bae ◽  
Petros Koumoutsakos
Keyword(s):  
Reinforcement Learning ◽  
Turbulent Flows ◽  
Weather Prediction ◽  
Computational Cost ◽  
Turbulence Models ◽  
Reynolds Numbers ◽  
Large Eddy ◽  
Multi Agent ◽  
Wall Models ◽  
Cooperating Agents

Abstract The predictive capabilities of turbulent flow simulations, critical for aerodynamic design and weather prediction, hinge on the choice of turbulence models. The abundance of data from experiments and simulations and the advent of machine learning have provided a boost to these modeling efforts. However, simulations of turbulent flows remain hindered by the inability of heuristics and supervised learning to model the near-wall dynamics. We address this challenge by introducing scientific multi-agent reinforcement learning (SciMARL) for the discovery of wall models for large-eddy simulations (LES). In SciMARL, discretization points act also as cooperating agents that learn to supply the LES closure model. The agents self-learn using limited data and generalise to extreme Reynolds numbers and previously unseen geometries. The present simulations reduce by several orders of magnitude the computational cost over fully-resolved simulations, while reproducing key flow quantities. We believe that SciMARL creates new capabilities for the simulation of turbulent flows.

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