The Experts below are selected from a list of 142752 Experts worldwide ranked by ideXlab platform
Florian R Menter - One of the best experts on this subject based on the ideXlab platform.
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sensitization of the sst Turbulence Model to rotation and curvature by applying the spalart shur correction term
Journal of Turbomachinery-transactions of The Asme, 2009Co-Authors: Pavel E Smirnov, Florian R MenterAbstract: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.
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review of the shear stress transport Turbulence Model experience from an industrial perspective
International Journal of Computational Fluid Dynamics, 2009Co-Authors: Florian R MenterAbstract:The present author was asked to provide an update on the status and the more recent developments around the shear-stress transport (SST) Turbulence Model for this special issue of the journal. The article is therefore not intended as a comprehensive overview of the status of engineering Turbulence Modelling in general, nor on the overall Turbulence Modelling strategy for ANSYS computational fluid dynamics (CFD) in particular. It is clear from many decades of Turbulence Modelling that no single Model-nor even a single Modelling approach-can solve all engineering flows. Any successful CFD code will therefore have to offer a wide range of Models from simple Eddy-viscosity Models through second moment closures all the way to the variety of unsteady Modelling concepts currently under development. This article is solely intended to outline the role of the concepts behind the SST Model in current and future CFD simulations of engineering flows.
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sensitization of the sst Turbulence Model to rotation and curvature by applying the spalart shur correction term
ASME Turbo Expo 2008: Power for Land Sea and Air, 2008Co-Authors: Pavel E Smirnov, Florian R MenterAbstract:A rotation-curvature correction suggested earlier by Spalart and Shur 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 DNS data, on one hand, and with the corresponding results of the original SST Model and advanced Reynolds stresses 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.© 2008 ASME
Xiaoqing Zheng - One of the best experts on this subject based on the ideXlab platform.
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a strongly coupled time marching method for solving the navier stokes andk ω Turbulence Model equations with multigrid
Journal of Computational Physics, 1996Co-Authors: Xiaoqing ZhengAbstract:Many researchers use a time-lagged or loosely coupled approach in solving the Navier?Stokes equations and two-equation Turbulence Model equations in a time-marching method. The Navier?Stokes equations and the Turbulence Model equations are solved separately and often with different methods. In this paper a strongly coupled method is presented for such calculations. The Navier?Stokes equations and two-equation Turbulence Model equations, in particular, thek-? equations, are considered as one single set of strongly coupled equations and solved with the same explicit time-marching algorithm without time-lagging. A multigrid method, together with other acceleration techniques such as local time steps and implicit residual smoothing, is applied to both the Navier?Stokes and the Turbulence Model equations. Time step limits due to the source terms in thek-? equations are relieved by treating the appropriate source terms implicitly. The equations are also strongly coupled in space through the use of staggered control volumes. The method is applied to the calculation of flows through cascades as well as over isolated airfoils. Convergence rate is greatly improved by the use of the multigrid method with the strongly coupled time-marching scheme.
P K Chaviaropoulos - One of the best experts on this subject based on the ideXlab platform.
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evaluation of the effects of Turbulence Model enhancements on wind turbine wake predictions
Wind Energy, 2011Co-Authors: J Prospathopoulos, Evangelos S Politis, K Rados, P K ChaviaropoulosAbstract:The Modelling of wind turbine wakes is investigated in this paper using a Navier–Stokes solver employing the k–ω Turbulence Model appropriately modified for atmospheric flows. It is common knowledge that even single-wind turbine wake predictions with computational fluid dynamic methods underestimate the near wake deficit, directly contributing to the overestimation of the power of the downstream turbines. For a single-wind turbine, alternative Modelling enhancements under neutral and stable atmospheric conditions are tested in this paper to account for and eventually correct the Turbulence overestimation that is responsible for the faster flow recovery that appears in the numerical predictions. Their effect on the power predictions is evaluated with comparison with existing wake measurements. A second issue addressed in this paper concerns multi-wake predictions in wind farms, where the estimation of the reference wind speed that is required for the thrust calculation of a turbine located in the wake(s) of other turbines is not obvious. This is overcome by utilizing an induction factor-based concept: According to it, the definition of the induction factor and its relationship with the thrust coefficient are employed to provide an average wind speed value across the rotor disk for the estimation of the axial force. Application is made on the case of five wind turbines in a row. Copyright © 2010 John Wiley & Sons, Ltd.
Tobias Leicht - One of the best experts on this subject based on the ideXlab platform.
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adjoint based error estimation and adaptive mesh refinement for the rans and k ω Turbulence Model equations
Journal of Computational Physics, 2011Co-Authors: Ralf Hartmann, Joachim Held, Tobias LeichtAbstract:Abstract In this article we present the extension of the a posteriori error estimation and goal-oriented mesh refinement approach from laminar to turbulent flows, which are governed by the Reynolds-averaged Navier–Stokes and k – ω Turbulence Model (RANS- kω ) equations. In particular, we consider a discontinuous Galerkin discretization of the RANS- kω equations and use it within an adjoint-based error estimation and adaptive mesh refinement algorithm that targets the reduction of the discretization error in single as well as in multiple aerodynamic force coefficients. The accuracy of the error estimation and the performance of the goal-oriented mesh refinement algorithm is demonstrated for various test cases, including a two-dimensional turbulent flow around a three-element high lift configuration and a three-dimensional turbulent flow around a wing-body configuration.
Juan J Alonso - One of the best experts on this subject based on the ideXlab platform.
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A Machine Learning Strategy to Assist Turbulence Model Development
53rd AIAA Aerospace Sciences Meeting, 2015Co-Authors: Brendan D. Tracey, Karthikeyan Duraisamy, Juan J AlonsoAbstract:Turbulence Modeling in a Reynolds Averaged Navier{Stokes (RANS) setting has tra- ditionally evolved through a combination of theory, mathematics, and empiricism. The problem of closure, resulting from the averaging process, requires an infusion of informa- tion into the various Models that is often managed in an ad-hoc way or that is focused on particular classes of problems, thus diminishing the predictive capabilities of a Model in other flow contexts. In this work, a proof-of-concept of a new data-driven approach of Turbulence Model development is presented. The key idea in the proposed framework is to use supervised learning algorithms to build a representation of Turbulence Modeling closure terms. The learned terms are then inserted into a Computational Fluid Dynamics (CFD) numerical simulation with the aim of offering a better representation of Turbulence physics. But while the basic idea is attractive, Modeling unknown terms by increasingly large amounts of data from higher-fidelity simulations (LES, DNS, etc) or even experiment, the details of how to make the approach viable are not at all obvious. In this work, we investigate the feasibility of such an approach by attempting to reproduce, through a machine learning methodology, the results obtained with the well-established Spalart- Allmaras Model. In other words, the key question that we seek to answer is the following: Given a number of observations of CFD solutions using the Spalart-Allmaras Model (our truth Model), can we reproduce those solutions using machine-learning techniques with- out knowledge of the structure, functional form, and coefficients of the actual Model? We discuss the challenges of applying machine learning techniques in a fluid dynamic setting and possible successful approaches. We also explore the potential for machine learning as an enhancement to or replacement for traditional Turbulence Models. Our results high- light the potential and viability of machine learning approaches to aid Turbulence Model development.
