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Reynolds-averaged Navier-Stokes simulations are still the main method to study complex flows in engineering. However, traditional turbulence models cannot accurately predict flow fields with separations. In such situation, machine learning…

流体动力学 · 物理学 2022-02-02 Yilang Liu , Weiwei Zhang , Zhenhua Xia

The turbulent boundary layer over a flat plate is computed by direct numerical simulation (DNS) of the incompressible Navier-Stokes equations as a test bed for a synthetic turbulence generator (STG) inflow boundary condition. The inlet…

流体动力学 · 物理学 2021-02-15 James R. Wright , Riccardo Balin , John W. Patterson , John A. Evans , Kenneth E. Jansen

This work extends the framework of the partially-averaged Navier-Stokes (PANS) equations to variable-density flow, \text{i.e.}, multi-material and/or compressible mixing problems with density variations and production of turbulence kinetic…

流体动力学 · 物理学 2021-08-11 F. S. Pereira , F. F. Grinstein , D. M. Israel , R. Rauenzahn , S. S. Girimaji

Reliably predictive simulation of complex flows requires a level of model sophistication and robustness exceeding the capabilities of current Reynolds-averaged Navier-Stokes (RANS) models. The necessary capability can often be provided by…

流体动力学 · 物理学 2022-01-20 Sigfried W. Haering , Todd A. Oliver , Robert D. Moser

We present a machine learning-based framework for blending data-driven turbulent closures in the Reynolds-Averaged Navier-Stokes (RANS) equations, aimed at improving their generalizability across diverse flow regimes. Specialized models…

流体动力学 · 物理学 2025-03-05 Mourad Oulghelou , Soufiane Cherroud , Xavier Merle , Paola Cinnella

The constants and functions in Reynolds-averaged Navier Stokes (RANS) turbulence models are coupled. Consequently, modifications of a RANS model often negatively impact its basic calibrations, which is why machine-learned augmentations are…

流体动力学 · 物理学 2023-10-17 Yuanwei Bin , George Huang , Robert Kunz , Xiang I A Yang

The Reynolds-averaged Navier-Stokes (RANS) equations are widely used in turbulence applications. They require accurately modeling the anisotropic Reynolds stress tensor, for which traditional Reynolds stress closure models only yield…

流体动力学 · 物理学 2022-03-23 Haitz Sáez de Ocáriz Borde , David Sondak , Pavlos Protopapas

The partially-averaged Navier-Stokes (PANS) equations are used to predict the variable-density Rayleigh-Taylor (RT) flow at Atwood number 0.5 and maximum Reynolds number $500$. This is a prototypical problem of material mixing featuring…

Reynolds-averaged Navier--Stokes (RANS) simulations with turbulence closure models continue to play important roles in industrial flow simulations. However, the commonly used linear eddy viscosity models are intrinsically unable to handle…

流体动力学 · 物理学 2019-05-22 Jin-Long Wu , Heng Xiao , Rui Sun , Qiqi Wang

This paper addresses the issue of predicting separated flows with Reynolds-averaged Navier-Stokes (RANS) turbulence models, which are essential for many engineering tasks. Traditional RANS models usually struggle with this task, so recent…

流体动力学 · 物理学 2024-11-15 Chenyu Wu , Shaoguang Zhang , Yufei Zhang

Stochastic Structural Stability Theory (SSST) provides an autonomous, deterministic, nonlinear dynamical system for evolving the statistical mean state of a turbulent system. In this work SSST is applied to the problem of understanding the…

流体动力学 · 物理学 2014-12-30 Brian F. Farrell , Petros J. Ioannou

Reynolds-averaged Navier-Stokes (RANS)-based transition modeling is widely used in aerospace applications but suffers inaccuracies due to the Boussinesq turbulent viscosity hypothesis. The eigenspace perturbation method can estimate the…

流体动力学 · 物理学 2022-11-08 Minghan Chu , Weicheng Qian

We assess the suitability of Reynolds-Averaged Navier-Stokes (RANS) simulation using the Spallart-Almaras (SA) turbulence model as a closure in analysing the performance of fluidic Active Flow Control (AFC) applications. In particular, we…

流体动力学 · 物理学 2024-07-31 Navid Monshi Tousi , Josep M. Bergada , Fernando Mellibovsky

We develop time-series machine learning (ML) methods for closure modeling of the Unsteady Reynolds Averaged Navier Stokes (URANS) equations applied to stably stratified turbulence (SST). SST is strongly affected by fine balances between…

The Reynolds-Averaged Navier-Stokes (RANS) approach remains a backbone for turbulence modeling due to its high cost-effectiveness. Its accuracy is largely based on a reliable Reynolds stress anisotropy tensor closure model. There has been…

流体动力学 · 物理学 2022-08-31 Jiayi Cai , Pierre-Emmanuel Angeli , Jean-Marc Martinez , Guillaume Damblin , Didier Lucor

We present an extended version of an invited talk given on the International Conference "Turbulent Mixing and Beyond". The dynamical and statistical description of stably stratified turbulent boundary layers with the important example of…

流体动力学 · 物理学 2009-02-18 Victor S. L'vov , Itamar Procaccia , Oleksii Rudenko

A new slow growth formulation for DNS of wall-bounded turbulent flow is developed and demonstrated to enable extension of slow growth modeling concepts to complex boundary layer flows. As in previous slow growth approaches, the formulation…

流体动力学 · 物理学 2017-08-16 Victor Topalian , Todd A. Oliver , Rhys Ulerich , Robert D. Moser

Wall-modeled large-eddy simulation (WMLES) is performed for flow over a wing with a focus on documenting grid resolution requirements to predict both the laminar and turbulent regions accurately. Flow over a spanwise extruded NACA0012…

流体动力学 · 物理学 2026-02-13 P. Balakumar , Prahladh S. Iyer

In light of the challenges surrounding convergence and error propagation encountered in Reynolds-averaged Navier-Stokes (RANS) equations with data-driven Reynolds stress closures, researchers commonly attribute these issues to…

流体动力学 · 物理学 2024-05-07 Xianglin Shan , Wenbo Cao , Weiwei Zhang

It is well known that Boussinesq turbulent-viscosity hypothesis can introduce uncertainty in predictions for complex flow features such as separation, reattachment, and laminar-turbulent transition. This study adopts a recent physics-based…

流体动力学 · 物理学 2022-10-19 Minghan Chu , Xiaohua Wu , David E. Rival