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Numerical simulations based on Reynolds-Averaged Navier--Stokes (RANS) equations are widely used in engineering design and analysis involving turbulent flows. However, RANS simulations are known to be unreliable in many flows of engineering…

流体动力学 · 物理学 2017-09-19 Jinlong Wu , Rui Sun , Sylvain Laizet , Heng Xiao

In the present paper, an aerodynamic investigation of a high-speed train is performed. In the first section of this article, a generic high-speed train against a turbulent flow is simulated, numerically. The Reynolds-Averaged Navier-Stokes…

This work determines the inaccuracy of using Reynolds averaged Navier Stokes (RANS) turbulence models in transition to turbulent flow regimes by predicting the model-based discrepancies between RANS and large eddy simulation (LES) models…

流体动力学 · 物理学 2019-01-21 Mustafa Usta , Ali Tosyali

We consider three-dimensional stochastically forced Navier-Stokes equations subjected to white-in-time (colored-in-space) forcing in the absence of boundaries. Upper and lower bounds of the mean value of the time-averaged energy dissipation…

偏微分方程分析 · 数学 2020-04-21 Yat Tin Chow , Ali Pakzad

Reynolds Averaged Navier Stokes (RANS) models represent the workhorse for studying turbulent flows in industrial applications. Such single-point turbulence models have limitations in accounting for the influence of the non-local physics and…

流体动力学 · 物理学 2017-04-19 K. Duraisamy , Anand A. , G. Iaccarino

Despite their well-known limitations, Reynolds-Averaged Navier-Stokes (RANS) models are still the workhorse tools for turbulent flow simulations in today's engineering application. For many practical flows, the turbulence models are by far…

计算物理 · 物理学 2018-09-11 H. Xiao , J. -L. Wu , J. -X. Wang , R. Sun , C. J. Roy

This study presents a novel approach for enhancing Reynolds-averaged Navier-Stokes (RANS) turbulence modeling through the application of a Relative Importance Term Analysis (RITA) methodology to develop a new zonally-augmented $k-\omega$…

流体动力学 · 物理学 2025-11-26 Tyler Buchanan , Monica Lăcătuş , Alastair West , Richard P. Dwight

Using the Lagrangian transport of momentum, the Reynolds stress can be expressed in terms of basic turbulence parameters. DNS data at higher Reynolds numbers (Re= 1000 and 5200) have been used to again validate this theory, where it is the…

流体动力学 · 物理学 2019-03-11 T. -W. Lee

We accomplish two major tasks. First, we show that the turbulent motion at large scales obeys Gaussian statistics in the interval 0 < Rlambda < 8.8, where Rlambda is the microscale Reynolds number, and that the Gaussian flow breaks down to…

流体动力学 · 物理学 2021-06-23 K. R. Sreenivasan , V. Yakhot

Symbolic regression (SR) methods have been extensively investigated to explore explicit algebraic Reynolds stress models (EARSM) for turbulence closure of Reynolds-averaged Navier-Stokes (RANS) equations. The deduced EARSM can be readily…

流体动力学 · 物理学 2024-10-15 Yu Zhang , Kefeng Zheng , Fei Liu , Qingfu Zhang , Zhenkun Wang

Turbulence modeling is a classical approach to address the multiscale nature of fluid turbulence. Instead of resolving all scales of motion, which is currently mathematically and numerically intractable, reduced models that capture the…

流体动力学 · 物理学 2018-12-10 Rui Fang , David Sondak , Pavlos Protopapas , Sauro Succi

Anomalous dissipation is a dissipation mechanism of kinetic energy which is established by a sufficiently spatially rough velocity field. It implies that the rescaled mean kinetic energy dissipation rate becomes constant with respect to…

流体动力学 · 物理学 2024-11-22 Georgy Zinchenko , Vladyslav Pushenko , Joerg Schumacher

Numerical models based on Reynolds-Averaged Navier-Stokes (RANS) equations are widely used in engineering turbulence modeling. However, the RANS predictions have large model-form uncertainties for many complex flows. Quantification of these…

计算物理 · 物理学 2017-01-25 Jian-Xun Wang , Rui Sun , Heng Xiao

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

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

In the present paper a new data-driven model is proposed to close and increase accuracy of RANS equations. The divergence of the Reynolds Stress Tensor (RST) is obtained through a Neural Network (NN) whose architecture and input choice…

流体动力学 · 物理学 2022-10-19 Stefano Berrone , Davide Oberto

In this paper, a novel zonal machine learning (ML) approach for Reynolds-averaged Navier-Stokes (RANS) turbulence modelling based on the divide-and-conquer technique is introduced. This approach involves partitioning the flow domain into…

流体动力学 · 物理学 2024-08-27 Anthony Man , Mohammad Jadidi , Amir Keshmiri , Hujun Yin , Yasser Mahmoudi

The development of advanced simulation tools is essential, both presently and in the future, for improving wind-energy design strategies, paving the way for a complete transition to sustainable solutions. The Reynolds-averaged Navier-Stokes…

流体动力学 · 物理学 2024-11-19 Ali Amarloo , Navid Zehtabiyan-Rezaie , Mahdi Abkar

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

Direct numerical simulations of three-dimensional (3D) homogeneous turbulence under rapid rigid rotation are conducted to examine the predictions of resonant wave theory for both small Rossby number and large Reynolds number. The simulation…

混沌动力学 · 物理学 2009-11-10 Q. Chen , S. Chen , G. L. Eyink , D. D. Holm