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相关论文: The Myth of URANS

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Computational fluid dynamics using the Reynolds-averaged Navier-Stokes (RANS) remains the most cost-effective approach to study wake flows and power losses in wind farms. The underlying assumptions associated with turbulence closures are…

流体动力学 · 物理学 2022-08-03 Ali Eidi , Navid Zehtabiyan-Rezaie , Reza Ghiassi , Xiang Yang , Mahdi Abkar

Computational fluid dynamics (CFD) is a useful tool for prediction of turbulence in aerodynamic and biomedical applications. The choice of appropriate turbulence models is key to reaching accurate predictions. The present investigation…

流体动力学 · 物理学 2018-03-13 Fardin Khalili , Peshala P. T. Gamage , Hansen A. Mansy

Unsteady Aerodynamic Shape Optimization presents new challenges in terms of sensitivity analysis of time-dependent objective functions. In this work, we consider periodic unsteady flows governed by the URANS equations. Hence, the resulting…

数值分析 · 数学 2024-12-03 Steffen Schotthöfer , Beckett Y. Zhou , Tim Albring , Nicolas R. Gauger

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

Turbulent problems in industrial applications are predominantly solved using Reynolds Averaged Navier Stokes (RANS) turbulence models. The accuracy of the RANS models is limited due to closure assumptions that induce uncertainty into the…

流体动力学 · 物理学 2018-02-20 Atieh Alizadeh Moghaddam , Amir Sadaghiyani

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…

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

The present study assesses RANS-based turbulence models to simulate isothermal flow in a combustor representing a constituent can combustor of can-annular configuration used in jet engines. Two-equation models (standard $k-\epsilon$,…

流体动力学 · 物理学 2024-06-25 Aishvarya Kumar , Ram Prakash Bharti

Data-driven methods for improving turbulence modeling in Reynolds-Averaged Navier-Stokes (RANS) simulations have gained significant interest in the computational fluid dynamics community. Modern machine learning algorithms have opened up a…

流体动力学 · 物理学 2019-02-05 Nicholas Geneva , Nicholas Zabaras

A rational theory is proposed to describe the large-scale motion in turbulence. The fluid element with inner orientational structures is proposed to be the building block of fluid dynamics. The variance of the orientational structures then…

流体动力学 · 物理学 2011-05-31 Wennan Zou

Hypersonic flow conditions pose exceptional challenges for Reynolds-Averaged Navier-Stokes (RANS) turbulence modeling. Critical phenomena include compressibility effects, shock/turbulent boundary layer interactions, turbulence-chemistry…

流体动力学 · 物理学 2025-04-30 Pratikkumar Raje , Eric Parish , Jean-Pierre Hickey , Paola Cinnella , Karthik Duraisamy

Data-driven correction of turbulence models offers a promising route for improving Reynolds-averaged Navier-Stokes (RANS) predictions, but quantifying uncertainty in such corrections and ensuring generalization across flows remain key…

流体动力学 · 物理学 2026-04-28 Tyler Buchanan , Ali Eidi , Richard P. Dwight

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

To fully evaluate a turbulent flow, Direct Numerical Simulation (DNS) is the most accurate method by far and requires considerable computational power and time; not optimum for industry standards. Developing an alternative model, providing…

流体动力学 · 物理学 2022-07-04 Indrajit Nandi , Saikat Saha , Sabir Subedi , Sumon Saha

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

Three-dimensional turbulence is usually studied experimentally by using a spatially localized forcing at large scales (e.g. via rotating blades or oscillating grids), often in a deterministic way. Here, we report an original technique where…

Submarine hydrodynamics presents unique challenges in accurately predicting flow separation, wake structure, and resistance due to complex geometry and turbulent behaviour at high Reynolds (Re) numbers. Traditional Reynolds-Averaged…

流体动力学 · 物理学 2025-10-07 Noh Zainal Abidin , Frederic Grondin , Pol Muller , Jean-François Sigrist

The Reynolds-averaged Navier-Stokes (RANS) equations for steady-state assessment of incompressible turbulent flows remain the workhorse for practical computational fluid dynamics (CFD) applications. Consequently, improvements in speed or…

流体动力学 · 物理学 2020-12-04 Romit Maulik , Himanshu Sharma , Saumil Patel , Bethany Lusch , Elise Jennings

Reynolds-averaged Navier-Stokes (RANS) is one of the most cost-efficient approaches to simulate wind-farm-atmosphere interactions. However, the applicability of RANS-based methods is always limited by the accuracy of turbulence closure…

流体动力学 · 物理学 2021-12-08 Ali Eidi , Reza Ghiassi , Xiang Yang , Mahdi Abkar

Sampling synthetic turbulent fields as a computationally tractable surrogate for direct numerical simulations (DNS) is an important practical problem in various applications, and allows to test our physical understanding of the main…

流体动力学 · 物理学 2025-11-19 Timo Schorlepp , Katharina Kormann , Jeremiah Lübke , Tobias Schäfer , Rainer Grauer