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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

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

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

Reynolds-Averaged Navier-Stokes(RANS) method will still play a vital role in the following several decade in aerospace engineering. Although RANS models are widely used, empiricism and large discrepancies between models reduce the…

流体动力学 · 物理学 2018-07-05 Weiwei Zhang , Linyang Zhu , Yilang Liu , Jiaqing Kou

Reynolds-averaged Navier-Stokes (RANS) equations are widely used in engineering turbulent flow simulations. However, RANS predictions may have large discrepancies due to the uncertainties in modeled Reynolds stresses. Recently, Wang et al.…

流体动力学 · 物理学 2018-09-11 Jin-Long Wu , Heng Xiao , Eric Paterson

In the turbulence modeling community, significant efforts have been made to quantify the uncertainties in the Reynolds-Averaged Navier--Stokes (RANS) models and to improve their predictive capabilities. Of crucial importance in these…

流体动力学 · 物理学 2017-10-11 Heng Xiao , Jin-Long Wu , Jian-xun Wang , Eric G. Paterson

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

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

This study presents a compact data-driven Reynolds-averaged Navier-Stokes (RANS) model for wind turbine wake prediction, built as an enhancement of the standard \(k\)-\(\varepsilon\) formulation. Several candidate models were discovered…

流体动力学 · 物理学 2025-12-09 Kherlen Jigjid , Ali Eidi , Nguyen Anh Khoa Doan , Richard P. Dwight

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…

Wing-body junction flows occur when a boundary layer encounters an airfoil mounted on the surface. The corner flow near the trailing edge is challenging for the linear eddy viscosity Reynolds Averaged Navier-Stokes (RANS) models, due to the…

流体动力学 · 物理学 2016-05-20 Jin-Long Wu , Jian-Xun Wang , Heng Xiao

In this paper, investigations are conducted using Reynolds-averaged Navier-Stokes (RANS) turbulence models to investigate the importance of turbulence modelling for nasal inspiration at a constant flow rate of 250 ml/s. Four different,…

医学物理 · 物理学 2017-06-09 Elin Aasgrav , Sverre Gullikstad Johnsen , Are Johan Simonsen , Bernhard Müller

This chapter provides an introduction to data-driven techniques for the development and calibration of closure models for the Reynolds-Averaged Navier--Stokes (RANS) equations. RANS models are the workhorse for engineering applications of…

流体动力学 · 物理学 2024-04-16 Paola Cinnella

Reynolds-averaged Navier-Stokes (RANS) turbulence models (such as k-\epsilon models) are still widely used for engineering applications because of their relatively simplicity and robustness. In fully developed plane channel flow (i.e. the…

流体动力学 · 物理学 2010-12-01 Najla El Gharbi , Rafik Absi , Ahmed Benzaoui

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

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

Several methods have been proposed to characterize the complex interactions in turbulent wakes, especially for flows with strong cyclic dynamics. This paper introduces the concept of Fourier-Averaged Navier-Stokes (FANS) equations as a…

流体动力学 · 物理学 2024-01-25 Benjamin R. S. Freeman , Robert J. Martinuzzi , Arman Hemmati

Reynolds-Averaged Navier-Stokes (RANS) models are widely used for turbulent flow simulations due to their computational efficiency, but their accuracy strongly depends on the selected turbulence closure and may vary across the flow domain.…

数值分析 · 数学 2026-03-18 Piero Zappi , Anna Ivagnes , Niccolò Tonicello , Gianluigi Rozza

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…

Accurate prediction of mixing transition induced by interfacial instabilities is vital for engineering applications, but has remained a great challenge for decades. For engineering practices, Reynolds-averaged Navier-Stokes simulation…

流体动力学 · 物理学 2023-10-02 Hansong Xie , Mengjuan Xiao , Yousheng Zhang , Yaomin Zhao
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