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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

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

In this work, model closures of the multiphase Reynolds-Average Navier-Stokes (RANS) equations are developed for homogeneous, fully-developed gas--particle flows. To date, the majority of RANS closures are based on extensions of…

流体动力学 · 物理学 2021-07-07 S. Beetham , R. O. Fox , J. Capecelatro

Numerical solution of the system of partial differential equations arising from the Reynolds-Averaged Navier-Stokes (RANS) equations with $k-\epsilon$ turbulence model presents several challenges due to the advection dominated nature of the…

数值分析 · 数学 2017-12-06 Umberto Villa , Alexandre Noll Marques

The goal of this dissertation is to investigate the PANS model capabilities in providing significant improvement over RANS predictions at slightly higher computational expense and producing LES quality results at significantly lower…

流体动力学 · 物理学 2017-12-12 Pooyan Razi

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…

A computational fluid dynamics (CFD) model that solves the steady-state Reynolds-Averaged Navier-Stokes (RANS) equations for buoyant compressible pollution dispersion under different meteorological conditions is developed. A 6.4 km by 6.4…

流体动力学 · 物理学 2022-11-15 Zhihao Li , Rebecca Tanzer-Gruener , Albert Presto , Peter Adams , Satbir Singh

A data-driven framework for formulation of closures of the Reynolds-Average Navier--Stokes (RANS) equations is presented. In recent years, the scientific community has turned to machine learning techniques to distill a wealth of highly…

流体动力学 · 物理学 2020-09-02 S. Beetham , J. Capecelatro

Solving the Reynolds-averaged Navier-Stokes equations (RANS) closed with an eddy viscosity computed through a turbulence model is still the leading approach for Computational Fluid Dynamics simulations. Unfortunately, universal models with…

流体动力学 · 物理学 2025-09-18 Marco Castelletti , Maurizio Quadrio

The paper investigates the use of low-diffusion (contact-discontinuity-resolving [Liou M.S.: {\em J. Comp. Phys.} {\bf 160} (2000) 623--648]) approximate Riemann solvers for the convective part of the Reynolds-averaged Navier-Stokes…

计算物理 · 物理学 2016-07-01 N. Ben Nasr , G. A. Gerolymos , I. Vallet

Numerical solvers of the incompressible Navier-Stokes equations have reproduced turbulence phenomena such as the law of the wall, the dependence of turbulence intensities on the Reynolds number, and experimentally observed properties of…

流体动力学 · 物理学 2015-06-05 Divakar Viswanath , Ian Tobasco

In this visualisation the instantaneous local velocity is expressed in terms of four components to capture the development of and interactions between coherent structures in turbulent flows. It is then possible to isolate the terms linked…

流体动力学 · 物理学 2009-10-13 Trinh Khanh Tuoc

Turbulent flows have high requirements for very fine meshes near the boundary to ensure accuracy. In the context of topology optimization (TO), such fine meshes become unrealistic and common approaches are hampered by low accuracy and…

流体动力学 · 物理学 2026-01-06 Amirhossein Bayat , Hao Li , Joe Alexandersen

A priori tests of turbulence models for the compressible Reynolds-Averaged Navier--Stokes (RANS) are performed by using Direct Numerical Simulations (DNS) data of zero-pressure-gradient flat-plate turbulent boundary layers. The DNS database…

流体动力学 · 物理学 2023-10-17 Sciacovelli L. , Cannici A. , Passiatore D. , Cinnella P

Most turbulence models used in Reynolds-averaged Navier-Stokes (RANS) simulations are partial differential equations (PDE) that describe the transport of turbulent quantities. Such quantities include turbulent kinetic energy for eddy…

流体动力学 · 物理学 2022-02-18 Ruiying Xu , Xu-Hui Zhou , Jiequn Han , Richard P. Dwight , Heng Xiao

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

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

Typical multispecies compressible Navier-Stokes computations employ conservative equations for mass fraction transport. Upwind discretisations of these governing equations produce spurious pressure oscillations at diffuse contact surfaces…

计算物理 · 物理学 2018-06-28 Ben Thornber , Michael Groom , David Youngs

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

This paper proposes a phenomenological Reynolds Averaged Navier-Stokes (RANS) calculation model based on physical constraints. In this model part of the source terms in the e equation was replaced with the deep learning model, using the…

流体动力学 · 物理学 2021-12-28 Shuming Zhang , Haiwang Li , Ruquan You , Tinglin Kong , Zhi Tao
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