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The influence of near-wall density and viscosity gradients on near-wall turbulence in a channel are studied by means of Direct Numerical Simulation (DNS) of the low-Mach number approximation of the Navier--Stokes equations. Different…

流体动力学 · 物理学 2023-02-17 Ashish Patel , Bendiks Jan Boersma , Rene Pecnik

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

In supersonic and hypersonic flows, the near-wall density variation due to wall cooling poses a challenge for accurately predicting the near-wall velocity and temperature profiles using classical eddy viscosity turbulence models.…

流体动力学 · 物理学 2025-08-19 Zifei Yin

The impact of intrinsic compressibility effects -- changes in fluid volume due to pressure variations -- on high-speed wall-bounded turbulence has often been overlooked or incorrectly attributed to mean property variations. To unambiguously…

流体动力学 · 物理学 2025-03-19 Asif Manzoor Hasan , Pedro Costa , Johan Larsson , Sergio Pirozzoli , Rene Pecnik

This paper presents a Weakly Compressible Smoothed Particle Hydrodynamics (WCSPH) method for solving the two-equation Reynolds-Averaged Navier-Stokes (RANS) model. The turbulent wall-bounded flow with or without mild flow separation, a…

流体动力学 · 物理学 2025-01-31 Feng Wang , Zhongguo Sun , Xiangyu Hu

At sufficiently high Reynolds numbers, shear-flow turbulence close to a wall acquires universal properties. When length and velocity are rescaled by appropriate characteristic scales of the turbulent flow and thereby measured in \emph{inner…

流体动力学 · 物理学 2020-03-18 Sajjad Azimi , Tobias M. Schneider

It has previously been shown that by increasing the Reynolds number across a channel by spatially varying the viscosity does not cause an immediate change in the size of turbulent structures and a delay is in fact observed in both wall…

流体动力学 · 物理学 2021-06-11 Victor Coppo Leite , Elia Merzari

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

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

In typical nature and engineering scenarios, such as supernova explosion and inertial confinement fusion, mixing flows induced by hydrodynamics interfacial instabilities are essentially compressible. Despite their significance, accurate…

流体动力学 · 物理学 2025-06-23 Hansong Xie , Tengfei Luo , Yaomin Zhao , Yousheng Zhang , Jianchun Wang

This paper develops scaling laws for wall-pressure root-mean-square (r.m.s.) and the peak of streamwise turbulence intensity, accounting for both variable-property and intrinsic compressibility effects -- those associated with changes in…

流体动力学 · 物理学 2025-05-15 Asif Manzoor Hasan , Pedro Costa , Johan Larsson , Rene Pecnik

In this work, a near-wall model, which couples the inverse of a recently developed compressible velocity transformation [Griffin, Fu, & Moin, PNAS, 118:34, 2021] and an algebraic temperature-velocity relation, is developed for high-speed…

流体动力学 · 物理学 2023-09-11 Kevin Patrick Griffin , Lin Fu , Parviz Moin

Despite well-known limitations of Reynolds-averaged Navier-Stokes (RANS) simulations, this methodology remains the most widely used tool for predicting many turbulent flows, due to computational efficiency. Machine learning is a promising…

流体动力学 · 物理学 2022-03-14 Ryley McConkey , Eugene Yee , Fue-Sang Lien

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

Traditional Reynolds-averaged Navier-Stokes (RANS) closures, based on the Boussinesq eddy viscosity hypothesis and calibrated on canonical flows, often yield inaccurate predictions of both mean flow and turbulence statistics. Here, we…

We study the Reynolds number scaling and the geometric self-similarity of a gain-based, low-rank approximation to turbulent channel flows, determined by the resolvent formulation of McKeon & Sharma (2010), in order to obtain a description…

流体动力学 · 物理学 2014-05-22 Rashad Moarref , Ati S. Sharma , Joel A. Tropp , Beverley J. McKeon

The Reynolds-averaged Navier-Stokes (RANS) equations provide a computationally efficient method for solving fluid flow problems in engineering applications. However, the use of closure models to represent turbulence effects can reduce their…

流体动力学 · 物理学 2024-05-02 Oliver Brenner , Justin Plogmann , Pasha Piroozmand , Patrick Jenny

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

Very recently, a defect model which depicts the growth tendency of the near-wall peak of the streamwise turbulence intensity has been developed (Chen $\&$ Sreenivasan, J. Fluid Mech. (2021), vol.908, R3). Based on the finiteness of the…

流体动力学 · 物理学 2023-03-30 Cheng Cheng , Lin Fu

A novel approach to wall modeling for the incompressible Navier-Stokes equations including flows of moderate and large Reynolds numbers is presented. The basic idea is that a problem-tailored function space allows prediction of turbulent…

流体动力学 · 物理学 2016-04-18 Benjamin Krank , Wolfgang A. Wall
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