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Accurate and generalizable Reynolds-averaged Navier-Stokes (RANS) models for turbulent flows rely on effective closures, but currently available closures are notoriously unreliable. Kassinos et al. (J. Fluid Mechanics, 428, pp. 213-248,…

流体动力学 · 物理学 2026-03-02 Aaron Miller , Sahil Kommalapati , Robert Moser , Petros Koumoutsakos

Reynolds-averaged Navier-Stokes (RANS) equations are presently one of the most popular models for simulating turbulence. Performing RANS simulation requires additional modeling for the anisotropic Reynolds stress tensor, but traditional…

流体动力学 · 物理学 2020-12-02 Rui Fang , David Sondak , Pavlos Protopapas , Sauro Succi

The Reynolds-averaged Navier-Stokes (RANS) equations are widely used in turbulence applications. They require accurately modeling the anisotropic Reynolds stress tensor, for which traditional Reynolds stress closure models only yield…

流体动力学 · 物理学 2022-03-23 Haitz Sáez de Ocáriz Borde , David Sondak , Pavlos Protopapas

Surrogate models are necessary to optimize meaningful quantities in physical dynamics as their recursive numerical resolutions are often prohibitively expensive. It is mainly the case for fluid dynamics and the resolution of Navier-Stokes…

机器学习 · 计算机科学 2023-06-02 Florent Bonnet , Ahmed Jocelyn Mazari , Paola Cinnella , Patrick Gallinari

Computational fluid dynamics (CFD) solvers employing two-equation eddy viscosity models are the industry standard for simulating turbulent flows using the Reynolds-averaged Navier-Stokes (RANS) formulation. While these methods are…

机器学习 · 计算机科学 2024-10-25 Shinjan Ghosh , Amit Chakraborty , Georgia Olympia Brikis , Biswadip Dey

Predicting particle-laden flows requires accurate fluid force models. However, a reliable particle force model for finite-size particles in turbulent flows remains lacking. In the present work, a fluid force model for a finite-size…

流体动力学 · 物理学 2025-05-29 Yuqi Wang , Ruifeng Hu

Computational Fluid Dynamics (CFD) plays a pivotal role in fluid mechanics, enabling precise simulations of fluid behavior through partial differential equations (PDEs). However, traditional CFD methods are resource-intensive, particularly…

计算工程、金融与科学 · 计算机科学 2025-02-06 Tiffany Fan , Murray Cutforth , Marta D'Elia , Alexandre Cortiella , Alireza Doostan , Eric Darve

Many geophysical and astrophysical phenomena are driven by turbulent fluid dynamics, containing behaviors separated by tens of orders of magnitude in scale. While direct simulations have made large strides toward understanding geophysical…

流体动力学 · 物理学 2018-10-10 Jonathan S Cheng , Jonathan M Aurnou , Keith Julien , Rudie P J Kunnen

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

Although Reynolds-Averaged Navier-Stokes (RANS) equations are still the dominant tool for engineering design and analysis applications involving turbulent flows, standard RANS models are known to be unreliable in many flows of engineering…

计算物理 · 物理学 2018-09-11 Jin-Long Wu , Jian-Xun Wang , Heng Xiao , Julia Ling

Scale-resolving simulations of high Reynolds number incompressible flows are often limited by the Courant-Friedrichs-Lewy (CFL) stability restriction imposed by explicit time-stepping schemes, resulting in small time step sizes and long…

流体动力学 · 物理学 2026-04-20 Henrik Wüstenberg , Alexandra Liosi , Spencer J. Sherwin , Joaquim Peiró , David Moxey

Spatially developing round jet flows are fundamental to numerous engineering applications. This letter applies the wave-particle turbulence simulation (WPTS) method, a recently developed multiscale approach, to simulate a spatially…

流体动力学 · 物理学 2025-07-22 Xiaojian Yang , Kun Xu

By analogy with the kinetic theory of gases, most turbulence modeling strate- gies rely on an eddy viscosity to model the unresolved turbulent fluctuations. How- ever, the ratio of unresolved to resolved scales - very much like a degree of…

流体动力学 · 物理学 2016-08-24 Marcello Righi

A new model for the "rapid" part of the velocity/pressure-gradient correlation in the Reynolds averaged Navier-Stokes equations is suggested. It is shown that in an inhomogeneous incompressible turbulent flow, the model that is linear in…

流体动力学 · 物理学 2007-05-23 Svetlana V. Poroseva

A model for the pseudo-turbulent Reynolds stress tensor in compressible flows through monodisperse particle clouds is developed based on data from particle resolved numerical simulations. This model extends previous models for the…

流体动力学 · 物理学 2025-05-09 Andreas Nygård Osnes , Magnus Vartdal

Two-dimensional turbulence in a rectangular domain self-organises into large-scale unidirectional jets. While several results are present to characterize the mean jets velocity profile, much less is known about the fluctuations. We study…

流体动力学 · 物理学 2016-02-23 Cesare Nardini , Tomás Tangarife

Accurate and robust models for the pressure strain correlation are an essential component for the success of Reynolds Stress Models in turbulent flow simulations. However replicating the non-local action of pressure using only local tensors…

流体动力学 · 物理学 2019-03-14 Jyoti Prakash Panda

The Benchmarck on the Aerodynamics of a Rectangular 5:1 Cylinder is studied using a data-driven technique which bridges numerical simulation and available experimental results. Because of intrinsic features of the tools used for…

流体动力学 · 物理学 2023-12-25 Tom Moussie , Paolo Errante , Marcello Meldi

In this paper, we calibrate the coefficients for the one-dimensional Reynolds stress model with the data generated from the three-dimensional numerical simulations of upward overshooting in turbulent compressible convection. It has been…

太阳与恒星天体物理 · 物理学 2020-06-17 Tao Cai

A computational fluid dynamics (CFD) simulation framework for fluid-flow prediction is developed on the Tensor Processing Unit (TPU) platform. The TPU architecture is featured with accelerated dense matrix multiplication, large high…

计算物理 · 物理学 2022-03-02 Qing Wang , Matthias Ihme , Yi-Fan Chen , John Anderson