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相关论文: Macroscopic forcing method: a tool for turbulence …

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The macroscopic forcing method (MFM) of Mani and Park and similar methods for obtaining turbulence closure operators, such as the Green's function-based approach of Hamba, recover reduced solution operators from repeated direct numerical…

计算物理 · 物理学 2024-01-19 Spencer H. Bryngelson , Florian Schäfer , Jessie Liu , Ali Mani

While recent approaches, such as the macroscopic forcing method (MFM) or Green's function-based approaches, can be used to compute Reynolds-averaged Navier--Stokes closure operators using forced direct numerical simulations, MFM can also be…

流体动力学 · 物理学 2025-10-15 Dana Lynn Ona-Lansigan Lavacot , Jessie Liu , Brandon E. Morgan , Ali Mani

The importance of nonlocality of mean scalar transport in 2D Rayleigh-Taylor Instability (RTI) is investigated. The Macroscopic Forcing Method (MFM) is utilized to measure spatio-temporal moments of the eddy diffusivity kernel representing…

流体动力学 · 物理学 2024-05-06 Dana Lynn O. -L. Lavacot , Jessie Liu , Hannah Williams , Brandon E. Morgan , Ali Mani

This study considers advective and diffusive transport of passive scalar fields by spatially-varying incompressible flows. Prior studies have shown that the eddy diffusivities governing the mean field transport in such systems can generally…

流体动力学 · 物理学 2023-06-30 Jessie Liu , Hannah Williams , Ali Mani

We use the recently developed Macroscopic Forcing Method [Mani and Park, Physical Review Fluids, 6:054607, 2021] to compute the scale-dependent eddy diffusivity characterizing ensemble-averaged scalar and momentum transport in…

流体动力学 · 物理学 2022-01-19 Yasaman Shirian , Ali Mani

Reynolds-averaged Navier--Stokes (RANS) closure must be sensitive to the flow physics, including nonlocality and anisotropy of the effective eddy viscosity. Recent approaches used forced direct numerical simulations to probe these effects,…

流体动力学 · 物理学 2024-09-24 Jessie Liu , Florian Schäfer , Spencer H. Bryngelson , Tamer A. Zaki , Ali Mani

This study aims to quantify how turbulence in a channel flow mixes momentum in the mean sense. We applied the macroscopic forcing method (Mani and Park, Physical Review Fluids, 2021, p.054607) to direct numerical simulation (DNS) of a…

流体动力学 · 物理学 2024-11-05 Danah Park , Ali Mani

Coarse resolution numerical ocean models must typically include a parameterisation for mesoscale turbulence. A common recipe for such parameterisations is to invoke down-gradient mixing, or diffusion, of some tracer quantity, such as…

流体动力学 · 物理学 2016-08-03 Julian Mak , James R. Maddison , David P. Marshall

Computational Fluid Dynamics (CFD) simulations using turbulence models are commonly used in engineering design. Of the different turbulence modeling approaches that are available, eddy viscosity based models are the most common for their…

流体动力学 · 物理学 2023-10-24 Minghan Chu , Weicheng Qian

While Macroscopic Fluctuation Theory (MFT) has been highly successful in analyzing non-equilibrium steady states, its application to non-steady-state processes remains limited. In this study, we apply MFT to the relaxation process of…

统计力学 · 物理学 2026-05-27 Daisuke Suzuki , Tomohiro Sasamoto

In this paper, we propose a variational approach to estimate eddy viscosity using forward sensitivity method (FSM) for closure modeling in nonlinear reduced order models. FSM is a data assimilation technique that blends model's predictions…

动力系统 · 数学 2020-11-05 Shady E. Ahmed , Kinjal Bhar , Omer San , Adil Rasheed

In this paper, based on the idea of direct discrete modeling (DDM) with equilibrium distribution functions (EDFs), we develop a general framework of the mesoscopic numerical method (MesoNM) for macroscopic partial differential equations…

数值分析 · 数学 2025-06-13 Baochang shi , Rui Du , Zhenhua Chai

Modal decomposition techniques are important tools for the analysis of unsteady flows and, in order to provide meaningful insights with respect to coherent structures and their characteristic frequencies, the modes must possess a robust…

流体动力学 · 物理学 2023-08-24 Lucas F. de Souza , Renato F. Miotto , William R. Wolf

We present a methodology to determine the best turbulence closure for an eddy-permitting ocean model through measurement of the error-landscape of the closure's subgrid spectral transfers and flux. We apply this method to 6 different…

流体动力学 · 物理学 2015-06-05 Jonathan Pietarila Graham , Todd Ringler

Multiphase flow phenomena have been widely observed in the industrial applications, yet it remains a challenging unsolved problem. Three-dimensional computational fluid dynamics (CFD) approaches resolve of the flow fields on finer spatial…

流体动力学 · 物理学 2020-05-11 Han Bao , Jinyong Feng , Nam Dinh , Hongbin Zhang

This article provides a reduced-order modelling framework for turbulent compressible flows discretized by the use of finite volume approaches. The basic idea behind this work is the construction of a reduced-order model capable of providing…

流体动力学 · 物理学 2024-05-31 Matteo Zancanaro , Valentin Nkana Ngan , Giovanni Stabile , Gianluigi Rozza

In this study we investigate the statistics of two-dimensional stationary turbulence using a Markovian forcing scheme, which correlates the forcing process in the current time step to the previous time step according to a defined memory…

流体动力学 · 物理学 2012-12-06 Omer San , Anne E. Staples

The description of hydrodynamic interactions between a particle and the surrounding liquid, down to the nanometer scale, is of primary importance since confined liquids are ubiquitous in many natural and technological situations. In this…

Fluid turbulence is an important problem for physics and engineering. Turbulence modeling deals with the development of simplified models that can act as surrogates for representing the effects of turbulence on flow evolution. Such models…

流体动力学 · 物理学 2021-11-16 J P Panda

The closure problem of turbulence is still a challenging issue in turbulence modeling. In this work, a stability condition is used to close turbulence. Specifically, we regard single-phase flow as a mixture of turbulent and non-turbulent…

流体动力学 · 物理学 2014-06-20 Lin Zhang , Xiaoping Qiu , Limin Wang , Jinghai Li
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