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相关论文: Data-Driven Fractional Subgrid-scale Modeling for …

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The occurrence of extremely thin concentration boundary layers at fluid interfaces for high local P\'eclet numbers is a severe obstacle for efficient and accurate numerical simulation of mass transfer processes in two-phase fluid systems.…

计算物理 · 物理学 2025-11-27 Moritz Schwarzmeier , Tomislav Marić , Željko Tuković , Dieter Bothe

By use of Lagrangian tracers propagated on 2D simulations of Scrape-Off Layer (SOL) turbulence, we are able to determine the non-local fractional-advection, fractional-diffusion equation (FADE) coefficients for a number of equilibrium…

等离子体物理 · 物理学 2025-03-28 T. Gheorghiu , F. Militello , J. Juul Rasmussen

Accurate subgrid-scale (SGS) modeling remains a major challenge in large eddy simulation (LES), particularly for wall-bounded turbulent flows with strong near-wall anisotropy. This study proposes a novel SGS model based on Liutex theory,…

流体动力学 · 物理学 2025-12-18 Jiawei Chen , Yifei Yu , Chaoqun Liu

This paper introduces generative Residual Networks (ResNet) as a surrogate Machine Learning (ML) tool for Large Eddy Simulation (LES) Sub Grid Scale (SGS) resolving. The study investigates the impact of incorporating Dual Scale Residual…

流体动力学 · 物理学 2024-09-13 Omar Sallam , Mirjam Fürth

While direct numerical simulations (DNS) are the most accurate method for studying turbulence, their large computational cost restricts their use to idealized configurations and to Reynolds numbers well below those found in practical…

流体动力学 · 物理学 2025-12-09 Chang Hsin Chen , Arnab Moitro , Alexei Y. Poludnenko

In this work, we will present a physically consistent theory to derive the governing equations of the Large Eddy Simulation (LES) framework based on first principles rather than the motivation to conduct computationally affordable…

流体动力学 · 物理学 2021-10-13 Max Okraschevski , Sven Hoffmann , Katharina Stichling , Rainer Koch , Hans-Joerg Bauer

In this work, we aim to deepen the understanding of inertial clustering and the role of sling events in high-Reynolds number ($Re$) particle-laden turbulence. To this end, we perform one-way coupled particle tracking in flow fields obtained…

流体动力学 · 物理学 2025-01-09 Lukas A. Codispoti , Daniel W. Meyer , Patrick Jenny

In this paper we propose a new modeling framework for large eddy simulations (LES) of particle-laden turbulent flows that captures the interaction between the particle and fluid phase on both the resolved and subgrid-scales. Unlike the vast…

流体动力学 · 物理学 2023-10-26 Max Hausmann , Fabien Evrard , Berend van Wachem

Symmetry-based analyses of multiscale velocity gradients highlight that strain self-amplification (SS) and vortex stretching (VS) drive forward energy transfer in turbulent flows. By contrast, a strain-vorticity covariance mechanism…

流体动力学 · 物理学 2026-01-14 Rahul Arun , Mostafa Kamal , Tim Colonius , Perry L. Johnson

The direct numerical simulation (DNS) of compressible isotropic turbulence up to the supersonic regime $Ma_{t} = 1.2$ has been investigated by high-order gas-kinetic scheme (HGKS) [{\it{Computers}} \& {\it{Fluids, 192, 2019}}]. In this…

流体动力学 · 物理学 2020-05-27 Guiyu Cao , Liang Pan , Kun Xu

We present a one-equation subgrid scale model that evolves the turbulence energy corresponding to unresolved velocity fluctuations in large eddy simulations. The model is derived in the context of the Germano consistent decomposition of the…

天体物理学 · 物理学 2009-11-11 W. Schmidt , J. C. Niemeyer , W. Hillebrandt

This study aims to enhance the generalizability of Reynolds-averaged Navier-Stokes (RANS) turbulence models, which are crucial for engineering applications. Classic RANS turbulence models often struggle to predict separated flows…

流体动力学 · 物理学 2025-09-03 Chenyu Wu , Shaoguang Zhang , Changxin Guo , Yufei Zhang

Subgrid-scale (SGS) models are critical in large-eddy simulations (LES) of turbulent flows. In this paper we conduct a comparative study on different SGS models, including one-k-equation, wall-adapting local eddy-viscosity (WALE), Sigma and…

流体动力学 · 物理学 2017-09-01 Liang Shi

We propose a statistical-stochastic surrogate modeling approach to predict the response of the mean and variance statistics under various initial conditions and external forcing perturbations. The proposed modeling framework extends the…

数据分析、统计与概率 · 物理学 2023-04-07 Di Qi , John Harlim

Accurately forecasting the real-time travel demand for dockless scooter-sharing is crucial for the planning and operations of transportation systems. Deep learning models provide researchers with powerful tools to achieve this task, but…

计算机与社会 · 计算机科学 2024-10-28 Yiming Xu , Xilei Zhao , Xiaojian Zhang , Mudit Paliwal

Two dimensional stochastic time model of scrape-off layer (SOL) turbulent transport is studied. Instability arisen in the system with respect to the stochastic perturbations of both either density or vorticity reveals itself in the strong…

等离子体物理 · 物理学 2009-11-10 D. Volchenkov , R. Lima

Turbulent flow across an in-line array of tube-banks with transverse and longitudinal pitch PT /D = 2.67, and PL /D = 2.31, has been simulated successfully by Large Eddy Simulation (LES) based on the dynamic Smagorinsky subgrid scale model…

流体动力学 · 物理学 2016-05-30 C. Jin , I. Potts , D. C. Swailes , M. W. Reeks

A new tuning-free subgrid-scale model, termed `locally-averaged scale-dependent dynamic' (LASDD) model, is developed and implemented in large-eddy simulations (LESs) of stable boundary layers. The new model dynamically computes the…

大气与海洋物理 · 物理学 2016-08-16 Sukanta Basu , Fernando Porté-Agel

We perform three-dimensional hydrodynamic simulations of two idealized regimes of stellar convection: a cooling-driven model (Model C) and an entropy-gradient-driven model (Model S). The two regimes exhibit striking contrasts: while Model S…

太阳与恒星天体物理 · 物理学 2025-11-18 Youhei Masada , Tomoya Takiwaki , Nobumitsu Yoko

High-order Discontinuous Galerkin (DG) methods offer excellent accuracy for turbulent flow simulations, especially when implemented on GPU-oriented architectures that favor very high polynomial orders. On modern GPUs, high-order polynomial…