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Turbulence in compressible plasma plays a key role in many areas of astrophysics and engineering. The extreme plasma parameters in these environments, e.g. high Reynolds numbers, supersonic and super-Alfvenic flows, however, make direct…

Compressible magnetohydrodynamic (MHD) turbulence is ubiquitous in astrophysical phenomena ranging from the intergalactic to the stellar scales. In studying them, numerical simulations are nearly inescapable, due to the large degree of…

流体动力学 · 物理学 2016-07-27 Dimitar G Vlaykov , Philipp Grete , Wolfram Schmidt , Dominik R G Schleicher

Large eddy simulations (LES) are a powerful tool in understanding processes that are inaccessible by direct simulations due to their complexity, for example, in the highly turbulent regime. However, their accuracy and success depends on a…

流体动力学 · 物理学 2017-03-27 Philipp Grete , Dimitar G Vlaykov , Wolfram Schmidt , Dominik R G Schleicher

Most sub-grid scale (SGS) models employed in LES (large eddy simulation) formulations were originally developed for incompressible, single phase, inert flows and assume transfer of energy based on the classical energy cascade mechanism.…

流体动力学 · 物理学 2023-09-13 Jhon Cordova , Cesar Celis , Andres Mendiburu , Luis Bravo , Prashant Khare

We formulate and implement the Euler equations with SGS dynamics and provide numerical tests of an SGS turbulence energy model that predicts the turbulent pressure of unresolved velocity fluctuations and the rate of dissipation for highly…

星系天体物理 · 物理学 2015-05-20 W. Schmidt , C. Federrath

Large Eddy Simulation (LES) of turbulent non-Newtonian flows involves two additional closures, namely the Non-Newtonian SubGrid-Scale (NNSGS) stress tensor and filtered viscosity. Here, dynamic closures are proposed for NNSGS, eliminating…

流体动力学 · 物理学 2025-11-14 E. Amani , A. Ahmadpour , M. J. Aghajari

Deep learning is increasingly becoming a promising pathway to improving the accuracy of sub-grid scale (SGS) turbulence closure models for large eddy simulations (LES). We leverage the concept of differentiable turbulence, whereby an…

Large eddy simulation (LES) has become a central technique for simulating turbulent flows in engineering and applied sciences, offering a compromise between accuracy and computational cost by resolving large scale motions and modeling the…

流体动力学 · 物理学 2025-08-27 Rik Hoekstra , Wouter Edeling

Accurate subgrid-scale turbulence models are needed to perform realistic numerical magnetohydrodynamic (MHD) simulations of the subsurface flows of the Sun. To perform large-eddy simulations (LES) of turbulent MHD flows, three unknown terms…

太阳与恒星天体物理 · 物理学 2010-10-28 G. Balarac , A. G. Kosovichev , O. Brugière , A. A. Wray , N. N. Mansour

Rotating turbulent flows form a challenging test case for large-eddy simulation (LES). We, therefore, propose and validate a new subgrid-scale (SGS) model for such flows. The proposed SGS model consists of a dissipative eddy viscosity term…

流体动力学 · 物理学 2019-04-30 Maurits H. Silvis , H. Jane Bae , F. Xavier Trias , Mahdi Abkar , Roel Verstappen

This study proposes a multiscale convolutional neural network subgrid-scale (MSC-SGS) model for large-eddy simulation (LES). This model incorporates multiscale representations obtained via filtering to capture turbulent vortices…

流体动力学 · 物理学 2025-02-18 Bahrul Jalaali , Kie Okabayashi

We present two families of sub-grid scale (SGS) turbulence models developed for large-eddy simulation (LES) purposes. Their development required the formulation of physics-informed robust and efficient Deep Learning (DL) algorithms which,…

流体动力学 · 物理学 2023-07-20 Rikhi Bose , Arunabha M. Roy

We introduce a novel recursive process to a neural-network-based subgrid-scale (NN-based SGS) model for large eddy simulation (LES) of high Reynolds number turbulent flow. This process is designed to allow an SGS model to be applicable to a…

流体动力学 · 物理学 2024-12-04 Chonghyuk Cho , Jonghwan Park , Haecheon Choi

We live in an age in which high-performance computing is transforming the way we do science. Previously intractable problems are now becoming accessible by means of increasingly realistic numerical simulations. One of the most enduring and…

Due to the prohibitive cost of resolving all relevant scales, direct numerical simulations of turbulence remain unfeasible for most real-world applications. Consequently, dynamically simplified formulations are needed for coarse-grained…

流体动力学 · 物理学 2025-12-30 F. Xavier Trias , Jesús Ruano , Alexey Duben , Andrey Gorobets

By combining AI and fluid physics, we discover a closed-form closure for 2D turbulence from small direct numerical simulation (DNS) data. Large-eddy simulation (LES) with this closure is accurate and stable, reproducing DNS statistics…

大气与海洋物理 · 物理学 2026-01-21 Karan Jakhar , Yifei Guan , Pedram Hassanzadeh

In this review, the methodology of large eddy simulations (LES) is introduced and applications in astrophysics are discussed. As theoretical framework, the scale decomposition of the dynamical equations for compressible neutral fluids by…

星系天体物理 · 物理学 2025-09-09 Wolfram Schmidt-Brückner

There is growing interest in discovering interpretable, closed-form equations for subgrid-scale (SGS) closures/parameterizations of complex processes in Earth systems. Here, we apply a common equation-discovery technique with expansive…

流体动力学 · 物理学 2024-07-09 Karan Jakhar , Yifei Guan , Rambod Mojgani , Ashesh Chattopadhyay , Pedram Hassanzadeh

When simulating multiscale systems, where some fields cannot be fully prescribed despite their effects on the simulation's accuracy, closure models are needed. This phenomenon is observed in turbulent fluid dynamics, where Large Eddy…

流体动力学 · 物理学 2025-12-01 Eduardo Vital , Jean-Marc Gratien , Yassine Ayoun , Thibault Faney , Julien Bohbot

Direct numerical simulations of the incompressible Navier-Stokes equations are not feasible yet for most practical turbulent flows. Therefore, dynamically less complex mathematical formulations are necessary for coarse-grained simulations.…

流体动力学 · 物理学 2017-12-04 F. X. Trias , A. Gorobets , M. H. Silvis , R. W. C. P. Verstappen , A. Oliva
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