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Coarse graining enables the investigation of molecular dynamics for larger systems and at longer timescales than is possible at atomic resolution. However, a coarse graining model must be formulated such that the conclusions we draw from it…

A significant portion of the data today, e.g, social networks, web connections, etc., can be modeled by graphs. A proper analysis of graphs with Machine Learning (ML) algorithms has the potential to yield far-reaching insights into many…

Social and Information Networks · Computer Science 2020-09-11 Taha Atahan Akyildiz , Amro Alabsi Aljundi , Kamer Kaya

We review progress that has been made in utilizing one form of Direct Statistical Simulation (DSS) to describe geophysical and astrophysical flows that are anisotropic and inhomogeneous. We first explain the approach, which is based upon a…

Fluid Dynamics · Physics 2019-03-19 J. B. Marston , Wanming Qi , S. M. Tobias

In order to enable simulations of developing wind turbine array boundary layers with highly realistic inflow conditions a concurrent precursor method for Large Eddy Simulations is proposed. In this method we consider two domains…

Fluid Dynamics · Physics 2014-05-06 Richard J. A. M. Stevens , Jason Graham , Charles Meneveau

The computational study of strongly-coupled, gas-solid flows at scales relevant to most environmental and engineering applications requires the use of `coarse-grained' methodologies such as the two-fluid model, particle-in-cell approach or…

Fluid Dynamics · Physics 2025-09-16 Lee Rosenberg , William Fullmer , Sarah Beetham

A new methodology based on energy flux similarity is suggested in this paper for large eddy simulation (LES) of transitional and turbulent flows. Existing knowledge reveals that the energy cascade generally exists in transitional and…

Fluid Dynamics · Physics 2019-10-31 Han Qi , Xinliang Li , Hao Zhou , Changping Yu

Current design constraints have encouraged the studies of aeroacoustic fields around compressible jet flows. The present work addresses the numerical study of unsteady turbulent jet flows as a preparation for future aeroacoustic analyses of…

Fluid Dynamics · Physics 2023-01-13 Sami Yamouni , Carlos Junqueira-Junior , Joao Luiz F. Azevedo , William R. Wolf

We test a subgrid-scale spectral model of rotating turbulent flows against direct numerical simulations. The particular case of Taylor-Green forcing at large scale is considered, a configuration that mimics the flow between two counter…

Fluid Dynamics · Physics 2008-12-11 J. Baerenzung , P. D. Mininni , A. Pouquet , H. Politano , Y. Ponty

The accuracy of a large eddy simulation (LES) is determined by the accuracy of the model used to describe the effect of unresolved scales, the numerical errors of the resolved scales, and the optimality of the length scale that separates…

Computational Physics · Physics 2019-12-11 Siavash Toosi , Johan Larsson

A promising and cost-effective method for numerical simulation of high Re wall-bounded flows is wall-modeled large-eddy simulation. Most wall models are formulated from the Reynolds-averaged Navier-Stokes equations (RANS). These RANS-based…

Fluid Dynamics · Physics 2021-01-08 Ahmed Elnahhas , Adrián Lozano-Durán , Parviz Moin

We develop a numerical method for simulation of incompressible viscous flows by integrating the technology of random vortex method with the core idea of Large Eddy Simulation (LES). Specifically, we utilize the filtering method in LES,…

Fluid Dynamics · Physics 2024-10-08 Zihao Guo , Zhongmin Qian

For wall-bounded turbulent flows, Townsend's attached eddy hypothesis proposes that the logarithmic layer is populated by a set of energetic and geometrically self-similar eddies. These eddies scale with a single length scale, their…

Fluid Dynamics · Physics 2019-11-26 Leo Hellström , Tyler Van Buren , John Vaccaro , Alexander J. Smits

The primary objective of this work is to develop coarse-graining schemes for stochastic many-body microscopic models and quantify their effectiveness in terms of a priori and a posteriori error analysis. In this paper we focus on stochastic…

Numerical Analysis · Mathematics 2007-05-23 Markos A. Katsoulakis , Petr Plechac , Luc Rey-Bellet , Dimitrios K. Tsagkarogiannis

Computationally resolving turbulence remains a central challenge in fluid dynamics due to its multi-scale interactions. Fully resolving large-scale turbulence through direct numerical simulation (DNS) is computationally prohibitive,…

Machine Learning · Computer Science 2025-10-29 Yiheng Du , Aditi S. Krishnapriyan

We use an "equation-free", coarse-grained computational approach to accelerate molecular dynamics-based computations of demixing (segregation) of dissimilar particles subject to an upward gas flow (gas-fluidized beds). We explore the…

Soft Condensed Matter · Physics 2009-11-11 Sung Joon Moon , S. Sundaresan , I. G. Kevrekidis

A filtered density function (FDF) model based on deep neural network (DNN), termed DNN-FDF, is introduced for large eddy simulation (LES) of turbulent flows involving conserved scalar transport. The primary objectives of this study are to…

Fluid Dynamics · Physics 2023-10-02 Shubhangi Bansude , Reza Sheikhi

Simulations of stochastically forced shear-flow turbulence in a shearing-periodic domain are used to study the spontaneous generation of large-scale flow patterns in the direction perpendicular to the plane of the shear. Based on an…

Astrophysics · Physics 2009-01-09 Petri J. Käpylä , Dhrubaditya Mitra , Axel Brandenburg

We study a mathematical model of a perturbed stratified shear mean flow in the presence of eddy coefficients of turbulent viscosity. We adopt the standard Boussinesq approximation in the natural convection of the buoyancy-driven flow and…

Atmospheric and Oceanic Physics · Physics 2024-06-19 N. Karjanto

The LES/PDF methodology is applied to the Cambridge/Sandia turbulent stratified flame series under swirling conditions. The methane/air chemistry is represented by a 16-species reduced ARM1 mechanism, and the in situ adaptive tabulation…

Fluid Dynamics · Physics 2020-12-04 Hasret Turkeri , Xinyu Zhao , Metin Muradgolu