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The present work is concerned with a study of large eddy simulations (LES) of unsteady turbulent jet flows. In particular, the present analysis is focused on the effects of the subgrid-scale modeling used when a second-order spatial…

流体动力学 · 物理学 2022-12-26 Carlos Junqueira-Junior , Sami Yamouni , João Luiz F. Azevedo , William R. Wolf

In this paper a procedure for large-eddy simulation (LES) has been devised for fluid and magnetohydrodynamic turbulence in Fourier space using the renormalized parameters. The parameters calculated using field theory have been taken from…

混沌动力学 · 物理学 2009-11-10 Mahendra K. Verma , Shishir Kumar

We study the numerical errors of large-eddy simulation (LES) in isotropic and wall-bounded turbulence. A direct-numerical-simulation (DNS)-aided LES formulation, where the subgrid-scale (SGS) term of the LES is computed by using filtered…

流体动力学 · 物理学 2022-08-05 H. Jane Bae , Adrian Lozano-Duran

A unified subgrid-scale (SGS) and wall model for large-eddy simulation (LES) is proposed by devising the flow as a collection of building blocks that enables the prediction of the eddy viscosity. The core assumption of the model is that…

流体动力学 · 物理学 2022-12-20 Yuenong Ling , Gonzalo Arranz , Emily Williams , Konrad Goc , Kevin Griffin , Adrián Lozano-Durán

Wall-bounded turbulence is relevant for many engineering and natural science applications, yet there are still aspects of its underlying physics that are not fully understood, particularly at high Reynolds numbers. In this study, we…

流体动力学 · 物理学 2024-04-04 Himani Garg , Lei Wang , Martin Andersson , Christer Fureby

In isotropic helical turbulence, a new single helical model is suggested for large eddy simulation. Based on the Kolmogrov's hypotheses, the helical model is proposed according to the balance of helicity dissipation and the average of…

流体动力学 · 物理学 2015-06-12 Changping Yu

Even though compressible plasma turbulence is encountered in many astrophysical phenomena, its effect is often not well understood. Furthermore, direct numerical simulations are typically not able to reach the extreme parameters of these…

流体动力学 · 物理学 2016-07-27 P. Grete , D. G. Vlaykov , W. Schmidt , D. R. G. Schleicher

Assuming a general constitutive relation for the turbulent stresses in terms of the local large-scale velocity gradient, we constructed a class of subgrid-scale models for large-eddy simulation that are consistent with important physical…

流体动力学 · 物理学 2015-10-28 Maurits H. Silvis , Roel Verstappen

In large eddy simulation of atmospheric boundary layer flows over wind farms, wall-layer models are generally imposed for the surface fluxes without considering the spatial variability of the surface roughness. In this study, we consider…

流体动力学 · 物理学 2022-05-03 Jagdeep Singh , Jahrul Alam

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

In this work, important two-phase flow scalings are derived, which enable the quantification of grid-point and time-step requirements as functions of Re, We, and Ca numbers. The adequate grid resolution is determined in the…

流体动力学 · 物理学 2025-10-10 Luis H. Hatashita , Pranav Nathan , Suhas S. Jain

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

In this paper we present a new technique for efficiently implementing Large Eddy Simulation with the Discontin- uous Galerkin method on unstructured meshes. In particular, we will focus upon the approach to overcome the computational…

流体动力学 · 物理学 2016-10-24 Angus Creech , Adrian Jackson , James Maddison , James Percival , Tom Bruce

In this investigation, a data-driven turbulence closure framework is introduced and deployed for the sub-grid modelling of Kraichnan turbulence. The novelty of the proposed method lies in the fact that snapshots from high-fidelity numerical…

流体动力学 · 物理学 2018-11-14 Romit Maulik , Omer San , Adil Rasheed , Prakash Vedula

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

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

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,…

机器学习 · 计算机科学 2025-10-29 Yiheng Du , Aditi S. Krishnapriyan

Two widely used sub-grid scale models, the standard and the dynamic Smagorinsky models were tested in a simulation of the flow in a thermally driven 3D cavity at Rayleigh number of Ra=1E9. The main focus of the research is the response of…

流体动力学 · 物理学 2021-10-04 M. A. Sayed , A. Dehbi , M. Hadžiabić , B. Ničeno , K. Mikityuk

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

In wildfires, atmospheric turbulence plays a major role in the transfer of turbulent kinetic energy. Understanding how turbulence feeds back into a dynamical system is important, down to the varying small scales of fuel structures (i.e.…

流体动力学 · 物理学 2024-09-11 Dorianis M. Perez , Jesse M. Canfield , Rodman R. Linn , Kevin Speer