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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.…

Fluid Dynamics · Physics 2017-12-04 F. X. Trias , A. Gorobets , M. H. Silvis , R. W. C. P. Verstappen , A. Oliva

Based on the characteristics of the multi-scale and similarity at different scales in turbulent flow, we propose a scale decomposition for solving the turbulence problem of incompressible Newtonian fluid. The solution domain is decomposed…

Fluid Dynamics · Physics 2023-02-21 Shanwen Tan

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…

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…

Fluid Dynamics · Physics 2021-11-16 J P Panda

One of the more promising recent approaches to turbulence modelling is the Variational Multiscale Large Eddy Simulation (VMS LES) method proposed by Hughes et al. [Comp. Visual. Sci., vol. 3, pp. 47-59, 2000]. This method avoids several…

Computational Physics · Physics 2007-05-23 Thor Gjesdal , Carl Erik Wasberg , Bjorn Anders Pettersson Reif , Oyvind Andreassen

Filtering is concerned with the sequential estimation of the state, and uncertainties, of a Markovian system, given noisy observations. It is particularly difficult to achieve accurate filtering in complex dynamical systems, such as those…

Probability · Mathematics 2015-12-14 Wonjung Lee , Andrew Stuart

In this article we detail the use of machine learning for spatiotemporally dynamic turbulence model classification and hybridization for the large eddy simulations (LES) of turbulence. Our predictive framework is devised around the…

Fluid Dynamics · Physics 2019-05-15 Romit Maulik , Omer San , Jamey D. Jacob , Christopher Crick

A new method for the localization of the regions where small scale turbulent fluctuations are present in hypersonic flows is applied to the large-eddy simulation (LES) of a compressible turbulent jet with an initial Mach number equal to 5.…

Fluid Dynamics · Physics 2015-06-12 D. Tordella , M. Iovieno , S. Massaglia , A. Mignone

Turbulence modeling has the potential to revolutionize high-speed vehicle design by serving as a co-equal partner to costly and challenging ground and flight testing. However, the fundamental assumptions that make turbulence modeling such…

Fluid Dynamics · Physics 2023-04-18 Chitrarth Prasad , Datta V. Gaitonde

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…

Fluid Dynamics · Physics 2025-02-18 Bahrul Jalaali , Kie Okabayashi

All hydrodynamical simulations of turbulent astrophysical phenomena require sub-grid scale models to properly treat energy dissipation and metal mixing. We present the first implementation and application of an anisotropic eddy viscosity…

Astrophysics of Galaxies · Physics 2021-07-02 Douglas Rennehan

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…

We extend the data-assimilation approach of Ling and Lozano-Dur\'an (AIAA 2025-1280) to develop machine-learning-based subgrid-scale stress (SGS) models for large-eddy simulation (LES) that are consistent with the numerical scheme of the…

Fluid Dynamics · Physics 2026-01-29 Yuenong Ling , Adrián Lozano-Durán

We developed a novel autonomously dynamic nonlocal turbulence model for the large and very large eddy simulation (LES, VLES) of the homogeneous isotropic turbulent flows (HIT). The model is based on a generalized (integer-to-noninteger)…

Fluid Dynamics · Physics 2022-03-07 S. Hadi Seyedi , Mohsen Zayernouri

FEARLESS (Fluid mEchanics with Adaptively Refined Large Eddy SimulationS) is a numerical scheme for modelling subgrid-scale turbulence in cosmological adaptive mesh refinement simulations. In this contribution, the main features of this…

Cosmology and Nongalactic Astrophysics · Physics 2012-10-18 L. Iapichino , W. Schmidt , J. C. Niemeyer , J. Merklein

We study the sub-grid scale characteristics of a vorticity-transport-based approach for large-eddy simulations. In particular, we consider a multi-dimensional upwind scheme for the vorticity transport equations and establish its properties…

Fluid Dynamics · Physics 2021-02-05 Daniel Foti , Karthik Duraisamy

In the present study, we investigate different data-driven parameterizations for large eddy simulation of two-dimensional turbulence in the \emph{a priori} settings. These models utilize resolved flow field variables on the coarser grid to…

Computational Physics · Physics 2020-01-29 Suraj Pawar , Omer San , Adil Rasheed , Prakash Vedula

In the quest for advanced propulsion and power-generation systems, high-fidelity simulations are too computationally expensive to survey the desired design space, and a new design methodology is needed that combines engineering physics,…

We propose a new model of turbulence for use in large-eddy simulations (LES). The turbulent force, represented here by the turbulent Lamb vector, is divided in two contributions. The contribution including only subfilter fields is…

Fluid Dynamics · Physics 2009-11-11 Jean-Philippe Laval , Berengere Dubrulle

We discuss a theoretical framework to define an optimal sub-grid closure for shell models of turbulence. The closure is based on the ansatz that consecutive shell multipliers are short-range correlated, following the third hypothesis of…

Fluid Dynamics · Physics 2017-04-26 Luca Biferale , Alexei A. Mailybaev , Giorgio Parisi