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While deep learning has shown tremendous success in a wide range of domains, it remains a grand challenge to incorporate physical principles in a systematic manner to the design, training, and inference of such models. In this paper, we aim…

Computational Physics · Physics 2020-06-16 Rui Wang , Karthik Kashinath , Mustafa Mustafa , Adrian Albert , Rose Yu

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…

Astrophysics of Galaxies · Physics 2025-09-09 Wolfram Schmidt-Brückner

Simulating massively separated turbulent flows over bodies is one of the major applications for large-eddy simulation (LES). In the current work, we propose a machine-learning-based LES framework for the rapid simulation of turbulent flows…

Fluid Dynamics · Physics 2026-03-17 Yunpeng Wang , Huiyu Yang , Zelong Yuan , Zhijie Li , Wenhui Peng , Jianchun Wang

The inner-outer interaction model (Marusic, Mathis & Hutchins, Science, vol. 329, 2010, 193-196) and the attached-eddy model (Townsend, Cambridge University Press, 1976) are two fundamental models describing the multi-scale turbulence…

Fluid Dynamics · Physics 2022-05-17 Cheng Cheng , Lin Fu

This work proposes a data-driven explicit algebraic stress-based detached-eddy simulation (DES) method. Despite the widespread use of data-driven methods in model development for both Reynolds-averaged Navier-Stokes (RANS) and large-eddy…

Fluid Dynamics · Physics 2026-01-14 Hao-Chen Liu , Zifei Yin , Xin-Lei Zhang , Guowei He

Reliably predictive simulation of complex flows requires a level of model sophistication and robustness exceeding the capabilities of current Reynolds-averaged Navier-Stokes (RANS) models. The necessary capability can often be provided by…

Fluid Dynamics · Physics 2022-01-20 Sigfried W. Haering , Todd A. Oliver , Robert D. Moser

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

A new modeling approach for large-eddy simulation (LES) is obtained by combining a `regularization principle' with an explicit filter and its inversion. This regularization approach allows a systematic derivation of the implied…

Chaotic Dynamics · Physics 2009-11-07 Bernard J. Geurts , Darryl D. Holm

A large eddy simulation wall model is developed based on a formal interpretation of quasi-equilibrium that governs the momentum balance integrated in the wall-normal direction. The model substitutes the law-of-the-wall velocity profile for…

Fluid Dynamics · Physics 2022-02-02 Mitchell Fowler , Tamer A. Zaki , Charles Meneveau

This article proposes a Reynolds number scaling of the required grid points to perform wall-modeled LES of turbulent flows encountering separation off a solid surface. Based on comparisons between the various time scales in a…

Fluid Dynamics · Physics 2024-01-02 Rahul Agrawal , Sanjeeb Bose , Parviz Moin

Data-driven approaches offer novel opportunities for improving the performance of turbulent flow simulations, which are critical to wide-ranging applications from wind farms and aerodynamic designs to weather and climate forecasting. While…

Fluid Dynamics · Physics 2024-02-14 Xiao Xue , Shuo Wang , Hua-Dong Yao , Lars Davidson , Peter V. Coveney

The $\alpha$-modeling strategy is followed to derive a new subgrid parameterization of the turbulent stress tensor in large-eddy simulation (LES). The LES-$\alpha$ modeling yields an explicitly filtered subgrid parameterization which…

Chaotic Dynamics · Physics 2007-05-23 Bernard J. Geurts , Darryl D. Holm

We propose a novel model to obtain the subgrid-scale velocity in the context of large-eddy simulation (LES) of particle-laden turbulent flows, to recover accurate particle statistics. In the new wavelet enrichment model, the subgrid-scale…

Fluid Dynamics · Physics 2023-10-26 Max Hausmann , Fabien Evrard , Berend van Wachem

Turbulent flow over a rounded broad-crested weir is investigated by means of detached eddy simulation (DES) and large eddy simulation (LES) with special emphasis on the interaction of coherent vortex structures with free-surface. In order…

Fluid Dynamics · Physics 2023-05-03 M. Anil Kizilaslan , Elif Deniz Atlas , Huseyin Yaban , Ender Demirel

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.…

Fluid Dynamics · Physics 2023-09-13 Jhon Cordova , Cesar Celis , Andres Mendiburu , Luis Bravo , Prashant Khare

Wall-bounded turbulence is characterized by coherent, worm-like structures such as hairpin vortices. The attached-eddy model provides a successful statistical framework for the log-law region, yet the complex geometry and multiscale nature…

Fluid Dynamics · Physics 2026-04-15 Weiyu Shen , Yuchen Ge , Zishuo Han , Yaomin Zhao , Yue Yang

In many engineering and industrial applications, the investigation of rotating turbulent flow is of great interest. In rotor-stator cavities, the centrifugal and Coriolis forces have a strong influence on the turbulence by producing a…

We develop an enrichment wall-model within the spectral element method (SEM) framework for large-eddy simulations of wall-bounded flows. The method augments the polynomial solution in the wall-adjacent elements with an analytical…

Fluid Dynamics · Physics 2024-11-22 Steven R. Brill , Pinaki Pal , Muhsin Ameen , Chao Xu , Matthias Ihme

The variance and spectra of wall-normal velocities are investigated for direct numerical simulations of turbulent flow in a channel, pipe, and zero-pressure-gradient boundary layer across a decade of friction Reynolds numbers. Spectra along…

Fluid Dynamics · Physics 2026-04-22 Michael Heisel , Rahul Deshpande , Gabriel G. Katul