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In this paper, we have used renormalized viscosity for performing large eddy simulations (LES) of decaying homogeneous and isotropic turbulence inside a periodic cube on coarse grids ($32^3$, $64^3$ and $128^3$) at initial Taylor Reynolds…

Fluid Dynamics · Physics 2018-04-25 Sumit Vashishtha , Anando Chatterjee , Abhishek Kumar , Mahendra K Verma

Despite a cost-effective option in practical engineering, Reynolds-averaged Navier-Stokes simulations are facing the ever-growing demand for more accurate turbulence models. Recently, emerging machine learning techniques are making…

Fluid Dynamics · Physics 2021-05-04 Chao Jiang

In wall-modeled large-eddy simulations (WMLES), the near-wall model plays a significant role in predicting the skin friction, although the majority of the boundary layer is resolved by the outer large-eddy simulation (LES) solver. In this…

Fluid Dynamics · Physics 2020-10-09 Kevin Patrick Griffin , Lin Fu

Large eddy simulations and three-dimensional proper orthogonal decomposition were used to study the interaction between a large stationary and moving bluff body and a high Reynolds number stably-stratified turbulent boundary layer. An…

Fluid Dynamics · Physics 2019-01-23 John S. Haywood , Adrian Sescu

High Reynolds Homogeneous Isotropic Turbulence is fully described within the Navier-Stokes (NS) equations, which are notoriously difficult to solve numerically. Engineers, interested primarily in describing turbulence at a reduced range of…

The state-of-the-art in wind-farm flow-physics modeling is Large Eddy Simulation (LES) which makes accurate predictions of most relevant physics, but requires extensive computational resources. The next-fidelity model types are…

Fluid Dynamics · Physics 2021-01-19 Julia Steiner , Richard P. Dwight , Axelle Viré

Large Eddy Simulations (LES) are becoming increasingly viable due to the growth in computational power the last few decades, and subgrid stress modelling plays a large role in the accuracy of LES. A new class of neural network models, S4…

Fluid Dynamics · Physics 2025-11-17 Andy Wu , Sanjiva K. Lele

In large-eddy simulations, subgrid-scale (SGS) processes are parameterized as a function of filtered grid-scale variables. First-order, algebraic SGS models are based on the eddy-viscosity assumption, which does not always hold for…

Extending gradient-type turbulence closures to turbulent premixed flames is challenging due to the significant influence of combustion heat release. We incorporate a deep neural network (DNN) into Reynolds-averaged Navier--Stokes (RANS)…

Fluid Dynamics · Physics 2025-06-18 Priyesh Kakka , Jonathan F. MacArt

Hypersonic flow conditions pose exceptional challenges for Reynolds-Averaged Navier-Stokes (RANS) turbulence modeling. Critical phenomena include compressibility effects, shock/turbulent boundary layer interactions, turbulence-chemistry…

Fluid Dynamics · Physics 2025-04-30 Pratikkumar Raje , Eric Parish , Jean-Pierre Hickey , Paola Cinnella , Karthik Duraisamy

Recent advances in data-driven turbulence modeling have established tensor basis neural networks (TBNN) as a physically grounded framework for Reynolds-stress closure in Reynolds-averaged Navier-Stokes (RANS) simulations. However, their…

Fluid Dynamics · Physics 2026-04-13 Zelong Yuan , Yuzhu Pearl Li

The presence of nonlocal interactions and intermittent signals in the homogeneous isotropic turbulence grant multi-point statistical functions a key role in formulating a new generation of large-eddy simulation (LES) models of higher…

Fluid Dynamics · Physics 2023-06-22 Mehdi Samiee , Ali Akhavan-Safaei , Mohsen Zayernouri

In light of the challenges surrounding convergence and error propagation encountered in Reynolds-averaged Navier-Stokes (RANS) equations with data-driven Reynolds stress closures, researchers commonly attribute these issues to…

Fluid Dynamics · Physics 2024-05-07 Xianglin Shan , Wenbo Cao , Weiwei Zhang

Jin (Phys. Fluids, vol. 31, 2019, p. 125102) proposed a new turbulence simulation method which shows better performance than other classic turbulence models. It is composed of a small-eddy-dissipation mixing length (SED-ML) model for…

Fluid Dynamics · Physics 2022-01-27 Yan Jin

We present a new approach for constructing data-driven subgrid stress models for large eddy simulation of turbulent flows using anisotropic grids. The key to our approach is a Galilean, rotationally, reflectionally and unit invariant model…

Fluid Dynamics · Physics 2023-07-18 Aviral Prakash , Kenneth E. Jansen , John A. Evans

Buoyant shear layers are encountered in many engineering and environmental applications and have been studied by researchers in the context of experiments and modeling for decades. Often, these flows have high Reynolds and Richardson…

Fluid Dynamics · Physics 2021-07-27 Naman Jain , Hieu T. Pham , Xinyi Huang , Sutanu Sarkar , Xiang Yang , Robert Kunz

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…

Fluid Dynamics · Physics 2024-12-04 Chonghyuk Cho , Jonghwan Park , Haecheon Choi

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

The main objective of this work is to develop a unified framework that can be used as a lens to quantitatively assess and augment a wide range of coarse-grained models of turbulence, viz. large eddy simulations (LES), hybrid…

Fluid Dynamics · Physics 2023-01-25 Aniruddhe Pradhan , Karthik Duraisamy

This is the second part to our companion paper. The novel method to quantify artificial dissipation proposed in Part 1 is further applied in turbulent channel flow at $\mathrm{Re_\tau}=180$ using various subgrid-scale models, with an…

Fluid Dynamics · Physics 2025-03-03 Jing Sun , Roel Verstappen
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