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A new flamelet model is developed for sub-grid modeling and coupled with the resolved flow for turbulent combustion. The model differs from current models in critical ways. (i) Non-premixed flames, premixed flames, or multi-branched flame…

Fluid Dynamics · Physics 2022-08-10 William A. Sirignano

This paper presents a novel generative model to synthesize fluid simulations from a set of reduced parameters. A convolutional neural network is trained on a collection of discrete, parameterizable fluid simulation velocity fields. Due to…

Machine Learning · Computer Science 2019-09-05 Byungsoo Kim , Vinicius C. Azevedo , Nils Thuerey , Theodore Kim , Markus Gross , Barbara Solenthaler

We present a wall model for large-eddy simulation that incorporates surface-roughness effects and is applicable across low- and high-speed flows, for both transitional and fully rough conditions. The model, implemented using an artificial…

Fluid Dynamics · Physics 2026-01-29 Rong Ma , Adrian Lozano-Duran

Direct Numerical Simulation data obtained earlier from two statistically 1D, planar, fully-developed, weakly turbulent, single-step-chemistry, premixed flames characterized by two significantly different (7.53 and 2.50) density ratios…

The turbulence kinetic energy dissipation rate $\epsilon$, from a turbulent combustion computation using either Reynolds-averaged Navier-Stokes (RANS) or large-eddy simulation (LES), is proposed for closure with a sub-grid non-premixed…

Fluid Dynamics · Physics 2026-04-01 William A. Sirignano , Wes Hellwig , Sylvain L. Walsh

We study the error scaling properties of large-eddy simulation (LES) in the outer region of wall-bounded turbulence at moderately high Reynolds numbers. In order to avoid the additional complexity of wall-modeling, we perform LES of…

Fluid Dynamics · Physics 2021-10-26 Adrián Lozano-Durán , Hyunji Jane Bae

This work presents a review and perspectives on recent developments in the use of machine learning (ML) to augment Reynolds-averaged Navier--Stokes (RANS) and Large Eddy Simulation (LES) models of turbulent flows. Different approaches of…

Fluid Dynamics · Physics 2021-05-19 Karthik Duraisamy

Starting with an integral formulation of mass flow rate through an ensemble of isotherms constituting a statistically planar, turbulent premixed flame, a scaling for the corresponding turbulent flame speed is derived without invoking…

Fluid Dynamics · Physics 2022-07-14 Swetaprovo Chaudhuri , Bruno Savard

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…

Fluid Dynamics · Physics 2024-04-04 Himani Garg , Lei Wang , Martin Andersson , Christer Fureby

The complex evolution of turbulent mixing in Rayleigh-Taylor convection is studied in terms of eddy diffusiviy models for the mean temperature profile. It is found that a non-linear model, derived within the general framework of Prandtl…

Chaotic Dynamics · Physics 2015-05-18 G. Boffetta , F. De Lillo , S. Musacchio

This work presents a converged framework of Machine-Learning Assisted Turbulence Modeling (MLATM). Our objective is to develop a turbulence model directly learning from high fidelity data (DNS/LES) with eddy-viscosity hypothesis induced.…

Fluid Dynamics · Physics 2019-07-09 Weishuo Liu , Jian Fang , Stefano Rolfo , Lipeng Lu

Implementing multicomponent diffusion models in numerical combustion studies is computationally expensive; to reduce cost, numerical simulations commonly use mixture-averaged diffusion treatments or simpler models. However, the accuracy and…

Fluid Dynamics · Physics 2020-10-19 Aaron J. Fillo , Jason Schlup , Guillaume Blanquart , Kyle E. Niemeyer

For decades, supercritical flame simulations incorporating detailed chemistry and real-fluid transport have been limited to millions of cells, constraining the resolved spatial and temporal scales of the physical system. We optimize the…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-08-27 Zhuoqiang Guo , Runze Mao , Lijun Liu , Guangming Tan , Weile Jia , Zhi X. Chen

The robotic systems continuously interact with complex dynamical systems in the physical world. Reliable predictions of spatiotemporal evolution of these dynamical systems, with limited knowledge of system dynamics, are crucial for…

Artificial Intelligence · Computer Science 2019-01-08 Yun Long , Xueyuan She , Saibal Mukhopadhyay

A dynamic mixed super-resolution model (DMSRM) for large-eddy simulations (LESs) is proposed, which combines the traditional dynamic mixed model (DMM) formulation with the generation of super-resolved velocity fields from which the…

Tabulated chemistry methods are a well-known strategy to efficiently store the flows thermochemical properties. In particular, the Flamelet-Generated Manifold (FGM) is a widely used technique that generates the database with a small number…

Fluid Dynamics · Physics 2024-11-21 Alessandro Ballatore , Diego Quan Reyes , Hesheng Bao , Jeroen van Oijen

An online Data Assimilation strategy based on the Ensemble Kalman Filter (EnKF) is used to improve the predictive capabilities of Large Eddy Simulation (LES) for the analysis of the turbulent flow in a plane channel, $Re_\tau \approx 550$.…

Fluid Dynamics · Physics 2023-10-30 Lucas Villanueva , Karine Truffin , Marcello Meldi

Stratified premixed combustion, known for its capability to expand flammability limits and reduce overall-lean combustion instability, has been widely adopted to comply with increasingly stringent environmental regulations. Numerous…

Fluid Dynamics · Physics 2023-12-12 Min Zhang , Runze Mao , Han Li , Ruixin Yang , Zhi X. Chen

In this study, we conduct a parametric analysis to evaluate the sensitivities of wall-modeled large-eddy simulation (LES) with respect to subgrid-scale (SGS) models, mesh resolution, wall boundary conditions and mesh anisotropy. While such…

Fluid Dynamics · Physics 2024-03-26 Di Zhou , H. Jane Bae

In this paper, a turbulence model based on deep neural network is developed for turbulent flow around airfoil at high Reynolds numbers. According to the data got from the Spalart-Allmaras (SA) turbulence model, we build a neural network…

Fluid Dynamics · Physics 2021-11-29 Xuxiang Sun , Wenbo Cao , Yilang Liu , Linyang Zhu , Weiwei Zhang
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