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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

In this paper, we train turbulence models based on convolutional neural networks. These learned turbulence models improve under-resolved low resolution solutions to the incompressible Navier-Stokes equations at simulation time. Our study…

Fluid Dynamics · Physics 2022-10-12 Björn List , Li-Wei Chen , Nils Thuerey

The present research proposes a new memory-efficient method using diffusion models to inject turbulent inflow conditions into Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) for various flow problems. A guided diffusion…

The emergence of large-scale spatial modulations of turbulent channel flow, as the Reynolds number is decreased, is addressed numerically using the framework of linear stability analysis. Such modulations are known as the precursors of…

Current design constraints have encouraged the studies of aeroacoustics fields around compressible jet flows. The present work addresses the numerical study of subgrid scale modeling for unsteady turbulent jet flows as a preliminary step…

Fluid Dynamics · Physics 2023-01-03 Carlos Junqueira-Junior , Sami Yamouni , Joao Luiz F. Azevedo , William Wolf

Turbulence simulation with classical numerical solvers requires high-resolution grids to accurately resolve dynamics. Here we train learned simulators at low spatial and temporal resolutions to capture turbulent dynamics generated at high…

We describe a simple and systematic method for obtaining approximate sensitivity information from a chaotic dynamical system using a hierarchy of cumulant equations. The resulting forward and adjoint systems yield information about…

Chaotic Dynamics · Physics 2018-06-26 John Craske

We consider the question of fundamental limitations on the performance of eddy-viscosity closure models for turbulent flows, focusing on the Leith model for 2D {Large-Eddy Simulation}. Optimal eddy viscosities depending on the magnitude of…

Fluid Dynamics · Physics 2022-03-29 Pritpal Matharu , Bartosz Protas

A sensitive porosity adjoint method (SPAM) for optimizing the topology of fluid machines has been proposed. A sensitivity function with respect to the porosity has been developed. In the first step of the optimization process, porous media…

Fluid Dynamics · Physics 2015-12-29 B. Philippi , Y. Jin

Direct numerical simulations of free round jets at a Reynolds number ($Re_{D}$) of $5000$, based on jet diameter ($D$) and jet-exit bulk velocity ($U_{e}$), are performed to study jet turbulence characteristics at supercritical pressures.…

Fluid Dynamics · Physics 2021-08-04 Nek Sharan , Josette Bellan

Direct numerical simulations (DNS) are an indispensable tool for understanding the fundamental physics of turbulent flows. Because of their steep increase in computational cost with Reynolds number ($R_{\lambda}$), well-resolved DNS are…

Computational Physics · Physics 2020-08-26 Komal Kumari , Diego A. Donzis

Transition to turbulence dramatically alters the properties of fluid flows. In most canonical shear flows, the laminar flow is linearly stable and a finite-amplitude perturbation is necessary to trigger transition. Controlling transition to…

Fluid Dynamics · Physics 2020-05-20 Anton Pershin , Cedric Beaume , Steven M. Tobias

Large Eddy Simulations of turbulent flows are powerful tools used in many engineering and geophysical settings. Choosing the right value of the free parameters for their subgrid scale models is a crucial task for which the current methods…

Fluid Dynamics · Physics 2021-02-03 M. Buzzicotti , P. Clark Di Leoni

Simulating complex gas flows from turbulent to rarefied regimes is a long-standing challenge, since turbulence and rarefied flow represent contrasting extremes of computational aerodynamics. We propose a multiscale method to bridge this…

Fluid Dynamics · Physics 2024-08-16 Songyan Tian , Lei Wu

Supersonic shear layers experience instabilities that generate significant adverse effects; in complex configurations, these instabilities have global impacts as they foster compounding complications with other independent flow features. We…

Fluid Dynamics · Physics 2024-08-19 Melissa Yeung , Datta V. Gaitonde , Yiyang Sun

Novel experimental modalities acquire spatially resolved velocity measurements for steady state and transient flows which are of interest for engineering and biological applications. One of the drawbacks of such high resolution velocity…

Numerical Analysis · Mathematics 2015-12-31 H. Egger , T. Seitz , C. Tropea

We investigate the reconstruction of a turbulent flow field in the atmospheric boundary layer from a time series of lidar measurements, using Large-Eddy Simulations (LES) and a 4D-Var data assimilation algorithm. This leads to an…

Fluid Dynamics · Physics 2020-11-17 Pieter Bauweraerts , Johan Meyers

New regulations are imposing noise emissions limitations for the aviation industry which are pushing researchers and engineers to invest efforts in studying the aeroacoustics phenomena. Following this trend, an in-house computational fluid…

Distributed, Parallel, and Cluster Computing · Computer Science 2020-03-24 Carlos Junqueira-Junior , João Luiz F. Azevedo , Jairo Panetta , William R. Wolf , Sami Yamouni

We consider linear feedback flow control of the largest scales in an incompressible turbulent channel flow at a friction Reynolds number of Re$_{\tau}$ = 2000. A linear model is formed by linearizing the Navier-Stokes equations about the…

Fluid Dynamics · Physics 2020-10-20 Stephan F. Oehler , Simon J. Illingworth

Inverse design of complex flows is notoriously challenging because of the high cost of high dimensional optimization. Usually, optimization problems are either restricted to few control parameters, or adjoint-based approaches are used to…

Fluid Dynamics · Physics 2024-03-12 Mohammed Alhashim , Kaylie Hausknecht , Michael Brenner
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