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Complex turbulent flow simulations are an integral aspect of the engineering design process. The mainstay of these simulations is represented by eddy viscosity based turbulence models. Eddy viscosity models are computationally cheap due to…

Fluid Dynamics · Physics 2024-08-14 Minghan Chu , Weicheng Qian

We investigate the application of artificial neural networks to stabilize proper orthogonal decomposition based reduced order models for quasi-stationary geophysical turbulent flows. An extreme learning machine concept is introduced for…

Fluid Dynamics · Physics 2018-05-09 Omer San , Romit Maulik

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

In-air microfluidics became a new method for technical production processes with ultra-high throughput formerly performed in micro channels. Direct Numerical Simulations (DNS) provide a valuable contribution for the fundamental…

When reporting statistics from simulations of statistically stationary chaotic phenomenon, it is important to verify that the simulations are time-converged. This condition is connected with the statistical error or number of digits with…

Fluid Dynamics · Physics 2021-11-24 Yasaman Shirian , Jeremy Horwitz , Ali Mani

The performance of the nonlinearly stable flux reconstruction (NSFR) schemes for resolving subsonic viscous turbulent free-shear flows is investigated. The schemes are extensively verified for the direct numerical simulation (DNS) of the…

Fluid Dynamics · Physics 2024-11-20 Julien Brillon , Siva Nadarajah

Analyzing the effects of ocean eddies is important in oceanology for gaining insights into transport of energy and biogeochemical particles. We present an application of statistical visualization algorithms for the analysis of the Red Sea…

Graphics · Computer Science 2021-06-24 Tushar M. Athawale , Alireza Entezari , Bei Wang , Chris R. Johnson

Time series forecasting (TSF) is critical across domains such as finance, meteorology, and energy. While extending the lookback window theoretically provides richer historical context, in practice, it often introduces irrelevant noise and…

Machine Learning · Computer Science 2026-04-03 Xiang Ao , Yinyu Tan , Mengru Chen

A linear response function (LRF) that relates the temporal tendency of zonal mean temperature and zonal wind to their anomalies and external forcing is used to accurately quantify the strength of the eddy-jet feedback associated with the…

Fluid Dynamics · Physics 2017-01-05 Ding Ma , Pedram Hassanzadeh , Zhiming Kuang

The effect of grid resolution on large eddy simulation (LES) of wall-bounded turbulent flow is investigated. A channel flow simulation campaign involving systematic variation of the streamwise ($\Delta x$) and spanwise ($\Delta z$) grid…

Fluid Dynamics · Physics 2018-05-23 Saleh Rezaeiravesh , Mattias Liefvendahl

We present a novel moving immersed boundary method (IBM) and employ it in direct numerical simulations (DNS) of the closed-vessel swirling von Karman flow in laminar and turbulent regimes. The IBM extends direct-forcing approaches by…

Fluid Dynamics · Physics 2022-02-17 M. Houssem Kasbaoui , Tejas Kulkarni , Fabrizio Bisetti

This article describes some common issues encountered in the use of Direct Numerical Simulation (DNS) turbulent flow data for machine learning. We focus on two specific issues; 1) the requirements for a fair validation set, and 2) the…

We present a new spectral method for the Direct Numerical Simulation of Magnetohydrodynamic turbulence at low Magnetic Reynolds number. The originality of our approach is that instead of using traditional bases of functions, it relies on…

Fluid Dynamics · Physics 2011-11-15 Alban Pothérat , Vitali Dymkou

Modern datasets often contain high-dimensional features exhibiting complex dependencies. To effectively analyze such data, dimensionality reduction methods rely on estimating the dataset's intrinsic dimension (id) as a measure of its…

Machine Learning · Computer Science 2025-10-21 Dhruv Gupta , Aditya Nagarsekar , Vraj Shah , Sujith Thomas

Discrete flow models (DFMs) have been proposed to learn the data distribution on finite state space, offering a flexible framework as an alternative to discrete diffusion models. A line of recent work has studied samplers for discrete…

Machine Learning · Statistics 2026-05-28 Zhengyan Wan , Yidong Ouyang , Liyan Xie , Hongyuan Zha , Fang Fang , Guang Cheng

We present a new Eulerian framework for the computation of turbulent compressible multiphase channel flows, specifically to assess turbulence modulation by dispersed particulate matter in dilute concentrations but with significant mass…

Fluid Dynamics · Physics 2025-08-12 Ajay Dhankarghare , Yuval Dagan

The simulation of high Reynolds number (Re) separated turbulent flows faces significant problems for decades: large eddy simulation (LES) is computationally too expensive, and Reynolds-averaged Navier-Stokes (RANS) methods and hybrid…

Fluid Dynamics · Physics 2024-12-02 Stefan Heinz , Adeyemi Fagbade

This work improves upon our previously introduced explicit dynamic modal filter (DEMF) within the framework of the discontinuous Galerkin spectral element method (DGSEM) by introducing a mechanism for self-tuning of the model parameters.…

Fluid Dynamics · Physics 2025-12-04 Mohammadmahdi Ranjbar , Ali Mostafavi , Farzad Mashayek

In this study, ensembles of experimental data are presented and utilized to compare and validate two models used in the simulation of variable density, compressible turbulent mixing. Though models of this kind (Reynolds Averaged Navier…

Fluid Dynamics · Physics 2022-03-07 Benjamin Musci , Britton Olson , Samuel Petter , Gokul Pathikonda , Devesh Ranjan

Efficient molecular dynamics (MD) simulation is vital for understanding atomic-scale processes in materials science and biophysics. Traditional density functional theory (DFT) methods are computationally expensive, which limits the…

Machine Learning · Computer Science 2025-10-03 Hung Le , Sherif Abbas , Minh Hoang Nguyen , Van Dai Do , Huu Hiep Nguyen , Dung Nguyen