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The PANS equations model is used to predict the transitional Taylor-Green vortex (TGV) flow at Re=3000. A new form of the closure is proposed, in which the PANS procedure is applied to a variant of the BHR turbulence model. The TGV is a…

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…

流体动力学 · 物理学 2021-05-19 Karthik Duraisamy

With the advent of improved computational resources, aerospace design has testing-based process to a simulation-driven procedure, wherein uncertainties in design and operating conditions are explicitly accounted for in the design under…

流体动力学 · 物理学 2018-03-05 Aashwin Ananda Mishra , Jayant Mukhopadhaya , Gianluca Iaccarino , Juan Alonso

In this work, we investigate both numerically and theoretically the sound generated by entropy waves passing through sudden area expansions. The numerical approach is based on a triple decomposition of the flow variables into a steady mean,…

流体动力学 · 物理学 2022-09-28 Juan Guzmán-Iñigo , Dong Yang , Renaud Gaudron , Aimee S. Morgans

We investigate the prediction of the turbulent flow around a canonical square cylinder at Re= 22000 solving the unsteady Reynolds-averaged Navier-Stokes (URANS) equations. The limitations of URANS modelling are overcome through the…

流体动力学 · 物理学 2022-03-09 Markus Zauner , Vincent Mons , Olivier Marquet , Benjamin Leclaire

We determine the timescales associated with turbulent diffusion and isotropization in closure models using anisotropically forced and freely decaying turbulence simulations and to study the applicability of these models. We compare the…

太阳与恒星天体物理 · 物理学 2012-01-11 J. E. Snellman , A. Brandenburg , P. J. Käpylä , M. J. Mantere

With this study we investigate the accuracy of deep learning models for the inference of Reynolds-Averaged Navier-Stokes solutions. We focus on a modernized U-net architecture, and evaluate a large number of trained neural networks with…

机器学习 · 计算机科学 2020-10-20 Nils Thuerey , Konstantin Weissenow , Lukas Prantl , Xiangyu Hu

Design exploration or optimization using computational fluid dynamics (CFD) is commonly used in the industry. Geometric variation is a key component of such design problems, especially in turbulent flow scenarios, which involves running…

机器学习 · 计算机科学 2024-12-04 Shinjan Ghosh , Julian Busch , Georgia Olympia Brikis , Biswadip Dey

We present a novel framework inspired by the Immersed Boundary Method for predicting the fluid-structure interaction of complex structures immersed in flows with moderate to high Reynolds numbers. The main novelties of the proposed…

The development of advanced simulation tools is essential, both presently and in the future, for improving wind-energy design strategies, paving the way for a complete transition to sustainable solutions. The Reynolds-averaged Navier-Stokes…

流体动力学 · 物理学 2024-11-19 Ali Amarloo , Navid Zehtabiyan-Rezaie , Mahdi Abkar

This study proposes a newly-developed deep-learning-based method to generate turbulent inflow conditions for spatially-developing turbulent boundary layer (TBL) simulations. A combination of a transformer and a multiscale-enhanced…

流体动力学 · 物理学 2023-03-22 Mustafa Z. Yousif , Meng Zhang , Linqi Yu , Ricardo Vinuesa , HeeChang Lim

Although an increased availability of computational resources has enabled high-fidelity simulations of turbulent flows, the RANS models are still the dominant tools for industrial applications. However, the predictive capabilities of RANS…

流体动力学 · 物理学 2018-11-19 Jian-Xun Wang , Jinlong Wu , Julia Ling , Gianluca Iaccarino , Heng Xiao

Fan-array wind generators (FAWGs) provide controlled turbulent inflow conditions that cannot be reproduced in conventional wind tunnels. Despite their increasing use in experimental studies, numerical modeling of FAWG-generated flows…

流体动力学 · 物理学 2026-04-22 M. Hosein Niroomand , Utku Şentürk

Direct numerical simulation (DNS) is very accurate however, the computational cost increases significantly with the increase in Reynolds number. On the other hand, we have the Reynolds Averaged Navier Stokes (RANS) method for simulating…

流体动力学 · 物理学 2023-01-12 Manthan Mahajan , Nitin Kumar , Deep Shikha , Vamsi K Chalamalla , Sawan S Sinha

The complex small-scale statistics of turbulence are a result of the combined cascading dynamics through all scales of the flow. Predicting these statistics using fully resolved simulations at the high Reynolds numbers that typically occur…

流体动力学 · 物理学 2025-07-01 Lukas Bentkamp , Michael Wilczek

This work extends a pressure-gradient sensor for boundary-layer separation originally developed for the $k-\omega$ shear-stress transport Reynolds-averaged Navier-Stokes (RANS) model (Griffin et al., 2025, J. Turb.) to the Improved Delayed…

Despite its great scientific and technological importance, wall-bounded turbulence is an unresolved problem in classical physics that requires new perspectives to be tackled. One of the key strategies has been to study interactions among…

The development of turbulent mixing layers can be altered by the application of anisotropic strain rates, potentially arising from radial motion in convergent geometry or movement through non-uniform geometry. Previous closure models and…

流体动力学 · 物理学 2026-05-01 Bradley Pascoe , Michael Groom , Ben Thornber

We present a data-driven framework for turbulence modeling, applied to flow prediction in the FDA nozzle. In this study, the standard RANS equations have been modified using an implicit-explicit hybrid approach. New variables were…

流体动力学 · 物理学 2025-10-02 Hossein Geshani , Mehrdad Raisee Dehkordi , Masoud Shariat Panahi

A Direct Numerical Simulation (DNS) of the incompressible flow around a rectangular cylinder with chord-to-thickness ratio 5:1 (also known as the BARC benchmark) is presented. The work replicates the first DNS of this kind recently…

流体动力学 · 物理学 2021-05-04 Alessandro Chiarini , Maurizio Quadrio Politecnico di Milano
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