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The development of nuclear reactors that utilize pebble fuel has drastically increased the demand for improving the capabilities to simulate the packed beds found in these reactors. The complex flow fields found in a pebble bed make…

流体动力学 · 物理学 2022-03-11 David Reger , Elia Merzari , Paolo Balestra , Sebastian Schunert , Yassin Hassan , Haomin Yuan

Hybrid Reynolds-averaged Navier Stokes large eddy simulation (RANS LES) methods have become popular for simulation of massively separated flows at high Reynolds numbers due to their reduced computational cost and good accuracy. The current…

流体动力学 · 物理学 2021-02-19 Gaurav Kumar , Ashoke De , Harish Gopalan

Turbulent flows of viscoplastic fluids at high Reynolds numbers have been investigated recently with direct numerical simulations (DNS) but experimental results have been limited. For this reason, we carry out an experimental study of fully…

流体动力学 · 物理学 2022-07-19 Rodrigo S. Mitishita , Jordan A. MacKenzie , Gwynn J. Elfring , Ian A. Frigaard

The stratified inclined duct (SID) flow provides a canonical model for sustained, buoyancy-driven exchange between two reservoirs of different density, and emerges as a new paradigm in geophysical fluid dynamics. Yet, the flow dynamics…

流体动力学 · 物理学 2026-03-18 Rundong Zhou , Adrien Lefauve , Roberto Verzicco , Detlef Lohse

A numerical study for a hydrogen (H2) jet in an air crossflow (JICF) was performed using direct numerical simulation (DNS), large eddy simulation (LES), and Reynolds-averaged Navier-Stokes (RANS) approaches, based on a geometry…

流体动力学 · 物理学 2026-04-24 Yiqing Wang , Chao Xu , Riccardo Scarcelli , Ben Cantrell , Jon Anders , Sameera Wijeyakulasuriya

Direct Numerical Simulations (DNSs) are one of the most powerful tools for studying turbulent flows. Even if achievable Reynolds numbers are lower than those obtained with experimental means, there is a clear advantage since the entire…

流体动力学 · 物理学 2024-06-03 Sergio Hoyas , Ricardo Vinuesa , Peter Schmid , Hassan Nagib

This review focuses on Direct numerical simulations (DNS) of turbulent flows laden with droplets or bubbles. DNS of these flows are more challenging than those of flows laden with solid particles due to the surface deformation in the…

流体动力学 · 物理学 2018-04-10 Said Elghobashi

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

We perform direct numerical simulations of a kerosene-water mixture in pipe flow under realistic experimental conditions by solving the Cahn-Hilliard-Navier-Stokes equations. We compute the linear stability of core-annular flow of kerosene…

流体动力学 · 物理学 2021-08-13 Carlos Plana , Baofang Song , Marc Avila

The partially-averaged Navier-Stokes (PANS) equations are used to predict the variable-density Rayleigh-Taylor (RT) flow at Atwood number 0.5 and maximum Reynolds number $500$. This is a prototypical problem of material mixing featuring…

Results of numerical simulations obtained by a staggered finite difference scheme together with an efficient immersed boundary method are presented to understand the effects of the shape of three-dimensional obstacles on the transition of a…

流体动力学 · 物理学 2015-03-31 Paolo Orlandi , Sergio Pirozzoli , Matteo Bernardini

Magnetic Resonance Imaging (MRI) experiments have been performed in conjunction with Direct Numerical Simulations (DNS) to study neutrally buoyant particle-laden pipe flows. The flows are characterized by the suspension liquid Reynolds…

Turbulent flows beneath a free surface play a central role in the Earth system, yet their coupling to observable surface features remains incompletely understood. Recent studies using Direct Numerical Simulations (DNS) have reported strong…

We report on a investigation of turbulent bubbly flows. Bubbles of a size larger than the dissipative scale, cannot be treated as point-wise inclusions, and generate important hydrodynamic fields in the carrier fluid when in motion.…

流体动力学 · 物理学 2021-12-17 Alessio Innocenti , Alice Jaccod , Stéphane Popinet , Sergio Chibbaro

Triboelectric charging in particle-laden flows is a complex interplay of fluid and particle dynamics, collision mechanics, and electrostatics. In this study, we introduce triboFoam, an open-source solver built on the OpenFOAM framework,…

流体动力学 · 物理学 2026-02-12 Christoph Wilms , Holger Grosshans

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

The flow in a shock tube is extremely complex with dynamic multi-scale structures of sharp fronts, flow separation, and vortices due to the interaction of the shock wave, the contact surface, and the boundary layer over the side wall of the…

流体动力学 · 物理学 2018-01-17 Guangzhao Zhou , Kun Xu , Feng Liu

We study the turbulent square duct flow of dense suspensions of neutrally-buoyant spherical particles. Direct numerical simulations (DNS) are performed in the range of volume fractions $\phi=0-0.2$, using the immersed boundary method (IBM)…

流体动力学 · 物理学 2018-08-15 Walter Fornari , Hamid Tabaei Kazerooni , Jeanette Hussong , Luca Brandt

High resolution simulations of incompressible flows have become routine across a range of engineering applications. Despite their routine use, due to the high dimensional parameter space present for most practical applications, a…

流体动力学 · 物理学 2022-11-14 Christopher J. McDevitt , Eric Fowler , Subrata Roy

Dolphin swimming has been a captivating area of study, yet the hydrodynamics of the dorsal fin remain underexplored. In this study, we present three-dimensional simulations of flow around a wall-mounted dolphin dorsal fin, derived from a…

流体动力学 · 物理学 2024-09-09 Zhonglu Lin , An-Kang Gao , Yu Zhang