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To fully evaluate a turbulent flow, Direct Numerical Simulation (DNS) is the most accurate method by far and requires considerable computational power and time; not optimum for industry standards. Developing an alternative model, providing…

流体动力学 · 物理学 2022-07-04 Indrajit Nandi , Saikat Saha , Sabir Subedi , Sumon Saha

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

In many engineering and industrial applications, the investigation of rotating turbulent flow is of great interest. In rotor-stator cavities, the centrifugal and Coriolis forces have a strong influence on the turbulence by producing a…

This chapter provides an introduction to data-driven techniques for the development and calibration of closure models for the Reynolds-Averaged Navier--Stokes (RANS) equations. RANS models are the workhorse for engineering applications of…

流体动力学 · 物理学 2024-04-16 Paola Cinnella

This research computationally investigates the complex dynamic stall phenomena of a cross-flow turbine blade utilizing modal analysis to identify pertinent events within the cycle. The blade rotation perpendicular to the freestream…

流体动力学 · 物理学 2023-09-13 Mukul Dave , Jennifer A. Franck

Numerical simulations based on Reynolds-Averaged Navier--Stokes (RANS) equations are widely used in engineering design and analysis involving turbulent flows. However, RANS simulations are known to be unreliable in many flows of engineering…

流体动力学 · 物理学 2017-09-19 Jinlong Wu , Rui Sun , Sylvain Laizet , Heng Xiao

Reynolds Averaged Navier Stokes (RANS) models represent the workhorse for studying turbulent flows in industrial applications. Such single-point turbulence models have limitations in accounting for the influence of the non-local physics and…

流体动力学 · 物理学 2017-04-19 K. Duraisamy , Anand A. , G. Iaccarino

Computational fluid dynamics using the Reynolds-averaged Navier-Stokes (RANS) remains the most cost-effective approach to study wake flows and power losses in wind farms. The underlying assumptions associated with turbulence closures are…

流体动力学 · 物理学 2022-08-03 Ali Eidi , Navid Zehtabiyan-Rezaie , Reza Ghiassi , Xiang Yang , Mahdi Abkar

In the present paper, an aerodynamic investigation of a high-speed train is performed. In the first section of this article, a generic high-speed train against a turbulent flow is simulated, numerically. The Reynolds-Averaged Navier-Stokes…

We carry out Direct Numerical Simulation (DNS) of flows in closed rectangular ducts with several aspect ratios. The Navier-Stokes equations are discretized through a second-order finite difference scheme, with non-uniform grids in two…

流体动力学 · 物理学 2019-07-19 Paolo Orlandi , Sergio Pirozzoli

We solve a Bayesian inverse Reynolds-averaged Navier-Stokes (RANS) problem that assimilates mean flow data by jointly reconstructing the mean flow field and learning its unknown RANS parameters. We devise an algorithm that learns the most…

流体动力学 · 物理学 2024-12-17 A. Kontogiannis , P. Nair , M. Loecher , D. B. Ennis , A. Marsden , M. P. Juniper

To bridge the gap between high and low fidelity numerical modeling tools for vertical-axis (or cross-flow) turbines (VATs or CFTs), an actuator line model (ALM) was developed and validated for both a high and a medium solidity vertical-axis…

流体动力学 · 物理学 2018-09-25 Peter Bachant , Anders Goude , Martin Wosnik

We report the first high-order eddy-resolving simulation of flow over a marine propeller using a recently developed high-order sliding-mesh method. This method employs the flux reconstruction framework and a new dynamic curved mortar…

流体动力学 · 物理学 2021-06-07 Bin Zhang , Chi Ding , Chunlei Liang

To study the Reynolds stresses which describe turbulent momentum transport from turbulence affected by large-scale shear and rotation. Three-dimensional numerical simulations are used to study turbulent transport under the influences of…

太阳与恒星天体物理 · 物理学 2009-11-19 J. E. Snellman , P. J. Käpylä , M. J. Korpi , A. J. Liljeström

A priori tests of turbulence models for the compressible Reynolds-Averaged Navier--Stokes (RANS) are performed by using Direct Numerical Simulations (DNS) data of zero-pressure-gradient flat-plate turbulent boundary layers. The DNS database…

流体动力学 · 物理学 2023-10-17 Sciacovelli L. , Cannici A. , Passiatore D. , Cinnella P

The Reynolds-Averaged Navier-Stokes (RANS) approach remains a backbone for turbulence modeling due to its high cost-effectiveness. Its accuracy is largely based on a reliable Reynolds stress anisotropy tensor closure model. There has been…

流体动力学 · 物理学 2022-08-31 Jiayi Cai , Pierre-Emmanuel Angeli , Jean-Marc Martinez , Guillaume Damblin , Didier Lucor

The rotational dynamics of anisotropic particles advected in a turbulent fluid flow are important in many industrial and natural setting. Particle rotations are controlled by small scale properties of turbulence that are nearly universal,…

流体动力学 · 物理学 2013-04-19 Shima Parsa , Enrico Calzavarini , Federico Toschi , Greg A. Voth

Accurate and robust models for the pressure strain correlation are an essential component for the success of Reynolds Stress Models in turbulent flow simulations. However replicating the non-local action of pressure using only local tensors…

流体动力学 · 物理学 2019-03-14 Jyoti Prakash Panda

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

Predictive simulation of many complex flows requires moving beyond Reynolds-averaged Navier-Stokes (RANS) based models to representations resolving at least some scales of turbulence in at least some regions of the flow. To resolve…

流体动力学 · 物理学 2018-12-11 Sigfried Haering , Todd A. Oliver , Robert D. Moser
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