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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

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

The large structures in the outer layer of turbulent wall flows are of great physical importance, because they contain a substantial fraction of the streamwise kinetic energy and of the Reynolds stresses. Nevertheless, the organization of…

流体动力学 · 物理学 2013-09-12 Juan C. del Alamo , Javier Jimenez

The direct numerical simulation (DNS) of the Taylor--Couette flow in the fully turbulent regime is described. The numerical method extends the work by Quadrio & Luchini (Eur. J. Mech. B / Fluids, v.21, pp.413--427, 2002), and is based on a…

流体动力学 · 物理学 2009-11-13 Davide Pirro , Maurizio Quadrio

Estimation of the initial state of turbulent channel flow from limited data is investigated using an adjoint-variational approach. The data are generated from a reference direct numerical simulation (DNS) which is sub-sampled at different…

流体动力学 · 物理学 2021-07-01 Mengze Wang , Tamer A. Zaki

The statistics of the velocity gradient tensor in turbulent flows are of both theoretical and practical importance. The Lagrangian view provides a privileged perspective for studying the dynamics of turbulence in general, and of the…

流体动力学 · 物理学 2016-09-21 Perry L. Johnson , Charles Meneveau

Turbulent wall flows offer the most direct means for understanding the effects of boundaries and viscosity on turbulent fluctuations. Available data on mean-square fluctuations in these flows show apparent contradiction with classical…

流体动力学 · 物理学 2025-08-05 Xi Chen , Katepalli R. Sreenivasan

The turbulent flow in an infinitely extended plane channel is analysed by solving the Navier-Stokes equations with a DNS approach. Solutions are obtained in a numerical solution domain of finite size in the streamwise as well as in the…

流体动力学 · 物理学 2015-11-25 P. Kiš , Y. Jin , H. Herwig

We have conducted a direct numerical simulation (DNS) study of dilute turbulent particulate flow in a vertical plane channel, considering thousands of finite-size rigid particles with resolved phase interfaces. The particle diameter…

流体动力学 · 物理学 2011-09-01 Markus Uhlmann

We combine resolvent-mode decomposition with techniques from convex optimization to optimally approximate velocity spectra in a turbulent channel. The velocity is expressed as a weighted sum of resolvent modes that are dynamically…

流体动力学 · 物理学 2014-05-22 R. Moarref , M. R. Jovanovic , J. A. Tropp , A. S. Sharma , B. J. McKeon

For low enough flow rates, turbulent channel flow displays spatial modulations of large wavelengths. This phenomenon has recently been interpreted as a linear instability of the turbulent flow. We question here the ability of linear…

流体动力学 · 物理学 2024-06-21 P. V. Kashyap , Y. Duguet , O. Dauchot

Direct numerical simulations (DNS) of fully-developed turbulent channel flows for very low Reynolds numbers have been performed with a larger computational box sizes than those of existing DNS. The friction Reynolds number was decreased…

流体动力学 · 物理学 2014-09-17 Takahiro Tsukahara , Yohji Seki , Hiroshi Kawamura , Daisuke Tochio

Small-scale turbulence can be comprehensively described in terms of velocity gradients, which makes them an appealing starting point for low-dimensional modeling. Typical models consist of stochastic equations based on closures for…

流体动力学 · 物理学 2024-03-01 Maurizio Carbone , Vincent J. Peterhans , Alexander S. Ecker , Michael Wilczek

Turbulent flows under transcritical conditions are present in regenerative cooling systems of rocker engines and extraction processes in chemical engineering. The turbulent flows and the corresponding heat transfer phenomena in these…

流体动力学 · 物理学 2017-12-08 Peter C. Ma , Xiang I. A. Yang , Matthias Ihme

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

Coherent structures/motions in turbulence inherently give rise to intermittent signals with sharp peaks, heavy-skirt, and skewed distributions of velocity increments, highlighting the non-Gaussian nature of turbulence. That suggests that…

计算工程、金融与科学 · 计算机科学 2019-09-24 Mehdi Samiee , Ali Akhavan-Safaei , Mohsen Zayernouri

We seek possible statistical consequences of the way a forcing term is added to the Navier--Stokes equations in the Direct Numerical Simulation (DNS) of incompressible channel flow. Simulations driven by constant flow rate, constant…

流体动力学 · 物理学 2016-01-20 Maurizio Quadrio , Bettina Frohnapfel , Yosuke Hasegawa

The high-order statistics of fluctuations in velocity gradients in the crossover range from the inertial to the Kolmogorov and sub-Kolmogorov scales are studied by direct numerical simulations (DNS) of homogeneous isotropic turbulence with…

混沌动力学 · 物理学 2011-02-11 Joerg Schumacher , Katepalli R. Sreenivasan , Victor Yakhot

Direct numerical simulations (DNS) stand out as formidable tools in studying turbulent flows. Despite the fact that the achievable Reynolds number remains lower than those available through experimental methods, DNS offers a distinct…

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

The simulation of turbulent flow requires many degrees of freedom to resolve all the relevant times and length scales. However, due to the dissipative nature of the Navier-Stokes equations, the long-term dynamics are expected to lie on a…

流体动力学 · 物理学 2025-10-15 C. Ricardo Constante-Amores , Alec J. Linot , Michael D. Graham
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