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Two-way coupled DNS simulation of particle-laden turbulent Couette-flow [1], in the volume fraction regime $\phi>10^{-4}$, showed a discontinuous decrease of turbulence intensity beyond a critical volume fraction…

Fluid Dynamics · Physics 2022-04-04 Swagnik Ghosh , Partha Sarathi Goswami

A dynamical systems approach to turbulence envisions the flow as a trajectory through a high-dimensional state space transiently visiting the neighbourhoods of unstable simple invariant solutions (E. Hopf, Commun. Appl. Maths 1, 303, 1948).…

Fluid Dynamics · Physics 2023-11-15 Jacob Page , Peter Norgaard , Michael P. Brenner , Rich R. Kerswell

This paper presents a new iterative state estimation algorithm for advection dominated flows with non-Gaussian uncertainty description of $L^\infty$-type: uncertain initial condition and model error are assumed to be pointvise bounded in…

Optimization and Control · Mathematics 2017-12-05 Emanuele Ragnoli , Mykhaylo Zayats , Fearghal O'Donncha , Sergiy Zhuk

We have performed direct numerical simulations (DNS) of compressible turbulent channel flow at supercritical pressure with top and bottom isothermal walls kept respectively at a supercritical (Ttop > Tpb) and subcritical temperature (Tbot <…

Fluid Dynamics · Physics 2017-12-18 Kukjin Kim , Jean-Pierre Hickey , Carlo Scalo

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…

Fluid Dynamics · Physics 2019-07-19 Paolo Orlandi , Sergio Pirozzoli

Direct numerical simulations of turbulent open channel flow with friction Reynolds numbers of $Re_{\tau}=200,400,600$ are performed. Their results are compared with closed channel data in order to investigate the influence of the free…

Fluid Dynamics · Physics 2022-12-02 Christian Bauer

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…

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…

Fluid Dynamics · Physics 2024-03-01 Maurizio Carbone , Vincent J. Peterhans , Alexander S. Ecker , Michael Wilczek

This study explores the dynamics of dispersed bubbly turbulent flow in a channel using interface-resolved direct numerical simulation (DNS) with an efficient Coupled Level-Set Volume-of-Fluid (CLSVOF) solver. The influence of number of…

Fluid Dynamics · Physics 2024-06-07 Xuanwei Zhang , Yanchao Liu , Wenkang Wang , Guang Yang , Xu Chu

Near-wall regions in wall-bounded turbulent flows experience intermittent ejection of slow-moving fluid packets away from the wall and sweeps of faster moving fluid towards the wall. These extreme events play a central role in regulating…

Fluid Dynamics · Physics 2023-09-27 Eric Jagodinski , Xingquan Zhu , Siddhartha Verma

Direct numerical simulations of a temporally-developing, low-speed, variable-density, turbulent, plane mixing layer are performed. The Navier-Stokes equations in the low-Mach number approximation are solved using a novel algorithm based on…

Fluid Dynamics · Physics 2017-10-04 Antonio Almagro , Manuel Garcia-Villalba , Oscar Flores

The widest spanwise scales in turbulent channel flows are studied through the use of three periodic channel-flow simulations at friction Reynolds number $\mathrm{Re}_{\tau}=550$. The length and height of the channels are the same in all…

Fluid Dynamics · Physics 2023-07-07 Ramón Pozuelo , André V. G. Cavalieri , Philipp Schlatter , Ricardo Vinuesa

The need to estimate a particular quantile of a distribution is an important problem which frequently arises in many computer vision and signal processing applications. For example, our work was motivated by the requirements of many…

Computer Vision and Pattern Recognition · Computer Science 2015-04-22 Ognjen Arandjelovic , Duc-Son Pham , Svetha Venkatesh

Fully-convolutional neural networks (FCN) were proven to be effective for predicting the instantaneous state of a fully-developed turbulent flow at different wall-normal locations using quantities measured at the wall. In Guastoni et al.…

Numerical precision in large-scale scientific computations has become an emerging topic due to recent developments in computer hardware. Lower floating point precision offers the potential for significant performance improvements, but the…

Fluid Dynamics · Physics 2025-05-05 Martin Karp , Niclas Jansson , Saleh Rezaeiravesh , Stefano Markidis , Philipp Schlatter

Direct numerical simulations (DNS) are an indispensable tool for understanding the fundamental physics of turbulent flows. Because of their steep increase in computational cost with Reynolds number ($R_{\lambda}$), well-resolved DNS are…

Computational Physics · Physics 2020-08-26 Komal Kumari , Diego A. Donzis

Data-driven methods for improving turbulence modeling in Reynolds-Averaged Navier-Stokes (RANS) simulations have gained significant interest in the computational fluid dynamics community. Modern machine learning algorithms have opened up a…

Fluid Dynamics · Physics 2019-02-05 Nicholas Geneva , Nicholas Zabaras

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…

Fluid Dynamics · Physics 2024-06-06 Sergio Hoyas , Ricardo Vinuesa , Peter Schmid , Hassan Nagib

Airflow simulation results depend on a good prediction of near wall turbulence. In this paper a comparative study between different near wall treatments is presented. It is applied to two test cases: (1) the first concerns the fully…

Classical Physics · Physics 2010-11-30 Najla El Gharbi , Rafik Absi , Ahmed Benzaoui , E. H. Amara

The equations for LES are formally derived by low-pass filtering the NS equations with the effect of the small scales on the larger ones captured by a SGS model. However, it is known that the LES equations usually employed in practical…

Computational Physics · Physics 2019-02-08 H. Jane Bae , Adrián Lozano-Durán
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