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The lattice Boltzmann method (LBM) has shown its promising capability in simulating microscale gas flows. However, the suitable boundary condition is still one of the critical issues for the LBM to model microgaseous flows involving curved…

Computational Physics · Physics 2021-01-01 Liang Wang , Shi Tao , Junjie Hu , Kai Zhang , Gui Lu

This research explores several critical factors affecting CFD-based prediction accuracy of submarine hydrodynamics and builds upon previous work on preliminary mesh and solver benchmarking. A scaled submarine model is analyzed numerically…

Classical Physics · Physics 2025-10-07 Noh Zainal Abidin , Frederic Grondin , Pol Muller , Jean-François Sigrist

We assess the suitability of Reynolds-Averaged Navier-Stokes (RANS) simulation using the Spallart-Almaras (SA) turbulence model as a closure in analysing the performance of fluidic Active Flow Control (AFC) applications. In particular, we…

Fluid Dynamics · Physics 2024-07-31 Navid Monshi Tousi , Josep M. Bergada , Fernando Mellibovsky

Despite well-known limitations of Reynolds-averaged Navier-Stokes (RANS) simulations, this methodology remains the most widely used tool for predicting many turbulent flows, due to computational efficiency. Machine learning is a promising…

Fluid Dynamics · Physics 2022-03-14 Ryley McConkey , Eugene Yee , Fue-Sang Lien

Laminar-to-turbulence transition in zero-pressure-gradient boundary layer at Mach 4.5 is studied using direct numerical simulations. For a given level of total disturbance energy, the inflow spectra was designed to correspond to the…

Fluid Dynamics · Physics 2018-12-03 Reza Jahanbakhshi , Tamer A. Zaki

In this paper, a novel zonal machine learning (ML) approach for Reynolds-averaged Navier-Stokes (RANS) turbulence modelling based on the divide-and-conquer technique is introduced. This approach involves partitioning the flow domain into…

Fluid Dynamics · Physics 2024-08-27 Anthony Man , Mohammad Jadidi , Amir Keshmiri , Hujun Yin , Yasser Mahmoudi

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…

In this paper, we propose an approach for simulating wall-bounded incompressible turbulent flows by integrating the technology of random vortex method with the core principles of large-eddy simulations (LES). In particular, we employ the…

Fluid Dynamics · Physics 2025-11-11 Zihao Guo , Zhongmin Qian

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…

Fluid Dynamics · Physics 2023-10-17 Sciacovelli L. , Cannici A. , Passiatore D. , Cinnella P

This paper presents an incomplete Octree mesh implementation of the Shifted Boundary Method (Octree-SBM) for multiphysics simulations of coupled flow and heat transfer. Specifically, a semi-implicit formulation of the thermal Navier-Stokes…

This work introduces a novel data-driven framework to formulate explicit algebraic Reynolds-averaged Navier-Stokes (RANS) turbulence closures. Recent years have witnessed a blossom in applying machine learning (ML) methods to revolutionize…

Fluid Dynamics · Physics 2023-01-24 Hongwei Tang , Yan Wang , Tongguang Wang , Linlin Tian

We present three new semi-Lagrangian methods based on radial basis function (RBF) interpolation for numerically simulating transport on a sphere. The methods are mesh-free and are formulated entirely in Cartesian coordinates, thus avoiding…

Numerical Analysis · Mathematics 2018-05-09 Varun Shankar , Grady Wright

The air entrainment due to the turbulence in a free surface boundary layer shear flow created by a horizontally moving vertical surface-piercing wall is studied through experiments and direct numerical simulations. In the experiments, a…

Serrations are commonly employed to mitigate the turbulent boundary layer trailing-edge noise. However, significant discrepancies persist between model predictions and experimental observations. In this paper, we show that this results from…

Fluid Dynamics · Physics 2023-12-27 Haopeng Tian , Benshuai Lyu

High-speed stereo PIV-measurements have been performed in a turbulent boundary layer at Re$_{\theta}$ of 9800 in order to elucidate the coherent structures. Snapshot proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD)…

Fluid Dynamics · Physics 2017-04-14 Naseem Ali , Murat Tutkun , Raúl Bayoán Cal

Direct numerical simulations are performed to investigate the combined effects of surface roughness and wall heat transfer on spatially developing compressible turbulent boundary layers at $Ma=2.5$. The roughness consists of transverse…

Fluid Dynamics · Physics 2026-04-14 Youtian Su , Wei-Xi Huang , Chunxiao Xu

The present study aims to extend the applicability of the static-boundary absorption method in phase-resolving CFD simulations outside the conventional shallow-water waves limit. Even though this method was originally formulated for…

Fluid Dynamics · Physics 2020-11-26 Muhannad W. Gamaleldin , Alexander V. Babanin , Amin Chabchoub

We use the recent frame-indifferent theory of diffusive momentum transport to identify internal barriers in wall-bounded turbulence. Formed by the invariant manifolds of the Laplacian of the velocity field, the barriers block the viscous…

Fluid Dynamics · Physics 2022-05-25 Nikolas O. Aksamit , George Haller

Most of our knowledge on the nonlinear development of the magneto-rotational instability (MRI) relies on the results of numerical simulations employing the shearing box (SB) approximation. A number of difficulties arising from this approach…

Astrophysics · Physics 2009-11-13 O. Regev , O. M. Umurhan

We use machine learning to perform super-resolution analysis of grossly under-resolved turbulent flow field data to reconstruct the high-resolution flow field. Two machine-learning models are developed; namely the convolutional neural…

Fluid Dynamics · Physics 2019-05-08 Kai Fukami , Koji Fukagata , Kunihiko Taira
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