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In this work, a near-wall model, which couples the inverse of a recently developed compressible velocity transformation [Griffin, Fu, & Moin, PNAS, 118:34, 2021] and an algebraic temperature-velocity relation, is developed for high-speed…

Fluid Dynamics · Physics 2023-09-11 Kevin Patrick Griffin , Lin Fu , Parviz Moin

Additive manufacturing creates surfaces with random roughness, impacting heat transfer and pressure loss differently than traditional sand-grain roughness. We conducted high-fidelity heat transfer simulations over three-dimensional additive…

Fluid Dynamics · Physics 2024-06-11 Himani Garg , Guillaume Sahut , Erika Tuneskog , Karl-Johan Nogenmyr , Christer Fureby

This paper focuses on the use of reinforcement learning (RL) as a machine-learning (ML) modeling tool for near-wall turbulence. RL has demonstrated its effectiveness in solving high-dimensional problems, especially in domains such as games.…

Fluid Dynamics · Physics 2023-05-24 Aurélien Vadrot , Xiang I. A. Yang , H. Jane Bae , Mahdi Abkar

Recent advances in velocity and temperature transformations have enabled recovery of the law of the wall in compressible wall-bounded turbulent flows. Building on this foundation, a flux-controlled wall model (FCWM) for Large Eddy…

Fluid Dynamics · Physics 2025-12-29 Youjie Xu , Steffen J. Schmidt , Nikolaus A. Adams

Insights gained from modal analysis are invoked for predictive large-eddy simulation (LES) wall modeling. Specifically, we augment the law of the wall (LoW) by an additional mode based on a one-dimensional proper orthogonal decomposition…

Fluid Dynamics · Physics 2023-12-05 Christoffer Hansen , Xiang IA Yang , Mahdi Abkar

For high-fidelity predictions of turbulent flows in complex practical engineering problems, the Wall-Modeled (WM) Large-Eddy Simulation (LES) has aroused great interest. In the present study, we prove that the conventional Wall-Stress…

Fluid Dynamics · Physics 2025-11-12 Mohammad Taghvaei , Ehsan Amani

In this paper, wall-modeled large-eddy simulation (WMLES) is carried out with Ffowcs-Williams and Hawkings (FW-H) acoustic analogy to investigate the turbulent flow and hydrodynamic noise of an axisymmetric body of revolution. We first…

Fluid Dynamics · Physics 2024-07-16 Peng Jiang , Shijun Liao , Bin Xie

The present work proposes an inflow turbulence generation strategy using deep learning methods. This is achieved with the help of an autoencoder architecture with two different types of operational layers in the latent-space: a fully…

Fluid Dynamics · Physics 2019-10-16 Aakash Vijay Patil

The study presents wall-modeled large-eddy simulations (LES) characterizing the flow features of a neutral atmospheric boundary layer over two urban-like roughness geometries: an array of three-dimensional square prisms and the…

We conduct wall-modeled LES (WMLES) of a pressure-driven three-dimensional turbulent boundary layer (3DTBL) developing on the floor of a bent square duct to investigate the predictive capability of three widely used wall models, namely, a…

Fluid Dynamics · Physics 2023-04-26 Xiaohan Hu , Imran Hayat , George Ilhwan Park

This work investigates the current wall-modeled large-eddy simulation (WMLES) capabilities of the open-source computational fluid dynamics solver OpenFOAM, which is used widely in academia and industry. This is achieved by a simulation…

Fluid Dynamics · Physics 2024-12-24 Christoffer Hansen , Xiang I. A. Yang , Mahdi Abkar

Wall modelling in large-eddy simulation (LES) is necessary to overcome the prohibitive near-wall resolution requirements in high-Reynolds-number turbulent flows. Most existing wall models rely on assumptions about the state of the boundary…

Fluid Dynamics · Physics 2021-10-26 H. Jane Bae , Adrián Lozano-Durán , Sanjeeb T. Bose , Parviz Moin

High-fidelity large-eddy simulations of the flow around two rectangular obstacles are carried out at a Reynolds number of 10,000 based on the free-stream velocity and the obstacle height. The incoming flow is a developed turbulent boundary…

Modelling the near-wall region of wall-bounded turbulent flows is a widespread practice to reduce the computational cost of large-eddy simulations (LESs) at high Reynolds number. As a first step towards a data-driven wall-model, a…

The inner-outer interaction model (Marusic, Mathis & Hutchins, Science, vol. 329, 2010, 193-196) and the attached-eddy model (Townsend, Cambridge University Press, 1976) are two fundamental models describing the multi-scale turbulence…

Fluid Dynamics · Physics 2022-05-17 Cheng Cheng , Lin Fu

Large-eddy simulation (LES) of a turbulent flow through an array of building-like obstacles is an idealized model to study transport of contaminants in the urban atmospheric boundary layer (UABL). A reasonably accurate LES prediction of…

Fluid Dynamics · Physics 2017-11-09 Jahrul M Alam , Luke P. J. Fitzpatrick

The spatial characteristics and structure of an axisymmetric turbulent boundary layer under strong adverse pressure gradient and weak transverse curvature are investigated using incompressible large-eddy simulation. The boundary layer is on…

Fluid Dynamics · Physics 2025-01-03 Di Zhou , Kan Wang , Meng Wang

A new approach to turbulence simulation, based on a combination of large-eddy simulation (LES) for the whole flow and an array of non-space-filling quasi-direct numerical simulations (QDNS), which sample the response of near-wall turbulence…

Fluid Dynamics · Physics 2017-10-20 Neil D. Sandham , Roderick Johnstone , Christian T. Jacobs

The prediction of aircraft aerodynamic quantities of interest remains among the most pressing challenges for computational fluid dynamics. The aircraft aerodynamics are inherently turbulent with mean-flow three-dimensionality, often…

Fluid Dynamics · Physics 2021-05-25 Adrián Lozano-Durán , Hyunji Jane Bae

Due to the multitude of scales present in realistic oceanic conditions, resolving the surface stress is computationally intensive, motivating modeling approaches. In this work, a dynamic wave drag model is developed for Large Eddy…

Fluid Dynamics · Physics 2023-05-22 Aditya K. Aiyer , Luc Deike , Michael E. Mueller