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Symbolic regression (SR) methods have been extensively investigated to explore explicit algebraic Reynolds stress models (EARSM) for turbulence closure of Reynolds-averaged Navier-Stokes (RANS) equations. The deduced EARSM can be readily…

流体动力学 · 物理学 2024-10-15 Yu Zhang , Kefeng Zheng , Fei Liu , Qingfu Zhang , Zhenkun Wang

The analysis of flow around buildings has gained significant research interest across various domains, including pedestrian safety, pollutant dispersion, natural ventilation, and building energy efficiency. While these domains frequently…

流体动力学 · 物理学 2026-03-18 Hojin Lee , Andreas Lintermann , Sangseung Lee , Mario Rüttgers

This study constitutes the second phase of a research endeavor aimed at evaluating the feasibility of employing Long Short-Term Memory (LSTM) neural networks as a replacement for Reynolds-Averaged Navier-Stokes (RANS) turbulence models. In…

流体动力学 · 物理学 2024-11-19 Hugo D. Pasinato

This paper introduces two novel concepts in data-driven turbulence modeling that enable the simultaneous development of multiple closure models and the training towards multiple objectives. The concepts extend the evolutionary framework by…

流体动力学 · 物理学 2022-01-03 Fabian Waschkowski , Yaomin Zhao , Richard Sandberg , Joseph Klewicki

We present a body-free simulation framework for three-dimensional turbulent cylinder wakes, in which the upstream cylinder is not explicitly resolved. Instead, the incompressible Navier--Stokes equations are solved in a simplified…

流体动力学 · 物理学 2026-04-15 Zhicheng Wang , Theo Käufer , Khemraj Shukla , Michael Triantafyllou , George Em Karniadakis

The present study represents a data-driven turbulent model with Galilean invariance preservation based on machine learning algorithm. The fully connected neural network (FCNN) and tensor basis neural network (TBNN) [Ling et al. (2016)] are…

流体动力学 · 物理学 2025-02-11 Xuepeng Fu , Shixiao Fu , Chang Liu , Mengmeng Zhang , Qihan Hu

In this paper, we present two deep learning-based hybrid data-driven reduced order models for the prediction of unsteady fluid flows. The first model projects the high-fidelity time series data from a finite element Navier-Stokes solver to…

流体动力学 · 物理学 2024-05-29 Sandeep Reddy Bukka , Rachit Gupta , Allan Ross Magee , Rajeev Kumar Jaiman

Many unsteady flows exhibiting complex dynamics are nevertheless characterized by emergent large-scale coherence in space and time. Reduced-order models based on Galerkin projection of the governing equations onto an orthogonal modal basis…

流体动力学 · 物理学 2022-06-28 Jared L. Callaham , Jean-Christophe Loiseau , Steven L. Brunton

Design exploration or optimization using computational fluid dynamics (CFD) is commonly used in the industry. Geometric variation is a key component of such design problems, especially in turbulent flow scenarios, which involves running…

机器学习 · 计算机科学 2024-12-04 Shinjan Ghosh , Julian Busch , Georgia Olympia Brikis , Biswadip Dey

Buoyant shear layers are encountered in many engineering and environmental applications and have been studied by researchers in the context of experiments and modeling for decades. Often, these flows have high Reynolds and Richardson…

流体动力学 · 物理学 2021-07-27 Naman Jain , Hieu T. Pham , Xinyi Huang , Sutanu Sarkar , Xiang Yang , Robert Kunz

The goal of this dissertation is to investigate the PANS model capabilities in providing significant improvement over RANS predictions at slightly higher computational expense and producing LES quality results at significantly lower…

流体动力学 · 物理学 2017-12-12 Pooyan Razi

High resolution simulations of incompressible flows have become routine across a range of engineering applications. Despite their routine use, due to the high dimensional parameter space present for most practical applications, a…

流体动力学 · 物理学 2022-11-14 Christopher J. McDevitt , Eric Fowler , Subrata Roy

Data-driven techniques have improved the accuracy of Reynolds-averaged Navier-Stokes (RANS) models in fluid dynamics. However, modeling separated flows remains challenging due to their complex physics and sensitivity to local conditions.…

流体动力学 · 物理学 2025-11-19 Ali Eidi , Tyler Buchanan , Letian Jiang , Richard P. Dwight

In this study, the ducted propeller has been numerically investigated under oblique flow, which is crucial and challenging for the design and safe operation of thruster driven vessel and dynamic positioning (DP) system. A Reynolds-Averaged…

流体动力学 · 物理学 2018-01-11 Qin Zhang , Rajeev K. Jaiman , Peifeng Ma , Jing Liu

This study aims to enhance the generalizability of Reynolds-averaged Navier-Stokes (RANS) turbulence models, which are crucial for engineering applications. Classic RANS turbulence models often struggle to predict separated flows…

流体动力学 · 物理学 2025-09-03 Chenyu Wu , Shaoguang Zhang , Changxin Guo , Yufei Zhang

Reynolds-averaged Navier-Stokes (RANS) turbulence models (such as k-\epsilon models) are still widely used for engineering applications because of their relatively simplicity and robustness. In fully developed plane channel flow (i.e. the…

流体动力学 · 物理学 2010-12-01 Najla El Gharbi , Rafik Absi , Ahmed Benzaoui

The present study assesses RANS-based turbulence models to simulate isothermal flow in a combustor representing a constituent can combustor of can-annular configuration used in jet engines. Two-equation models (standard $k-\epsilon$,…

流体动力学 · 物理学 2024-06-25 Aishvarya Kumar , Ram Prakash Bharti

We propose an enhanced wall-boundary treatment for the lattice Boltzmann method (LBM), designed for high-Reynolds-number turbulent flows on adaptively refined Cartesian grids. The method improves the slip-velocity bounce-back scheme by…

流体动力学 · 物理学 2026-01-27 Jorge Ponsin , Carlos Lozano

Wing-body junction flows occur when a boundary layer encounters an airfoil mounted on the surface. The corner flow near the trailing edge is challenging for the linear eddy viscosity Reynolds Averaged Navier-Stokes (RANS) models, due to the…

流体动力学 · 物理学 2016-05-20 Jin-Long Wu , Jian-Xun Wang , Heng Xiao

We investigate rough-wall turbulent flows through direct numerical simulations of flow over three-dimensional transitionally rough sinusoidal surfaces. The roughness Reynolds number is fixed at $k^+=10$, where $k$ is the sinusoidal…

流体动力学 · 物理学 2020-12-09 M. MacDonald , L. Chan , D. Chung , N. Hutchins , A. Ooi