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Article
Publication date: 17 July 2019

Ali Ayyed Abdul-Kadhim, Fue-Sang Lien and Eugene Yee

This study aims to modify the standard probabilistic lattice Boltzmann methodology (LBM) cellular automata (CA) algorithm to enable a more realistic and accurate computation of…

171

Abstract

Purpose

This study aims to modify the standard probabilistic lattice Boltzmann methodology (LBM) cellular automata (CA) algorithm to enable a more realistic and accurate computation of the ensemble rather than individual particle trajectories that need to be updated from one time step to the next (allowing, as such, a fraction of the collection of particles in any lattice grid cell to be updated in a time step, rather than the entire collection of particles as in the standard LBM-CA algorithm leading to a better representation of the dynamic interaction between the particles and the background flow). Exploitation of the inherent parallelism of the modified LBM-CA algorithm to provide a computationally efficient scheme for computation of particle-laden flows on readily available commodity general-purpose graphics processing units (GPGPUs).

Design/methodology/approach

This paper presents a framework for the implementation of a LBM for the simulation of particle transport and deposition in complex flows on a GPGPU. Towards this objective, the authors have shown how to map the data structure of the LBM with a multiple-relaxation-time (MRT) collision operator and the Smagorinsky subgrid-scale turbulence model (for turbulent fluid flow simulations) coupled with a CA probabilistic method (for particle transport and deposition simulations) to a GPGPU to give a high-performance computing tool for the calculation of particle-laden flows.

Findings

A fluid-particle simulation using our LBM-MRT-CA algorithm run on a single GPGPU was 160 times as computationally efficient as the same algorithm run on a single CPU.

Research limitations/implications

The method is limited by the available computational resources (e.g. GPU memory size).

Originality/value

A new 3D LBM-MRT-CA model was developed to simulate the particle transport and deposition in complex laminar and turbulent flows with different hydrodynamic characteristics (e.g. vortex shedding, impingement, free shear layer, turbulent boundary layer). The solid particle information is encapsulated locally at the lattice grid nodes, allowing for straightforward mapping of the datastructure onto a GPGPU enabling a massive parallel execution of the LBM-MRT-CA algorithm. The new particle transport algorithm was based on the local (bulk) particle density and velocity and provides more realistic results for the particle transport and deposition than the standard LBM-CA algorithm.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 29 no. 7
Type: Research Article
ISSN: 0961-5539

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Article
Publication date: 13 June 2024

Ryley McConkey, Nikhila Kalia, Eugene Yee and Fue-Sang Lien

Industrial simulations of turbulent flows often rely on Reynolds-averaged Navier-Stokes (RANS) turbulence models, which contain numerous closure coefficients that need to be…

50

Abstract

Purpose

Industrial simulations of turbulent flows often rely on Reynolds-averaged Navier-Stokes (RANS) turbulence models, which contain numerous closure coefficients that need to be calibrated. This paper aims to address this issue by proposing a semi-automated calibration of these coefficients using a new framework (referred to as turbo-RANS) based on Bayesian optimization.

Design/methodology/approach

The authors introduce the generalized error and default coefficient preference (GEDCP) objective function, which can be used with integral, sparse or dense reference data for the purpose of calibrating RANS turbulence closure model coefficients. Then, the authors describe a Bayesian optimization-based algorithm for conducting the calibration of these model coefficients. An in-depth hyperparameter tuning study is conducted to recommend efficient settings for the turbo-RANS optimization procedure.

Findings

The authors demonstrate that the performance of the k-ω shear stress transport (SST) and generalized k-ω (GEKO) turbulence models can be efficiently improved via turbo-RANS, for three example cases: predicting the lift coefficient of an airfoil; predicting the velocity and turbulent kinetic energy fields for a separated flow; and, predicting the wall pressure coefficient distribution for flow through a converging-diverging channel.

Originality/value

To the best of the authors’ knowledge, this work is the first to propose and provide an open-source black-box calibration procedure for turbulence model coefficients based on Bayesian optimization. The authors propose a data-flexible objective function for the calibration target. The open-source implementation of the turbo-RANS framework includes OpenFOAM, Ansys Fluent, STAR-CCM+ and solver-agnostic templates for user application.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 34 no. 8
Type: Research Article
ISSN: 0961-5539

Keywords

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Article
Publication date: 16 January 2025

Sha Zhong, Mingzhi Yang, Bosen Qian, Lei Zhang, Dongqing He, Tongtong Lin and Fue-Sang Lien

This study aims to provide new insights into aerodynamic drag reduction for increasingly faster blunt-nosed trains, such as urban and freight trains. Specifically, this work…

25

Abstract

Purpose

This study aims to provide new insights into aerodynamic drag reduction for increasingly faster blunt-nosed trains, such as urban and freight trains. Specifically, this work investigates two distinctly different wake structures and associated aerodynamic drag of blunt-nosed trains.

Design/methodology/approach

Three typical cases of blunt-nosed trains with 1-, 2- and 3-m nose lengths are selected. The time-averaged and unsteady flow structures around the trains are analyzed using the improved delayed detached eddy simulation model and proper orthogonal decomposition method.

Findings

The simulation results indicate that for 2- and 3-m nose lengths, the flow separates at first and then reattaches to the slanted surface of the tail, with a pair of longitudinal vortices dominating the wake. In contrast, for the 1-m nose length case, the wake structure is characterized by complete separation, attributed to the larger curvature of the slanted tail surface. Consequently, the total time-averaged drag coefficient is reduced by 27.2% and 19.2% for the 1-m nose length case compared to the 2- and 3-m cases, respectively. Moreover, the predominant unsteady structures with Strouhal numbers St = 0.30 and St = 0.28 are detected in the near-wake of the 2- and 3-m nose length cases, respectively. These structures result from periodic vortex shedding at the lower slanted tail surface. In contrast, for the 1-m nose length case, the predominant unsteady structure with St = 0.19 is induced by the nearly periodic expansion and contraction of the upper bubbles.

Originality/value

Two distinctly different wake structures in blunt-nosed trains are identified. Unlike high-speed trains with longer, streamlined noses, for blunt-nosed trains, shorter nose lengths result in lower aerodynamic drag. Insights for reducing energy consumption in blunt-nosed trains are provided.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0961-5539

Keywords

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