Direct numerical simulation of a stationary spherical particle in fluctuating inflows

Wang, Yuqi; Zhu, Zhengping; Hu, Ruifeng; Shen, Lian

doi:10.1063/5.0076691

Cited by 5 publications

(4 citation statements)

References 31 publications

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“…(2022) and Wang et al. (2022), and are only briefly introduced here. The flow is incompressible turbulence and governed by the continuity equation and Navier–Stokes equation.…”

Section: Numerical Methodologymentioning

confidence: 99%

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Drag model of finite-sized particle in turbulent wall-bound flow over sediment bed

et al. 2023

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Drag force acting on a particle is vital for the accurate simulation of turbulent multiphase flows, but the robust drag model is still an open issue. Fully resolved direct numerical simulation (DNS) with an immersed boundary method is performed to investigate the drag force on saltating particles in wall turbulence over a sediment bed. Results show that, for saltating particles, the drag force along the particle trajectories cannot be estimated accurately by traditional drag models originally developed for an isolated particle that depends on the particle-wall separation distance or local volume fraction in addition to the particle Reynolds number. The errors between the models and DNS are especially clear during the descending phase of the particles. Through simple theoretical analysis and DNS data fitting, we present a corrected factor using the classical, particle Reynolds number dependent drag force model as the benchmark model. The new drag model, which takes the particle vertical velocity into account, can reasonably predict the mean drag force obtained by DNS along a particle trajectory.

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“…(2022) and Wang et al. (2022), and are only briefly introduced here. The flow is incompressible turbulence and governed by the continuity equation and Navier–Stokes equation.…”

Section: Numerical Methodologymentioning

confidence: 99%

“…Therefore, particle diameter in such a wall unit is D + p = D p u τ /ν = 9.69. The numerical methods follow from our previous work in Zhu et al (2022) and Wang et al (2022), and are only briefly introduced here. The flow is incompressible turbulence and governed by the continuity equation and Navier-Stokes equation.…”

Section: Numerical Methodologymentioning

confidence: 99%

Drag model of finite-sized particle in turbulent wall-bound flow over sediment bed

et al. 2023

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“…According to the surveyed literature, many researchers have paid attention to related issues of flow perturbations, such as pulsating flow. Wang et al [7] studied the effect of pulsating free flow on the resistance of drag force and wake characteristics of stationary spherical particles using DNS. Sinusoidal fluctuation around a mean value is adopted as the freestream velocity.…”

Section: Figurementioning

confidence: 99%

Analysis of Potential Fluctuation in Flow

2022

Processes

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Understanding the physics of flow instabilities is important for processes in a wide range of engineering applications. Flow instabilities occur at the interfaces between moving fluids. Potential fluctuations are generated at the interfaces between two moving fluids based on the relationship of continuity. Theoretical analysis demonstrated that, in flow instabilities, potential fluctuation exhibits a potential oscillatory wave surface concurrently in the temporal and spatial dimensions. Potential fluctuations already internally exist in flow before flow instabilities begin to develop; these potential fluctuations greatly affect the formation of interpenetrating structures after forces act on the interfaces. Experimental studies supported the theoretical study: Experiments visualizing condensation flows using refrigerant in one smooth tube and one three-dimensional enhanced tube were conducted to show the development of potential fluctuation in spatial dimensions, and an experiment with cooling tower fouling in seven helically ridged tubes and one smooth tube were conducted to show the development of potential fluctuation in the temporal dimension. Both experimental studies confirmed that potential fluctuation was determined by the densities and velocities of the two fluids in the instability as indicated by the relationship of continuity. In addition, the results of numerical simulation in the literature qualitatively confirm the theoretical study. This paper is a first attempt to provide a comprehensive analysis of the potential fluctuation in flow.

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“…A number of different particle-resolved simulation approaches have been developed for particle-laden flows, like the immersed boundary (IB) method (Mittal and Iaccarino, 2005;Uhlmann, 2005;Luo et al, 2007;Breugem, 2012;Kempe and Fröhlich, 2012b;Zhou and Fan, 2014;Tao et al, 2018;Huang and Tian, 2019;Wang et al, 2019) and the distributed Lagrange multiplier (DLM)/fictitious domain approach (Glowinski et al, 1999(Glowinski et al, , 2001Shao, 2007, 2010;Shao et al, 2012;Yu et al, 2021). Particle-resolved simulations have been commonly used to obtain high-fidelity simulation data, which is greatly helpful for developing reduced-order models (Bagchi and Balachandar, 2003;Eaton, 2009;Homann et al, 2013;Wang et al, 2022;Xia et al, 2022;Zhu et al, 2022) or studying the mechanism of the fluid-particle interactions (Uhlmann, 2008;Kidanemariam and Uhlmann, 2014b;Vreman, 2016;Vowinckel et al, 2016;Luo et al, 2017;Peng et al, 2020). The focus of the paper is on a collision algorithm for particles in particle-resolved simulations.…”

Section: Introductionmentioning

confidence: 99%

A multiple-time-step integration algorithm for particle-resolved simulation with physical collision time

Zhu¹,

Hu²,

Zheng³

2022

Preprint

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In this paper, we present a multiple-time-step integration algorithm (MTSA) for particle collisions in particle-resolved simulations of particle-laden flows. Since the time step required for resolving a collision process is much smaller than that for the fluid flow, the computational cost obtained by reducing the time step in the traditional soft-sphere model is quite high in particleresolved simulations of more than thousands of particles. In one state-of-theart methodology, the collision time needs to be stretched to several times the flow solver time step. However, the stretched collision time is artificial and not physical, and the simulation results are affected by the stretched collision time. The present MTSA used different time steps to resolve the fluid flow, fluid-particle interactions, and particle collisions. We assessed the MTSA for particle-wall collisions as well as particle-particle collisions, determined the optimal iteration number in the substeps, and obtained excellent agreement between experimental measurements and simulation results using the traditional soft-sphere model. The MTSA was then implemented in a simulation of sediment transport (turbulent flow over an erodible particle bed) with thousands of particles. By comparing the results obtained using the MTSA and the stretching collision time algorithm, we found that stretching the collision time reduced the particle stiffness, weakened the particle entrainment, and affected the turbulence and particle statistics. The computational cost of the MTSA could be reduced to about one order of magnitude less than that using the traditional soft-sphere model with almost the same accuracy.

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Direct numerical simulation of a stationary spherical particle in fluctuating inflows

Cited by 5 publications

References 31 publications

Drag model of finite-sized particle in turbulent wall-bound flow over sediment bed

Drag model of finite-sized particle in turbulent wall-bound flow over sediment bed

Analysis of Potential Fluctuation in Flow

A multiple-time-step integration algorithm for particle-resolved simulation with physical collision time

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