Drag increase and turbulence augmentation in two-way coupled particle-laden wall-bounded flows

Battista, F.; Gualtieri, P.; Mollicone, J.-P.; Salvadore, Francesco; Casciola, Carlo Massimo

doi:10.1063/5.0141964

Cited by 4 publications

(1 citation statement)

References 74 publications

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“…Investigations of particle-laden turbulent flows in the two-way-coupling regime are found in e.g. Vreman et al (2009), Zhao, Andersson & Gillissen (2010), Kuerten (2016), Richter & Sullivan (2014), Capecelatro, Desjardins & Fox (2018), and Battista et al (2023).…”

Section: Introductionmentioning

confidence: 99%

On the relevance of lift force modelling in turbulent wall flows with small inertial particles

Gao,

Shi,

Parsani

et al. 2024

J. Fluid Mech.

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In particle-laden turbulent wall flows, lift forces can influence the near-wall turbulence. This has been observed recently in particle-resolved simulations, which, however, are too expensive to be used in upscaled models. Instead, point-particle simulations have been the method of choice to simulate the dynamics of these flows during the last decades. While this approach is simpler, cheaper and physically sound for small inertial particles in turbulence, some issues remain. In the present work, we address challenges associated with lift force modelling in turbulent wall flows and the impact of lift forces in the near-wall flow. We performed direct numerical simulations of small inertial point particles in turbulent channel flow for fixed Stokes number and mass loading while varying the particle size. Our results show that the particle dynamics in the buffer region, causing the apparent particle-to-fluid slip velocity to vanish, raises major challenges for modelling lift forces accurately. While our results confirm that lift forces have little influence on particle dynamics for sufficiently small particle sizes, for inner-scaled diameters of order one and beyond, lift forces become quite important near the wall. The different particle dynamics under lift forces results in the modulation of streamwise momentum transport in the near-wall region. We analyse this lift-induced turbulence modulation for different lift force models, and the results indicate that realistic models are critical for particle-modelled simulations to correctly predict turbulence modulation by particles in the near-wall region.

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Section: Introductionmentioning

confidence: 99%

On the relevance of lift force modelling in turbulent wall flows with small inertial particles

Gao,

Shi,

Parsani

et al. 2024

J. Fluid Mech.

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Prediction of particle-laden pipe flows using deep neural network models

Haghshenas,

Hedayatpour,

Groll

2023

Physics of Fluids

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An accurate and fast prediction of particle-laden flow fields is of particular relevance for a wide variety of industrial applications. The motivation for this research is to evaluate the applicability of deep learning methods for providing statistical properties of the carrier and dispersed phases in a particle-laden vertical pipe flow. Deep neural network (DNN) models are trained for different dependent variables using 756 high-fidelity datasets acquired from point-particle large-eddy simulations for different values of Stokes number, St, bulk particle volume fraction, Φ¯v, and wall roughness, Δγ, for the range St=10−500, Φ¯v=5×10−5−10−3, and Δγ=1°−6°. The considered parameter space corresponds to the inertia-dominated regime and covers a large extent of the typical conditions in powder-based laser metal deposition. We find that the DNN models capture the nonlinear dynamics of the system and recreate the statistical properties of the particle-laden pipe flow. However, DNN predictions of the particle statistics are of higher accuracy compared to the fluid statistics, which is attributed to the highly non-monotonic dependence of the fluid statistics on the control parameters. Owing to significantly decreased time-to-solution, the trained DNN models are promising as surrogate models to expedite model development and design process of various industrial applications.

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Insight into the particle-laden turbulent flow statistics in sharply bent channels

Sharma,

Lakkaraju,

Atta

2023

Physics of Fluids

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In canonical wall-bounded flows, point particle-laden turbulence exhibits a substantial interaction between scales with a variety of regimes, and the dynamics of the point particle-laden fluid are primarily identified by the Reynolds number. Such interactions are even more augmented in curved channels with variable curvature, and fixed Reynolds numbers demonstrate distinct flow behavior, as shown by Brethouwer [J. Fluid Mech. 931, A21 (2022)]. In this work, we demonstrate the characteristics of wall-bounded point particle-laden turbulent flows in sharply bent channels by evaluating the time-averaged velocity profiles at the straight section, at the bend, and in the inclined sections. The mean (time-averaged) normalized velocity profiles retain their well-known logarithmic features, with the von Kármán and additive constants taking different values depending on the acute inclination of the bend. Near-wall fluctuations at the bend are found to be intensified due to the bend that leads to increased turbulent activity. On examining the friction Reynolds number along the bent channel walls in the streamwise direction, a modulated behavior with an abrupt change at the bend is observed. Budgets of turbulent kinetic energy (TKE) are delineated for various inclinations of the bend at different sections of the channel and are compared with the unladen sharply bent turbulent channel flows, which illustrate that TKE is modulated at the bend and there is an overall attenuation of TKE on loading the channel with point particles.

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Drag increase and turbulence augmentation in two-way coupled particle-laden wall-bounded flows

Cited by 4 publications

References 74 publications

On the relevance of lift force modelling in turbulent wall flows with small inertial particles

On the relevance of lift force modelling in turbulent wall flows with small inertial particles

Prediction of particle-laden pipe flows using deep neural network models

Insight into the particle-laden turbulent flow statistics in sharply bent channels

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