A model for particle removal from surfaces with large-scale roughness in turbulent flows

Nasr, Babak; Ahmadi, Goodarz; Ferro, A.; Dhaniyala, Suresh

doi:10.1080/02786826.2019.1692126

Cited by 21 publications

(8 citation statements)

References 51 publications

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“…The mechanism of developing strategy of L1 and L4 were demonstrated individually as Figure 7b. Under gentle L1 condition, the worse adhesion between developing solvent with mask surface re duce the opportunity of particle/solvent interaction, thus, the remaining fragments become hard defect after etching process [10][11] . However, after switching to the L4 condition, substantial improvement on opaque defect count reduction can be observed [12][13] .…”

Section: Table 2 Design Of Experiments (Doe) Of Developing Model (L1-l4)mentioning

confidence: 99%

New EUV mask blank for N3 technology node and beyond

Yang,

Chen,

Huang

et al. 2023

Photomask Technology 2023

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Section: Table 2 Design Of Experiments (Doe) Of Developing Model (L1-l4)mentioning

confidence: 99%

New EUV mask blank for N3 technology node and beyond

Yang,

Chen,

Huang

et al. 2023

Photomask Technology 2023

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“…The surface roughness is regarded as one of the most influential variables affecting PM resuspension because it has a significant impact on the adhesion forces on multi-scales ( Qian, Peccia & Ferro, 2014 ). The largest scale of surface roughness that can affect PM resuspension could be the macrostructure of urban areas, such as buildings and tree canopies, and there are smaller surface roughness scales contributed by the shrubs/grass level ( Salizzoni et al, 2008 ; Nasr et al, 2020 ). However, most of the studies have been conducted on microscale surface roughness such as leaf morphology using a single leaf ( Speak et al, 2012 ; Wang, Shi & Wang, 2015 ; Chen et al, 2017 ; Xu et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

Particulate matter resuspension from simulated urban green floors using a wind tunnel-mounted closed chamber

Seo

Park

Yoo

2023

PeerJ

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Background Green areas are thought to reduce particulate matter (PM) concentrations in urban environments. Plants are the key to PM reduction via various mechanisms, although most mechanisms do not lead to the complete removal of PM. Ultimately, PM falls into the soil via wind and rainfall. However, the fallen PM can re-entrain the atmosphere, which can affect plants capacity to reduce PM. In this study, we simulated an urban green floor and measured the resuspension of PM from the surface using a new experimental system, a wind tunnel-mounted closed chamber. Methods The developed system is capable of quantifying the resuspension rate at the millimeter scale, which is measured by using the 1 mm node chain. This is adequate for simulating in situ green floors, including fallen branches and leaves. This addressed limitations from previous studies which focused on micrometer-scale surfaces. In this study, the surfaces consisted of three types: bare sand soil, broadleaves, and coniferous leaves. The resuspended PM was measured using a light-scattering dust detector. Results The resuspension rate was highest of 14.45×10−4 s−1 on broad-leaved surfaces and lowest on coniferous surfaces of 5.35×10−4 s−1 (p < 0.05) and was not proportional to the millimeter-scale surface roughness measured by the roller chain method. This might be due to the lower roughness density of the broad-leaved surface, which can cause more turbulence for PM resuspension. Moreover, the size distribution of the resuspended PM indicated that the particles tended to agglomerate at 2.5 µm after resuspension. Conclusion Our findings suggest that the management of fallen leaves on the urban green floor is important in controlling PM concentrations and that the coniferous floor is more effective than the broadleaved floor in reducing PM resuspension. Future studies using the new system can be expanded to derive PM management strategies by diversifying the PM types, surfaces, and atmospheric conditions.

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“…They found that the supporting asperities reduced the detaching moment due to a lower adhesive force and smaller contact radius. In Nasr et al (2019) the substrate roughness was described using a two-dimensional (2-D) sinusoidal profile. They found that the wavelength and amplitude of roughness structures had played an important role in the critical shear velocity.…”

Section: Introductionmentioning

confidence: 99%

“…Re p ), a semi-empirical drag correlation was proposed by Schiller & Naumann (1933) for particles in unconfined flow. The drag correlation from the combination of O'Neill (1968) and Schiller & Naumann (1933) is generally accepted for calculating the drag coefficient of a particle on a smooth surface at a finite particle Reynolds number (Cui & Sommerfeld 2015;Nasr et al 2019). At Re p > 1000, the flow around the particle is fully turbulent, and the drag coefficient of the particle depends on the particle size compared with the height of the turbulent boundary layer (Sommerfeld, van Wachem & Oliemans 2008).…”

Section: Introductionmentioning

confidence: 99%

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Hydrodynamic force and torque acting on a micron-sized spherical particle attached to a surface with large-scale concave roughness in linear shear flow and the possibility of detachment

Meng

Pang²,

Liu³

et al. 2022

J. Fluid Mech.

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This work studies the detachment of a micron-sized spherical particle from a surface with concave roughness in a linear shear flow. The concave roughness is described as regularly spaced hollow hemispheres below a flat surface and is characterised by two dimensionless parameters, i.e. dimensionless asperity distance and asperity size ratio. The hydrodynamic force and torque on the particle are calculated by performing lattice Boltzmann simulations for particle Reynolds numbers ranging from 0.02 to 40. Empirical correlations of the drag, lift and torque coefficients of the particle as functions of the particle Reynolds number and the asperity size ratio are proposed. For detachment by lifting, sliding and rolling, a numerical approach to calculate the critical particle Reynolds number (i.e. above which the particle can detach from the surface) is proposed. It is found that the dimensionless asperity distance and the distribution of asperities on the rough surface have a minor influence on the hydrodynamic force and torque on the particle, and the detachment of the particle becomes more difficult as the particle sits deeper in a larger hole. Both the empirical correlations and the numerical approach can be implemented into Lagrangian particle tracking and can accurately predict the detachment of particles from the surface with concave roughness or the detachment of particles embedded in a flat surface.

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A model for particle removal from surfaces with large-scale roughness in turbulent flows

Cited by 21 publications

References 51 publications

New EUV mask blank for N3 technology node and beyond

New EUV mask blank for N3 technology node and beyond

Particulate matter resuspension from simulated urban green floors using a wind tunnel-mounted closed chamber

Hydrodynamic force and torque acting on a micron-sized spherical particle attached to a surface with large-scale concave roughness in linear shear flow and the possibility of detachment

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