The incipient motion of granular materials

Fletcher, B.

doi:10.1088/0022-3727/9/17/007

Cited by 53 publications

(39 citation statements)

References 6 publications

(6 reference statements)

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“…The mean value is 0.16, within the range of γ values proposed by previous researchers. Bagnold 's [1941], Zingg 's [1953], and Lyles and Krauss 's [1971] results for sand, Chepil 's [1945] results for sediments ranging from clay loam to sand, and Fletcher 's [1976] results for large glass spheres gave γ values of 0.10, 0.09–0.11, 0.12, 0.17–0.20 and 0.13 respectively. Figure 3 shows the threshold shear velocities of the sediments at dry state.…”

Section: Resultsmentioning

confidence: 99%

Defining the threshold wind velocity for moistened sediments

Dong

Liu

2007

J. Geophys. Res.

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[1] The moisture of surface sediments is one of the most significant factors governing the initiation of particle movement by the wind and hence the aeolian transport rate. This paper develops an equation for the threshold shear velocity of moistened sediments based more soundly on physics by means of the moment balance method, taking account of the interparticle cohesive forces produced by moisture. The equation relates threshold shear velocity directly to moisture content and contains three simple coefficients that need be determined from experiments. The threshold equation for moistened sediments basically follows Bagnold's threshold equation for loose, dry sediments but with a proportionality coefficient that accounts for the effect of moisture. Previously published data on the threshold shear velocities of moistened sediments from wind tunnel tests are used to determine the coefficients contained in the threshold equation, and it proves that the new equation describes the threshold velocities of moistened sediments reasonably well. Comparing the threshold shear velocity predicted using the new equation with the results of Belly's and Hotta et al.'s empirical equations reveals that the predicted results differed greatly. These differences can be attributed to differences in the definition of threshold velocity and moisture content in the physical and chemical properties of the sediment samples and in the experimental methods. Further theoretical and experimental investigation is required to fully understand the effects of sediment moisture content on threshold wind velocity. We also suggest that methodological improvement itself is also a topic of future research.Citation: Dong, Z., Q. Mu, and X. Liu (2007), Defining the threshold wind velocity for moistened sediments,

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Section: Resultsmentioning

confidence: 99%

Defining the threshold wind velocity for moistened sediments

Dong

Liu

2007

J. Geophys. Res.

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“…By fitting a group of studies from decades ago to recent time (Bagnold 1937;Chepil 1945;Fletcher 1976aFletcher , 1976bGreeley et al 2003;Mazurak et al 1953;Shao and Klose 2016;Shao and Lu 2000), u * t can be estimated with a curve that has a valley around 70 µm using equation 5:…”

Section: Us Environmental Protection Agencymentioning

confidence: 99%

Amplification factors to estimate wind erosion of piles of soil with different heights: Numerical simulation and structure-from-motion photogrammetry verification

Liu¹,

Qu²,

Ning³

2018

Journal of Soil and Water Conservation

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Abstract:In this study, we propose amplification factors of single conical piles with different heights and sizes to describe wind erosion acceleration effects of piles compared to flat erodible surfaces. It is not difficult using traditional wind erosion models to estimate wind erosion of flat agricultural land or pastoral surfaces. However, in the current form these models cannot be used for elevated surfaces such as dunes or piles, which may lead to more serious dust emission and air pollution. A computational fluid dynamics simulation was used to refine a wind erosion estimation method of pile from the US Environmental Protection Agency (USEPA) by dividing the pile surface into multiple subareas with constant wind speed for each subarea and then obtaining ratios of pile erosion to the corresponding flat surface by calculating the total erosion rate of the pile and that of a flat surface with the same floor area. The ratios as amplification factors, E amp , for six pile sizes (from 1 to 12 m) and five wind speed scenarios covering most erosive daily wind speeds from 824 stations throughout China were analyzed and fitted to equations to enable consideration of other heights and wind speed conditions. The changing patterns of the factors were verified using experimental surveys of two plots via the structure-from-motion photogrammetry method, which generated high-resolution digital elevation maps and detected detailed erosion changes. The erosion from a stockpile surface was found to be 2.52 to 7.16 times that from a flat surface with the same floor area, and the influence of pile height on amplification factor could be expressed by a logarithmic equation. These factors can be easily applied to estimate pile erosion by multiplying them by wind erosion results for flat surfaces obtained from other indexes or models. The procedure described in this paper can be extended to consider amplification or reduction effects of dunes, hills, piles, pits, and gullies with different shapes or configurations in relation to wind erosion. Furthermore, the reliability of wind erosion estimation over complex surfaces can be improved to implement specific soil conservation practices.Key words: computational fluid dynamics-Dunhuang-pile-structure-from-motion photogrammetry-wind erosion Wind erosion produces suspended particles from erodible surfaces and causes air pollution, land degradation, and human health problems. The impact of wind erosion is significant in areas severely affected by poor air quality, such as northern China. The effects of terrain obstructions like dunes or piles on modifying wind speed and wind erosion have long been identified (Bagnold 1941; Stunder and Arya 1988;Sullivan and Ajwa 2011). Several models have been developed and improved to estimate wind erosion from agricultural, pastoral, and, sometimes, human disturbed surfaces (Alfaro and Gomes 2001;Hagen 2001;Tatarko et al. 2016;Wagner 2013;Zhang et al. 2017). However, there are no models capable of estimating wind erosion from elevated surfaces because ...

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“…The aerodynamic related parameter A depended on the transfer of wind energy to erodible surface that might have different roughness factors. Threshold friction velocity models were obtained by deducing the aerodynamics of turbulent Reynolds stress (Bagnold, 1941;Zingg, 1953;Fletcher, 1976;Lin, 2008). Combined with outcomes of wind tunnel simulations, mechanisms of the particle movement were advanced (Bagnold, 1941;Chepil, 1945;Zingg, 1953;Bisal and Nielsen, 1962;Fletcher, 1976;He and Liu, 1989;Dong, 2005;Lin, 2008;Xuan, 2004).…”

Section: Introductionmentioning

confidence: 99%

“…Threshold friction velocity models were obtained by deducing the aerodynamics of turbulent Reynolds stress (Bagnold, 1941;Zingg, 1953;Fletcher, 1976;Lin, 2008). Combined with outcomes of wind tunnel simulations, mechanisms of the particle movement were advanced (Bagnold, 1941;Chepil, 1945;Zingg, 1953;Bisal and Nielsen, 1962;Fletcher, 1976;He and Liu, 1989;Dong, 2005;Lin, 2008;Xuan, 2004). Values of experimentally obtained A varied from 0.10 (Bagnold, 1941), 0.12 (Zingg, 1953), 0.13 (Fletcher, 1976), and lower limit of 0.16 to upper limit of 0.21 (Lin, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…Combined with outcomes of wind tunnel simulations, mechanisms of the particle movement were advanced (Bagnold, 1941;Chepil, 1945;Zingg, 1953;Bisal and Nielsen, 1962;Fletcher, 1976;He and Liu, 1989;Dong, 2005;Lin, 2008;Xuan, 2004). Values of experimentally obtained A varied from 0.10 (Bagnold, 1941), 0.12 (Zingg, 1953), 0.13 (Fletcher, 1976), and lower limit of 0.16 to upper limit of 0.21 (Lin, 2008). To explain the threshold friction velocity for moist soils, McKenna-Neuman and Nickling (1989) connected the capillary forces model deriven by Haines (1925) and Fisher (1926) to the threshold friction velocity model for dry soils deriven by Bagnold's (1941).…”

Section: Introductionmentioning

confidence: 99%

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Modeling the effect of humidity on the threshold friction velocity of coal particles

Zhang

Chen

Shou-yi

2012

Atmospheric Environment

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a b s t r a c tCoal particles emission could cause serious air pollution in coal production region and transport region. In coal mining industry, large amounts of water are regularly spayed to coal piles to prevent dust emission from the coal particles. The mechanism behind this measure is to manage the threshold friction velocity, which is an important parameter in controlling wind erosion and dust emission. Bagnold has developed a threshold friction velocity model for soil particles. However, the Bagnold model cannot be applied directly to coal particles as coal particles are quite different from soils in physical and chemical properties. We studied and modeled threshold friction velocity of coal particles under different humidities by using a wind tunnel. Results showed that the effects of humidity on coal particles' threshold friction velocity are related to the hydrophilic effect and adhesive effect. Bagnold model can be corrected by two new parameter items which explained the two effects. The new model, agreed well with wind tunnel measurements for coal particles with different size categories. Despite the fact the new model was developed for coal particles, its physical basis may allow the model application to other wind susceptible particles.

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The incipient motion of granular materials

Cited by 53 publications

References 6 publications

Defining the threshold wind velocity for moistened sediments

Defining the threshold wind velocity for moistened sediments

Amplification factors to estimate wind erosion of piles of soil with different heights: Numerical simulation and structure-from-motion photogrammetry verification

Modeling the effect of humidity on the threshold friction velocity of coal particles

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