Wind tunnel Study on Aeolian Saltation Dynamics and Mass flow

Okoli, Remigius Egwuonwu

doi:10.1006/jare.2002.1056

Cited by 13 publications

(5 citation statements)

References 32 publications

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“…[14] Each set of profile data (the paired sets of wind speed and elevation for each profile) was subjected to linear regression analysis, with elevation as the independent Butterfield [1999] sand/wind tunnel 0.004 -0.0455 Chamberlain [1983] sand-snow-water/field 0.0145 Charnock [1955] water/field 0.046 -0.085 Dong et al [2003] sand/wind tunnel 0.00036 -0.026 Ellison [1956] water/field 0.085 Farrell [1999] sand/field 0.24 Farrell [1999] sand/wind tunnel 0.032 Garratt [1977] water/field 0.0144 McEwan [1991] snow/field 0.025 Mikkelsen [1989] sand/wind tunnel 0.014 Okoli [2003] sand/wind tunnel 0.0125 Owen [1964] sand/wind tunnel 0.02 Rasmussen et al [1985] sand/field 0.07 -0.09 Rasmussen [1989] sand/wind tunnel 0.01 Rasmussen and Mikkelsen [1991] sand/wind tunnel 0.011 Rasmussen et al [1996] sand/wind tunnel <0.01 -0.03 Sherman [1992] sand/field and wind tunnel 0.009 Tabler [1980] snow/field 0.026 Wu [1968] water/field 0.012 Wu [1980] water/field 0.0156 -0.0185…”

Section: Methodsmentioning

confidence: 99%

Aerodynamic roughness lengths over movable beds: Comparison of wind tunnel and field data

Sherman¹,

Farrell²

2008

J. Geophys. Res.

100

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[1] We evaluate the increase in apparent roughness length during aeolian saltation. Using a data set comprising 291 wind profiles measured in wind tunnels or in the field, we tested the Charnock (1955), modified Charnock (Sherman, 1992), and Raupach (1991 relationships for the entire data set and for segregated wind tunnel and field data sets. We assessed the abilities of the models to predict enhanced roughness and investigated scaling differences between wind tunnels and field environments. For the entire data set, the Charnock relationship worked best, with an r 2 of 0.25. For the segregated data sets, the Charnock relationship also worked best, with an average r 2 of 0.675, as opposed to 0.66 for the modified Charnock and 0.61 for the Raupach model. For conditions with shear velocity 1.0 ms À1 , the comparable, average values of r 2 are 0.54, 0.625, and 0.645, respectively. There was an order of magnitude difference in the roughness length responses for wind tunnel and field data for each model evaluated, convincing evidence of a fundamental scaling constraint in the wind tunnel data. The scaling differences are attributed to conceptual and bias errors that include the use of the law of the wall versus the wake-defect law or differences in turbulence characteristics, especially regarding the role of large coherent structures. Raupach's (1991) A was found to be an extremely volatile constant, making the modified Charnock relationship most suitable for predicting aerodynamic roughness lengths over movable sand beds.

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Section: Methodsmentioning

confidence: 99%

Aerodynamic roughness lengths over movable beds: Comparison of wind tunnel and field data

Sherman¹,

Farrell²

2008

J. Geophys. Res.

100

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“…Numerous wind tunnel tests and field observations have been carried out to analyze the deformation of stockpiles and sand transport phenomena (Zhou et al, 2002;Okoli, 2003;Ferreira and Lambert, 2011). Computational models, largely qualitative, have been developed, including those to predict snow drift, with some of them considering erosion, saltation and deposition processes (Moore et al, 1994;Leys et al, 2001;Beyers et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Computational modeling of the wind erosion on a sinusoidal pile using a moving boundary method

Farimani¹,

Ferreira²,

Sousa³

2011

Geomorphology

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“…landform, vegetation cover, texture and sand material available). This is because different surfaces have different effects on threshold velocity, saltation of transported particles and deposition processes (Remigius, 2003;Dong et al, 2004). Figure 9 shows the structure of sand flow observed during the windy period from 31 March to 3 April 2014.…”

Section: Structure Of Sand Flowmentioning

confidence: 99%

Key Role of Desert–Oasis Transitional Area in Avoiding Oasis Land Degradation from Aeolian Desertification in Dunhuang, Northwest China

Zhang

Dan

et al. 2016

Land Degrad Dev

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The survival of oasis is partially determined by the evolution of desert–oasis transitional area (abbr. DOTA) characterized by fragile and unstable environments. This study reveals the function of DOTA in avoiding oasis land degradation from its aeolian environments based on the detailed wind data, in situ observation of wind‐blown sand and granular characteristics of surface sediments from desert to oasis. Results indicate that the DOTA has buffering function in slowing down aeolian desertification in oasis. Additionally, the annual mean wind speed reduces 40·8% from desert to DOTA area but up to 92·8% from desert to oasis. The frequency of sand‐laden wind, drift potential and sand transport all decrease following the section from desert to oasis while surface roughness increases. And the granular characteristics of surface sediments show that the weight percentage of coarse sand decreases but fine sand increases along the section from desert to oasis. This paper reveals that the aeolian environments are of great difference between desert and DOTA and the significant role of DOTA in protecting oasis. Integrated sand control system needs to be settled in the DOTA to strengthen its buffering function. Copyright © 2016 John Wiley & Sons, Ltd.

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Wind tunnel Study on Aeolian Saltation Dynamics and Mass flow

Cited by 13 publications

References 32 publications

Aerodynamic roughness lengths over movable beds: Comparison of wind tunnel and field data

Aerodynamic roughness lengths over movable beds: Comparison of wind tunnel and field data

Computational modeling of the wind erosion on a sinusoidal pile using a moving boundary method

Key Role of Desert–Oasis Transitional Area in Avoiding Oasis Land Degradation from Aeolian Desertification in Dunhuang, Northwest China

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