Experimental Investigation on Shear-Stress Partitioning for Flexible Plants with Approximately Zero Basal-to-Frontal Area Ratio in a Wind Tunnel

Kang, Liqiang; Zhang, Junjie; Yang, Zhicheng; Zou, Xueyong; Cao, Hong; Zhang, Chunlai

doi:10.1007/s10546-018-0373-3

Cited by 26 publications

(37 citation statements)

References 23 publications

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“…The details of the wind tunnel and the roughness elements have been described by Kang et al. (2018).…”

Section: Methodsmentioning

confidence: 99%

Influence of Wind Velocity and Soil Size Distribution on Emitted Dust Size Distribution: A Wind Tunnel Study

Wang

et al. 2021

JGR Atmospheres

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The question of whether the emitted dust size distribution (EDSD) depends on wind velocity or soil size distribution (SSD) remains to be resolved. This study used wind tunnel tests to investigate the influence of wind velocity and SSD on EDSD. The results showed that EDSD was influenced by both wind velocity and soil texture, supporting the saltation bombardment theory. The ratios of PM2.5 to PM10 (RPM2.5/10) and PM1.0 to PM10 (RPM1.0/10), which reflect the EDSD in the range of 0–10 μm, both showed trends of increase with increasing friction velocity (u*) for all six tested soils. The trend of increase was more significant for RPM1.0/10 than for RPM2.5/10, which indicates that the content of finer dust in the range 0–10 μm increased with increasing u*. When u* increased from 0.51 to 0.68 m s−1, the ranges of increase for RPM2.5/10 and RPM1.0/10 were most significant for sandy soils, followed by sandy loam soils and then loamy soils, indicating that the effect of wind velocity on EDSD increased with increasing sand content in the soils. The EDSD was also influenced significantly by the fully dispersed SSD, especially the sand and silt contents in the soils. The RPM2.5/10 and RPM1.0/10 increased (decreased) logarithmically (linearly) with increasing sand (silt) content in these soils. The finding of this study represents enrichment of data regarding EDSD, which could lead to greater understanding of the roles of wind velocity and SSD in relation to EDSD.

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“…The details of the wind tunnel and the roughness elements have been described by Kang et al. (2018).…”

Section: Methodsmentioning

confidence: 99%

Influence of Wind Velocity and Soil Size Distribution on Emitted Dust Size Distribution: A Wind Tunnel Study

Wang

et al. 2021

JGR Atmospheres

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“…The total drag on a rough surface (τ) can be divided into the pressure drag on the roughness elements (τ r ), the skin drag on the exposed soil surface (τ s ), and the skin drag on the top surface of the roughness elements (τ c ) (Shao, 2008). When examining the shear-stress partitioning model, τ, τ r , τ s and τ c are generally regarded as the shear stresses on a rough surface exerted by the wind, corresponding to the total shear stress acting on a rough surface and the shear stress acting on the roughness elements, exposed soil surface and top surface of the roughness elements, respectively (Kang et al, 2018;Raupach et al, 1993;Shao, 2008;Walter, Gromke, & Lehning, 2012).…”

Section: Shear-stress Partitioning Modelmentioning

confidence: 99%

“…The total shear stresses on the surfaces were measured using a system of floating and force-measurement devices (Figure 1d), which had previously been used and demonstrated to be reliable (Kang et al, 2018) The total shear stress on a rough surface corresponded to the total shear stresses on both the roughness elements and bare surface.…”

Section: Measurement and Calculation Of The Total Shear Stressmentioning

confidence: 99%

“…The measurement system consisted of three parts: a shear stress sensor, differential pressure transducer and signal acquisition device. The shear stress sensor consisted of 32 Irwin-type sensors (Irwin, 1981) The calculated shear-stress partitioning results were minimally affected by extremely few data points that deviated more than 3% (Kang et al, 2018). Therefore, the results of the shear-stress partitioning calculated using the data measured using the Irwin-type sensors were considered to be reliable.…”

Section: Measurement and Calculation Of The Total Shear Stressmentioning

confidence: 99%

“…Among the existing models, several models are based on the concept of RM93 (Chappell & Webb, 2016;Shao, 2005), whereas certain models are revised variants of RM93 (Crawley & Nickling, 2003;Kang et al, 2018;Walter, Voegeli, & Horender, 2017). Since RM93 was built on the foundation that the results of wind-tunnel experiments are general or universal across scales (Chappell & Webb, 2016), the shear-stress ratios quantitatively described by RM93 can be directly applied in the field to estimate u *t on a patch landscape scale (Gillette et al, 2006;Gillette & Pitchford, 2004;King et al, 2005;Tan et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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A modified Raupach's model applicable for shear‐stress partitioning on surfaces covered with dense and flat‐shaped gravel roughness elements

Zou

Zhang

et al. 2021

Earth Surf Processes Landf

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A commonly used measure to prevent soil wind erosion is to cover the surface with gravel. Gravel can inhibit soil erosion by covering the surface directly, changing the airflow field near the surface and sharing the shear stress of wind. Similar to other roughness elements, the protective effect of gravel on soil is usually expressed in terms of the ratio of the shear stress on the exposed soil surface to the total shear stress on the rough surface due to wind, i.e. through a shear-stress partitioning model. However, the existing shear-stress partitioning models, represented by Raupach's model (RM93), are only applicable when the lateral coverage of the roughness elements, λ < 0.10, and the applicability of the models to flat-shaped roughness elements is unclear. The purpose of this study is to verify the applicability of RM93 for dense and flat-shaped gravel roughness elements by using shear-stress data from wind-tunnel measurements pertaining to roughness elements with different densities (0.013 ≤ λ ≤ 0.318) and flat shapes (height-to-width ratios in the range 0.20 ≤ H/W ≤ 0.63), and to modify RM93 to enhance its predictive ability. The results indicate that RM93 cannot accurately predict the shear-stress partitioning for surfaces covered by densely distributed and flat-shaped gravel roughness elements. This phenomenon occurs because, when roughness elements are distributed densely or are flat-shaped, the proportion of the shear stress on the top surface of the roughness elements (τ c ) to the total shear stress (τ) is large; in this case, τ c plays a dominant role and serves as an essential component in the shear-stress partitioning model. Consequently, RM93 is modified by incorporating τ c into the calculation of τ. Under conditions of λ < 0.32 and H/W > 0.2, the modified RM93 can yield satisfactory predictions regarding the shear-stress partitioning.

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A general model for predicting aeolian transport rate over sand surfaces with vegetation cover

Liu

Cao

et al. 2022

Earth Surf Processes Landf

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Aeolian sand transport is caused by wind erosion, which is the main cause of environmental problems such as land desertification, blown sand disasters and air pollution. Previous research results and engineering practices have confirmed that vegetation improvement has long‐term and comprehensive benefits for controlling aeolian sand transport. The role of vegetation in aeolian sand transport can be explained by the complexity of atmospheric, earth and biosphere systems. However, the lack of a universal model used to predict aeolian transport rate on vegetated surface results in different understandings about vegetation effects due to different experimental methods and materials. To clarify the effect of vegetation on aeolian sand transport, we used a large wind tunnel whose experimental section was 24.0 m‐long, 3.0 m‐wide and 2.0‐m high to provide enough length for aeolian transport and designed a set of novel experimental methods to improve the measurement accuracy of the aeolian transport rate. Based on experimental data, we developed a new equation for aeolian transport rate over bare and vegetated surfaces. We determined that models of aeolian transport rate over bare surface in the literature underestimated the aeolian transport rate measured in this study by 63–80%, 89%, 50% and 35%, confirming that the transport rate decreases exponentially as vegetation coverage increases. However, the power exponent is a variable related to particle size and wind velocity as opposed to a constant in the existing literature; thus, we proposed a dimensionless parameter to express the relationship between the power exponent with particle size and wind velocity and constructed the new equation for vegetated surfaces to be more widely applicable. These results would help to understand the effect of vegetation cover on aeolian sand transport process and to improve the effectiveness of vegetation prevention and control.

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Experimental Investigation on Shear-Stress Partitioning for Flexible Plants with Approximately Zero Basal-to-Frontal Area Ratio in a Wind Tunnel

Cited by 26 publications

References 23 publications

Influence of Wind Velocity and Soil Size Distribution on Emitted Dust Size Distribution: A Wind Tunnel Study

Influence of Wind Velocity and Soil Size Distribution on Emitted Dust Size Distribution: A Wind Tunnel Study

A modified Raupach's model applicable for shear‐stress partitioning on surfaces covered with dense and flat‐shaped gravel roughness elements

A general model for predicting aeolian transport rate over sand surfaces with vegetation cover

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