Roughness estimation for wind-load simulation experiments

Tieleman, H. W.

doi:10.1016/s0167-6105(03)00058-8

Cited by 31 publications

(28 citation statements)

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“…with the displacement height d. It has been pointed out [36] that the roughness parameters should not be based on observations below the blending height z*, defined as the height above the ground where the momentum flux becomes one-dimensional and the average flow no longer varies with the horizontal location [36]. A minimal estimate of z* is 1.5H, where H is the typical obstacle height or roughness height; for very low roughness lengths z* can be estimated by 20z 0 .…”

Section: Assessment Of Roughness Length Under Neutral Conditionsmentioning

confidence: 99%

“…This approach was pioneered by Davenport and refined by Wieringa [38][39][40] based on carefully selected experimental results for homogeneous terrain. Table 1 is an updated version of the Wieringa roughness classes [36], providing the ranges of roughness length z 0 , roughness height H, displacement height d and blending height z* for a series of homogeneous terrain types.…”

Section: Assessment Of Roughness Length Under Neutral Conditionsmentioning

confidence: 99%

“…If the distance between the obstacles is large enough (greater than about 15 times the obstacle height), the mean wind speed adapts quickly to the local terrain between the large roughness elements. However, the local value of z 0 obtained from these wind speed profiles should not be used to obtain the turbulence intensities because the large-scale wake turbulence created by these obstacles is quite persistent and takes a much longer distance to adapt to the local terrain [36]. The equivalent roughness length values z 0,equiv to be used in this case for the determination of turbulence intensity are given in Table 2.…”

Section: Assessment Of Roughness Length Under Neutral Conditionsmentioning

confidence: 99%

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State of the Art and Trends in Wind Resource Assessment

Probst

Cárdenas

2010

Energies

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Given the significant rise of the utilization of wind energy the accurate assessment of the wind potential is becoming increasingly important. Direct applications of wind assessment techniques include the creation of wind maps on a local scale (typically 5-20 km) and the micrositing of wind turbines, the estimation of vertical wind speed variations, prospecting on a regional scale (>100 km), estimation of the long-term wind resource at a given site, and forecasting. The measurement of wind speed and direction still widely relies on cup anemometers, though sonic anemometers are becoming increasingly popular. Moreover, remote sensing by Doppler techniques using the backscattering of either sonic beams (SODAR) or light (LIDAR) allowing for vertical profiling well beyond hub height are quickly moving into the mainstream. Local wind maps are based on the predicted modification of the regional wind flow pattern by the local atmospheric boundary layer which in turn depends on both topographic and roughness features and the measured wind rose obtained from one or several measurement towers within the boundaries of the planned development site. Initial models were based on linearized versions of the Navier-Stokes equations, whereas more recently full CFD models have been applied to wind farm micrositing. Linear models tend to perform well for terrain slopes lower than about 25% and have the advantage of short execution times. Long-term performance is frequently estimated from correlations with nearby reference stations with concurrent information and continuous time series over a period of at least 10 years. Simple methods consider only point-to-point linear correlations; more advanced methods like multiple regression techniques and methods based on the theory of distributions will be discussed. Both for early prospecting in regions where only scarce or unreliable reference OPEN ACCESSEnergies 2010, 3 1088 information is available, wind flow modeling on a larger scale (mesoscale) is becoming increasingly popular.

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Section: Assessment Of Roughness Length Under Neutral Conditionsmentioning

confidence: 99%

Section: Assessment Of Roughness Length Under Neutral Conditionsmentioning

confidence: 99%

Section: Assessment Of Roughness Length Under Neutral Conditionsmentioning

confidence: 99%

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State of the Art and Trends in Wind Resource Assessment

Probst

Cárdenas

2010

Energies

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“…In principle, the obstacle and covering drop the wind speed, therefore, suspension particle retard and deposit easily (Youssef et al, 2012). Especially, the vegetation covered riverbed increase the surface roughness and roughness height (Counihan, 1975;Tieleman, 2003), it change the Aeolian dust diffusion. According to these reasons, at slower wind speed condition, the downstream village's ambient air quality might not be affected by coarse dust even if the sediment suspension from riverbed.…”

Section: Horizontal and Vertical Characteristics Of Aeolian Dustmentioning

confidence: 99%

“…3. In fact, the discrepancy of wind speed in the atmospheric boundary-layer was influenced by terrain, vegetation and structure (Conan et al, 2015) due to the surface roughness length increasing (Tieleman, 2003). Besides, the accumulated precipitation from November 23 th to 26 th , 2010 was zero in order to obviate the influences of moisture.…”

Section: Horizontal and Vertical Characteristics Of Aeolian Dustmentioning

confidence: 99%

The Horizontal and Vertical Characteristics of Aeolian Dust from Riverbed

Syu

Cheng

Kao

et al. 2016

Aerosol Air Qual. Res.

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Arid riverbed is an important source of Aeolian dust to influence the atmosphere of nearby village or downtown. In general, Aeolian dust pertains to wind activity in the study of geology, environment and meteorology on particle suspension. This field study set the horizontal/vertical sampling system up in order to investigate the Aeolian dust dimensional distribution properties from the riverbed of the Jhuoshuei River. In addition, the study utilized the unmanned aerial vehicle (UAV) imagery to discriminate the covering condition of riverbed. The results revealed that the percentage of riverbed covering conditions, including bare zone, wetlands, green covering and water covering was about 50.1, 15.7, 16.8 and 17.4%, respectively, on January 5 th , 2011. Two Aeolian dust cases from riverbed were measured on November 26 th , 2010 and January 15 th , 2011. First sampling case was under slower wind speed, and the total particulate matter (PM) concentration in vertical sampling was almost decreased with increasing sampling height, however, the phenomena of second case (faster wind speed) was just on the contrary. Besides, Aeolian dust was distributed in a bimodal or a multimodal curve, and the main peak size was above 10 µm. Mode size of suspension particle diameter was increased as the wind speed increased (14.8 µm/ first case; 21.3 µm/ second case). The total mass concentration ratio of south site to source site (one meter height) was about 1:9 in first case and about 1:3 in second case. Comparing with those two cases, the faster the wind is, the shorter the surface roughness height is (3.08 mm/ first case; 1.07 mm/ second case). Besides, as the wind speed increasing, the friction velocity was also increased (0.33 m s -1 / first case; 0.68 m s -1 / second case). Consequently, this study clued the spatial variability of river dust events, which can further aid the site investigating, forecasting and preventing of dust influences.

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RANS simulations of neutral atmospheric boundary layer flow over complex terrain with comparisons to field measurements

Han

Stoellinger

2019

Wind Energy

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Micro-scale Reynolds-averaged Navier-Stokes (RANS) simulations of the neutral atmospheric boundary layer (ABL) over complex terrain and a comparison of the results with conditionally averaged met-tower data are presented. A robust conditional sampling procedure for the meteorological tower (met-tower) data to identify near-neutral conditions based on a criterion for the turbulence intensity is developed.The conditionally averaged wind data on 14 met-towers are used for the model validation. The ABL flow simulations are conducted over complex terrain which includes a prominent hill using the OpenFOAM-based simulator for on/offshore wind farm applications (SOWFA) with the k−ϵ and the SST k−ω turbulence models. The discretization of the production term in the transport equation for the turbulent kinetic energy (TKE) is modified to greatly reduce the commonly observed nonphysical near surface TKE peak. The driving inflow is generated through an iterative approach using a precursor method to reproduce the measured wind statistics at the reference tower. Both of the RANS models are able to capture the flow behavior windward of the hill. The SST k−ω model predicts more intense flow separation than the k−ϵ model downstream of the steepest sections of the hill. The wind statistics predicted at the location of the met-towers by both of the RANS models are fairly consistent. Overall, the comparisons of the direction, mean, and standard deviation of the wind between the simulations and the tower data show reasonable agreement except for the differences of the mean wind speeds at four met-towers located closer to the main ridge of the hill in a region of strong terrain variations. KEYWORDSatmospheric boundary layer, complex terrain, field measurements, k−ϵ model, neutral stability, Reynolds-averaged Navier-Stokes (RANS) simulations, SST k−ω model terrain with hills, ridges, and even mountain slopes. The interaction of these complicated landforms with the atmospheric boundary layer (ABL) turbulent flow may greatly affect both the character of the large scale flow over wind farms and the local flow features such as flow acceleration, separation, and recirculation. Therefore, a detailed wind resource analysis for a prospective wind site is important for the wind engineers to accurately predict the power output from the entire wind plants.The most direct way to obtain wind resource information for a prospective wind farm is to conduct field measurements with meteorological towers (met-towers). The monitoring duration for the met-tower observation typically lasts for at least 1 year to obtain a sufficient number of samples so that reliable statistical analysis of diurnal and seasonal variability of the wind can be determined. 2 Due to cost limitations, the mettowers are usually sparsely distributed across the wind farm site, and thus, they only provide a highly localized assessment of the wind conditions. Extrapolation of the wind conditions from the tower locations to other regions of the wind farm is difficult in complex terrain...

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Roughness estimation for wind-load simulation experiments

Cited by 31 publications

References 4 publications

State of the Art and Trends in Wind Resource Assessment

State of the Art and Trends in Wind Resource Assessment

The Horizontal and Vertical Characteristics of Aeolian Dust from Riverbed

RANS simulations of neutral atmospheric boundary layer flow over complex terrain with comparisons to field measurements

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