Wind tunnel experimental analysis of a complex terrain micrositing

Renewable and Sustainable Energy Reviews 54 (2016) 110–119

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Wind tunnel experimental analysis of a complex terrain micrositing J.M.L. Mattuella a,n, A.M. Loredo-Souza a, M.G.K. Oliveira b, A.P. Petry c a b c

Department of Civil Engineering, Construction Aerodynamics Laboratory, Federal University of Rio Grande do Sul, Porto Alegre, Brazil Vento-S Consulting, Brazil Department of Mechanical Engineering, Federal University of Rio Grande do Sul, Porto Alegre, Brazil

art ic l e i nf o

a b s t r a c t

Article history: Received 31 October 2014 Received in revised form 12 June 2015 Accepted 18 September 2015

The technical and economic feasibility of wind energy projects are defined by identifying the correct wind potential in the site and by the technological choice of equipment. The optimal micrositing of wind turbines determines the success of the project. Most current tools are insufficient to evaluate air flow in a complex terrain where wind effects such as acceleration, deceleration are difficult to be predicted The uncertainties related to the energy outcome present an increasing problem as the precision regarding the amount of the energy that may be commercialized is even higher. The combined use of wind tunnel and mesoscale numerical modeling represents the solution for wind power site assessment in a complex terrain. This paper presents a review of the contribution that wind tunnels have recently made for physical modeling of both the velocity field and the turbulence intensity as a methodology for the atmospheric boundary layer study in a complex terrain. Hence, it describes an experimental simulation of the atmospheric boundary layer (ABL) in a wind tunnel over a complex area to characterize the mean flow (detachment and reattachment) and the turbulence intensity with emphasis in the wind energy production. The experiment was conducted in a wind tunnel and employed two terrain categories: Category I – plain terrain and Category III-IV – moderately rough, corresponding, respectively, to the power law exponent p¼ 0.11 and p¼0.23. The complex terrain wind profiles were correlated with that in the plain terrain to show the changes of the velocity and show the extension of turbulence wake caused the by variable topography of the area. The measurements of the wind velocity and turbulence intensity were performed with a hot wire anemometry system. Results demonstrate that velocity profile and turbulence intensity profile vary significantly over the complex area, which makes an accurate experimental evaluation necessary to certify the micrositing layout. Power losses due to wake effects can easily reach 20% of the total power, which may make a plant infeasible. & 2015 Elsevier Ltd. All rights reserved.

Keywords: Atmospheric boundary layer Complex terrain Micrositing Wind energy Wind tunnel

Contents 1. 2. 3.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Research in complex terrain – models and methodologies-a review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Atmospheric boundary layer (A.B.L) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Flow over complex areas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Wind power assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Wind tunnel experimental simulation methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Experimental set up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. Experimental model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Results and discussion. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

n

Corresponding author. Tel.: þ 55 51 3308 3745; fax: þ 55 51 3308 3746. E-mail address: [email protected] (J.M.L. Mattuella).

http://dx.doi.org/10.1016/j.rser.2015.09.088 1364-0321/& 2015 Elsevier Ltd. All rights reserved.

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Nomenclature

α ABL CFD GW HP L LES MW RANS T U(z) U(z1) U(z)d U(zr)

Shear parameter Atmospheric Boundary Layer Computational Fluid Dynamics gigawatt Horse Power hill characteristics length (m) Large Eddy Simulation megawatt Reynolds Averaged Navier Stokes period de integralização dos valores (t) mean wind speed, (m s  1) wind speed at the required or extrapolated height z1 (m s  1) wind speed (dimensioness) wind speed at the reference elevation zr (m s  1)

1. Introduction Wind represents an important source of energy and it plays, even more, a crucial role in the future energy supply worldwide. Brazil, in recent years, has shown an increase higher than that of economies like China, United States and India. In 2014, the wind capacity installed in Brazil reached 3369.8 MW, representing an increase of approximately 70% over the previous year. The wind growth forecast for 2016 is 40%, whereas that in a global scale is 9% [1]. According to the expansion plan of the Brazilian Electric System it is expected, that 8% of the electrical demand in the country will be supplied by wind energy until 2030 [2]. The total installed capacity of wind power plants in operation in Brazil, including those under construction and/or contracted until the year 2018, reaches 13,807 MW [1]. The estimated wind potential in this country is approximately 300 GW for 100 m height for wind velocity equal or greater than 7 m s  1 [2]. Currently, most of the wind power plants in Brazil are located on the coast. However, the intended future expansion will require the installation of wind farms in areas which include the variability of the topography and roughness. In addition, the Brazilian Institutional Model has recently defined that the physical guarantee energy of wind plants to be delivered to the interconnected system considers the value of annual energy with a probability of occurrence equal to or greater than 90% of the Reference Energy as contracted in the energy auction. This reduces the risk of non-compliance with the contracted energy in 10% [2]. These premises define that a greater accuracy in the definition of the wind potential is necessary in the project area, so that such potential is confirmed in the plant in operation. In this context of wind development, it is crucial to estimate both the technical and economical wind potential in complex areas. Wind speed is the most important parameter in the wind energy conversion devices and utilization of potential areas for micrositing. The energy which is obtained from wind is directly proportional with the cubic power of the wind speed. In complex the wind flows are highly dependent on the local topography and roughness, since their patterns vary locally. As a result of the change of wind behavior, acceleration or deceleration occur, as well as change of direction of the airflow [3]. If the area is a complex terrain, anemometric measurement may not be enough to analyze the assessment area. In order to take the uncertainty of the wind into account, computational and experimental studies are tools that improve the evaluation of the area. The technical

d kHz Iu p s u (rms) m* V v(t) x z zH z1 zo zr

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height elevation (m) kilohertz turbulence intensity (%) power law exponent area second (s) ratio of the standard deviation to the mean wind speed (dimensionless) logarithmic law parameters average wind speed defined by Eq. (02) (m s  1) individual value of wind velocity, (m s  1) horizontal coordinate, (m) vertical coordinate, (m) hub height equipment, (m) height of projection of wind speed (m) surface roughness (m) reference height of wind speed (m)

evaluation of the wind potential resources of an area is based, preliminarily, on site measurements performed with the use of anemometers and wind vanes. This methodology provides good response to analyze the wind behavior in flat terrains. However, the flow measurements (speed, direction, turbulence intensity and spectrum) over complex terrains become usually insufficient, when using only the mentioned method, particularly regarding the identification of the turbulence areas and the inclination flow. These parameters define the most appropriate wind farm configuration, in order to obtain the maximum array efficiency and the minimum wake losses, to confirm the predicted energy outcome. The experimental simulation of Boundary Layer Wind Tunnel represented an advance in the study of flow separation and reattachment around bluff bodies of complex geometry. According to Loredo-Souza [4] from such simulation it is possible to parameterize the effects of the wind over a complex terrain. A complex and heterogeneous area containing a main hill with a 34° slope surrounded by other lower ones is investigated in a wind tunnel, in order to analyze the structure of the turbulent flow and characterize the detachment and reattachment of the flow with focus on the identification of the turbulence areas with emphasis in the micrositing wind turbines. The experimental simulations were conducted in the Atmospheric Boundary Layer Wind Tunnel Prof. Joaquim Blessmann, of Federal University of Rio Grande do Sul [5] over a tri-dimensional reduced model scale. Twelve points were measured and 24 profiles were constructed considering two types ABL, corresponding of two terrain categories, Category I – plan and Category III-IV – moderately rough, corresponding, respectively, to the power law exponent p ¼0.11 and p ¼0.23 [6]. The complex terrain wind profiles were correlated with that in the flat terrain to show the topographic interference on the airflow. The measurements of the wind velocity and turbulence intensity were performed with a hot wire anemometry system as reported by Mattuella [7]. Intensive research involving the numerical simulation [8] and/ or experiments [7] has been carried out with the purpose of evaluating more precisely the wind potential in complex terrains. Field and wind tunnel experiments play a key role to study the interactions between the atmospheric boundary layer and wind turbines in wind farms as observed by Petry [9]. In particular, wind tunnel experiments offer valuable insights about the turbulent flow structure in micrositings. The average efficiency of an array of wind turbines found in classical models often overestimates the efficiency of large wind farms. Among the contributions of this

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article, it consolidates the experimental study in a Boundary Layer Wind Tunnel as an important step towards the identification of the most suitable spot in a complex area for micrositing of wind farm. The methodology based on identification of wind potential at the generation points entails the construction of wind profiles and the turbulence intensity considering the related aspects, such as topographic changes, roughness and wakes [10,11]. These aspects minimize the risk of the real energy outcome been lower than that contracted in the energy auctions.

2. Research in complex terrain – models and methodologies-a review In the 1960s, researchers proved that it was possible to obtain the structure of the wind in an experimental environment called wind tunnel, which had part of its floor covered with physical elements capable of simulating the structure of the atmospheric boundary layer[12] in order to obtain the effect of the surface roughness. Experimental studies from wind tunnel models provide situations in which analytical methods cannot be used to estimate wind loads [13]. The foundation for the study at atmospheric boundary layer flow over complex terrain was laid in the early 1970s. In 1975, an analytical model for a two-dimensional flow over a low and isolated hill was developed [14,15]. The dimensions of the hill were such that “d«L”, where d is the height of elevation and L is the characteristic length of the hill in the direction of the flow. The surface roughness zo was considered uniform and without separation of the airflow. The results obtained testified that the wind speed and the cutting forces were proportional to the size, shape and roughness of the hill for slopes where “d/L«1” and that within these limits it would be possible to employ the equations of fluid movement. One of the most significant results obtained at the time was that the change of flow in the presence of low and isolated hills, the wind speed, and cutting efforts were proportional to the size, shape, and roughness of such hills [15]. In addition to these variables, the study of turbulence in twodimensional aspects and the variation of the oscillations of the vertical component of wind and the Reynolds Stress were consolidated in ABL research in a wind tunnel [13,14]. In the investigation of complex terrain, numerical models were more extensively employed in the 70s [16,17]. The Jackson and Hunt model of 1975 [14] was extended to three-dimensional analysis of an asymmetric hill [18]. The results of that period suggested that the most relevant changes in the turbulence intensity characteristics occur only in the wake area downwind of the hill, where the transfer of energy to higher frequencies is more evident [18]. In the 80s, the assessment of the flow behavior on large hills such as Blashaval, Askervein and others confirmed the thesis of 1975 [15]. Britter, Hunt and Richards (1981) showed that the speed up effect at the crest of the hills was due to both the slope and the surface roughness [19]. Teunissen [20] presented the comparison investigation between wind-tunnel modeling and full-scale measurement. Wind-tunnel values of fractional speed-up ratio at various locations and heights above the hill are presented and compared with the corresponding full-scale values and with mathematical-model results in Table 1. In general, the agreement between the model and full-scale values was representative, with a tendency, in some cases, for the wind tunnel to overestimate the values observed in full scale [20] The evolution of research in this area improved with focus on large hills such as: Sirhowy Valley the Blashaval Hill [21], Askervein Hill [22] and Kettles Hill [23], which meant important advances in theoretical modeling of the airflow on low and

Table 1 Comparative study for speed up effect at the crest of the hill with three methodologies: field measurements, mathematic models and wind tunnel [20]. Wind direction (deg)

Height “z” (m)

Fiel measurement Mathematic models

2d/L

245

10 10 3 10

0.40 0.34 0.31 0.64

0.47 0.47 0.47 0.62

220

0.33 0.33 0.39 –

isolated hills. The best documented aspect of the airflow of turbulent boundary layer on this type of topographic feature at that time was the behavior of horizontal average speed. The main features of this field were identified as: a small reduction in speed in the lower levels, upwind side of the hill, the identification of the incremental wind speed at the top of the hill (the speed up effect), and the formation of a turbulent wake leeward of the hill [24]. Comparative methodologies between a wind tunnel and fullscale pressure measurement tests are still being used in the research evolution in wind engineering in pressure measurements for structural loads, as well as for wind velocity and turbulence intensity profiles construction. As an example for the first case, Hisashi Okada and YoungCheol Ha developed, in 1992, the high-quality data scale-model tests performed in wind tunnels for comparison with full-scale models. The results showed good agreement of wind tunnel tests with tests on the full-scale building model tests in terms of mean wind-pressure coefficients. The results of the effect of sizereduction ratio on the mean as well as rms wind-pressure coefficients were found to be insignificant within the range tested [25]. Kim and Patel in 1997 sought to validate mathematical models for airflow behavior forecast on hills with performing experiments in tunnel and numerical simulations on complex terrain. In wind tunnel experiments they employed the three-dimensional model measured with pitot tube. Such experiments improved the understanding of the wake recirculation in the downwind. Comparisons obtained by experimental results with numerical simulations matched both the average speed values and pressure distribution [26]. Rasouli and co-workers [27] also investigated threedimensional models, they presented a study of complex terrain in a wind tunnel experiment, using Particle Image Velocimetry, the technique provide velocity fields that permit identify the feature of the flow. In the 20th Century, Kim and Patel showed the characterization of the airflow phenomenon, especially according to its detachment and reattachment over topographic models in 2D and 3D analyses. The results obtained indicated that Reynolds Stress increased rapidly in regions of the boundary layer with a strong adverse pressure gradient. Such increase remained even when the flow reached the reattachment point, when turbulence decreased at the same time, and when the mean velocity profiles started to recompose [28]. In the 21st century, Kastner and Rotach presented wind tunnel measurements compared with Laser-Doppler velocimetry in 2003. In this study, a detailed model of an urban landscape has been reconstructed in the wind tunnel and the flow structure inside and above the urban canopy was investigated. Vertical profiles of all three velocity components were measured with a Laser-Doppler velocimeter, and an analysis of the measured mean flow and turbulence profiles was carried out. The results showed that the mean wind profiles above the urban structure follow a logarithmic wind law. Inside the roughness sub layer, a local scaling approach results in good agreement between measured and predicted mean wind profiles [29].

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Derickson et al. in 2004 investigated the wind behavior from the top of embankments to their foot, considering various terrain slopes. Therefore, a hybrid instrument which included wind tunnel and computational numerical mesoscale model was used. This study found that the behavior of a flow is a function of the characteristics of the terrain. The separation processes and airflow reattachment are unpredictable and irregular, as is the character of turbulent flow in general, which justifies the need for studies to be developed on a case [30]. This position was also held by Milller [24]. For the assessment of the wind resource in complex micrositings the use of the classical models are not reliable to predict wind behavior [30]. Current research in wind energy consists mainly of field measurements, being numerical simulations, and windtunnel measurements, essential complementary methodologies to the first one. Field measurements are the basic assessment of the micrositing. Such methodology offers limitations regarding the scope of the values measured. Thus, it is necessary to associate the modeling tool of the airflow in order to provide reliable results for different types of cases with different boundary conditions, according to topography and roughness. There is a need for these models to be validated, which can be done with either controlled laboratory experiments. Research in tunnels play an important role on the definition and validation of micrositings, especially in the study of the wind velocity profiles, turbulence intensity profiles and wakes, both coming from the topography, as well as from the other wind turbines in order to confirm the wind power expected in a power plant. A detailed characterization of the turbulent flow in a wind farm is a challenging task, since in such places multiple and superimposed factors coexist, such as roughness, topography and turbulence levels among other factors, which hinder the perfect understanding of the airflow in the area. In the micrositing layout the incoming flow field is characterized by unsteady velocity profile and turbulence intensities significantly higher than the ones in the free stream. The velocity deficit reduces the available energy in the wind and, consequently, the power produced by the downstream turbines. High turbulence intensities induce strong variable forces on the blades and can significantly increase the fatigue. Furthermore, the array efficiency varies according to inter-turbine and inter-flow interference. By knowing the turbulence areas, it is possible to locate the wind turbine points on the micrositing in order to allow the maximum array efficiency and the minimum wake losses [37,38]. The layout will finally establish the number, the precise location and spacing between turbines. The next step is the calculation of the actual wind energy production. Aiming at this, current study introduces the experimental methodology in wind tunnels for the wind parameters evaluation in wind farm projects [33]. Modeling and evaluation of the wind velocity, the turbulence intensity profiles on complex micrositings for wind power assessment using numerical and experimental methodology compared has been recently reported by Mattuella, Loredo-Souza and Petry [9,39,40].

3. Atmospheric boundary layer (A.B.L) Wind is the result of pressure difference between air masses flowing over the surface of the Earth. A.B.L. can be defined by the profile variability of wind speed, which is proportional to physical phenomena (such as drag force, evaporation, evapotranspiration, heat transfer, and pollutant emission) generated by the topography, roughness or obstacles located on the surface, which slow down the wind near it [4]. Wind structure is mainly the result of the nature of the underlying (vertical air temperature gradient) and surrounding surface. Turbulence, which is mostly of mechanical origin, is caused by the drag effects on a surface,

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whereas its thermal origin comes from the heat flows which lead to convective motions. They cause rapid alternations in wind velocity over a wide range of frequencies and amplitudes, commonly called gust [31]. In this layer, where the effects of surface friction are dominant, the effects of Earth's rotation like the Coriolis force can be neglected. The ABL is the lowest region of the troposphere directly affected by the physical characteristics of the surface of the earth. The thickness of ABL corresponds to gradient height (zg). A typical value to strong winds is 2500 m, depending on the roughness of the terrain [4,,13]. 3.1. Flow over complex areas The study about the development of the turbulent boundary layer is fundamental for analyzing the micrositing of wind turbines, as well as for assessing wind load on structures, especially in areas characterized by variable topography and/or by high surface roughness, the so-called complex terrains. Basically, the flow over an isolated hill can be described by the increment in wind speed at the crest of the hill, called the speed-up effect, and the associated deceleration of the flow on the leeward side of hill, with the beginning of the formation of turbulence area and airflow detachment. Flow recirculation occurs at the foot of the hill determining the so-called wind wake [32]. When the topographic characteristic is sufficiently abrupt around 30–40%, a strong adverse pressure gradient occurs, which causes the deceleration at the base windward [33]. This fact gives rise to the local pressure adverse gradient. Thus, in order for a flow separation to take place two conditions are critical: the average velocity and its gradient must be zero, simultaneously, at the same point. These critical points are called "separation points" and determine the instability of the flow and the start of the turbulence process. The different wind profiles caused by turbulence, the increase in the wind speed at the top of the hill, the extent of the recirculation wake, and the reattachment length of the airflow are the key aspects for micrositing in complex areas. Several methods are accessible to model the wind in the atmospheric boundary layer (ABL) both at meso- and at microscale levels. Micro-scale models focus on the atmospheric processes at the lowest part of the ABL and do not exceed the length of one kilometer. In this case Earth's rotation is not taken into consideration and the flow is commonly considered neutral. In this scale the experiments are developed in a wind tunnel and constructed through numerical methods to predict wind fields in the surface layer [34]. The numerical methods, based on mass-conservation and Navier–Stokes equations, Computational Fluid Dynamics (CFD) models, such simulate de ABL modeling turbulent flow with the use of Reynolds Averaged Navier Stokes (RANS) or Large Eddy Simulation (LES) [9,34]. The experimental results in airflow over a two-dimensional triangular ridge confirm that the reattachment point of the current lines takes place in an extent equal to 13d, where d is the height of the hill. Comparatively, the turbulence Models k-ε and k-ω show that the reattachment point occurs in 9.8–11.5d. These results lead to the conclusion that the reattachment point evaluated by more than one method occurs in a range between 9.8d and 13d [35]. In an experimental study to characterize the performance of wind farms in atmospheric boundary layer flow over hilly terrain, measurement of the flow field was related with the wind loads and power output measurement to investigate the effect of topography on wind farm layout [36]. The results show that in front of the hill, the blockage effect of the downstream hill affects both the mean velocity and turbulence intensity in the airflow, which leads to the reduced power output and enhanced fatigue loads, acting on wind turbines in such locations. As the wind flows up the hill,

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the speed-up effect highly increases the mean velocity and decreases the turbulence level in the flow. Based on this, we can conclude that placing wind turbines on top of the hills may be a good choice. The mean velocity decreases at a slow rate and reverts to the incoming flow profile after a short distance. The power output of wind turbines sited on the downhill can be even higher than that of flat terrain. However, the fatigue loads acting on wind turbines sited on hill peaks may increase greatly compared with flat terrain, which must be considered for the use of such places to install wind turbines in a micrositing [36].

4. Wind power assessment The general rules to be considered for the technical–economical wind energy power evaluation in a specific area are based on anemometric measurements collected by towers placed in a micrositing. The main parameters obtained are: (i) the mean wind speed recorded on annual or seasonal basis, (ii) the maximum values of wind speed, (iii) wind rose diagram (frequency of occurrence of specific wind speed and direction), (iv) the power spectrum of wind speed, and (v) height variation. This methodology provides good response to analyze wind behavior in a flat terrain. However, due to the multiplicity of factors of the air flow characteristics over complex terrains, the method referred is unable to evaluate wind potential, particularly regarding the identification and the extension of turbulence areas. The validated air flow parameters in an experimental simulation will provide the basic elements for the configuration of the wind farm. 4.1. Wind tunnel experimental simulation methodology Wind tunnels are capable of simulating experimental environments in the ABL with the use of different techniques for obtaining the average wind velocity, turbulence intensity, full scale and the power spectrum profiles corresponding to the type of terrain that it aims to simulate. In the early 60s, Jensen (1963, 1965) and Frank (1963) proved that it could be possible to obtain an appropriate scale of the structure of the natural wind covering a considerable length of the wind tunnel floor with a suitable roughness material. Davenport (1967), built a Boundary Layer Wind Tunnel in Canada and Cermark (1975, 1977) in the US [13]. Among the first researchers in a wind tunnel one can see the works of Counihan (1969), Cook(1975), Sykes(1977), Grenway (1977) and Blessmann(1982) reported by Whitter and LoredoSouza [13].

Several techniques have been used to artificially reproduce the thickness or depth of the boundary layer which vary greatly in sophistication and in working principles [13]. Typical examples are the use of fences, uniform grids, graded or sheared grids, jets, pulsation, wall roughness, steps and vortex generators. Such elements are usually placed on the wind tunnel surface at upwind test section. It is used to combine two or three elements [30]. The roughness is the most important component, since it defines the logarithmic law parameters (zo, m*), which describe the velocity profile in the wind tunnel. The development of the simulation methods evolved over the decades increasing their accuracy. Currently, they are consolidated in three types: roughness, barrier and mixing devices methods [13]. The simulation of specific locations with the use of random or regular arrangements of the roughness elements as used in the simulation methods to reproduce the correct characteristics of the general airflow, including the detailed representation of the surroundings (neighborhood models) are developed by Loredo-Souza [13]. The experimental boundary-layer wind tunnel simulations should attend the natural conditions in the environment [4] [13]. The following constitute similarity conditions that must be met between the natural and the experimental environment: a) geometric (similarity of detail and roughness of the surfaces in question); b) kinematics (the distribution of the average velocity and turbulence characteristic profiles should be similar between both environments); and c) dynamic (forces focusing on the body. The pressure forces, viscosity and inertia must be proportionate). In a natural flow, there are the forces of gravity, viscosity, inertia and pressure. For the experimental simulation, the forces of gravity are not considered. The turbulent Boundary Layer Wind Tunnel is obtained by roughening the surface such that the Reynolds number is greater than 2.5. The wind speed profile of the tunnel shall be described by the power law. It is shown by Eq. (01). Uðz1 Þ ¼ Uðzr Þ


α z1 zr

ð01Þ

where: α is the shear parameter and is commonly derived from measurements at two heights (z, zr) on a meteorological tower. It is characterized, among others, by two types of terrain: p ¼ 0.11 for flat terrain, such as sea and lakes and may vary up to p ¼0.34 for cities with high buildings.

Fig. 1. Boundary Layer Wind Tunnel Prof. Joaquim Blessmann – floor Plan.

J.M.L. Mattuella et al. / Renewable and Sustainable Energy Reviews 54 (2016) 110–119

4.2. Experimental set up The present experiments were conducted at Joaquim Blessmann Atmospheric Boundary Layer Wind Tunnel located at Laboratório de Aerodinâmica das Construções(Construction Aerodynamic Laboratory) of Universidade Federal do Rio Grande do Sul (Federal University of Rio Grande do Sul), Brazil. The LAC Wind Tunnel is a closed return low speed wind tunnel, specifically projected for the dynamic and static studies on civil construction models. Its design allows the simulation of the natural winds main characteristics. It has a length/height ratio on the main test section greater than 10 and dimensions of 1.30 m  0.90 m  9.32 m (width  height  length), as shown in Fig. 1. The maximum wind speed in this chamber, with soft and uniform flow is 42 m s  1 (150 km h  1). The propeller is driven by a 100 HP electric motor and the wind speed is controlled by a frequency inverter. The data acquisition is performed using a Dantec Dynamics anemometer, System 90 Streamline N S. The system uses a constant temperature

Fig. 2. The complex area in the LAC Wind Tunnel chamber.

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anemometer as reference. The hot wire probe and a calibrator are automatically integrated to the computer. The frame also has an input for a temperature sensor, which is designed to measure the flow temperature (fast fluctuations).The signal registration was done by the probe and the effective data acquisition was performed with the use of the Stream Ware application software, from same brand. The frequency was 2 kHz, and the acquisition period was 64 s. The data acquisition program is from Dantec Dynamic, and it also enables its own calibration and data accumulation. Mano Air 500 equipment measures the pressure difference between the piezometric rings, which are situated at the entrance of the tunnel and measure the average temperature inside it at an interval of 65 s [5]. The Boundary Layer Wind Tunnel Prof. Joaquim Blessman – floor plan is shown in Fig. 1. 4.3. Experimental model The tridimensional complex area was tested with the purpose to measure the flow and to show the results that can be obtained by a wind tunnel experimental research. The complex area cited is located in the state of Espírito Santo, Brazil. It comprises a main hill with a 34° slope, surrounded by several other lower ones. The model was constructed in a 1:1000 scale. In addition, it was built in layers in order to minimize the effects of the Reynolds number. This technique is used in several laboratories around the World [18]. The atmospheric boundary layer was simulated in the wind tunnel with the use of two types of methods: roughness and barrier. The first employed wooden blocks which covered the wind tunnel floor as shown in Fig. 2 [19]. The second employed triangular perforated plates at the entrance of the test chamber. The model was installed inside the tunnel chamber, in order to allow the experimental measurements. In Fig. 2 one can see the complex area in the tunnel chamber.

Fig. 3. Locations of measurement points in the complex area – floor plant and cutting plant.

Fig. 4. Coordinates measurement points located in main asymmetric hill.

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Fig. 5. Comparison of normalized wind speed profiles with pt 06 profile-plain terrain (a) points 01,02 and 03; (b) points 04,05 and 06; (c) points 07, 08 and 09; (d) points 10, 11 and 12.

Fig. 6. Comparison of normalized wind speed profiles measurements in the points evaluated (a) p¼ 0.11; (b) p ¼ 0.23.

The vertical profiles of turbulence intensity and wind speed were measured in two different types of terrain, related to power law exponents p ¼0.11 and p ¼0.23, according to Brazilian Standard Code NBR 6123 [6]. The power law is commonly used to extrapolate wind speed data from reference level to hub height. Velocity and turbulence profiles were constructed in 12 measurement points as seen in Fig. 3. Nine positions were located at the main radial direction of the flow above the main hill and three other points in the ground of the complex area, as shown in Fig. 4. In each point, both profiles were recorded at twenty (20) heights

of measurement: 10, 15, 20, 25, 30, 40, 50, 70, 100, 130, 160, 200, 250, 300, 350, 400, 450, 500, 550 and 600 (cm). For each height of a given point, 131,072 values were collected. It was possible to evaluate the velocity and turbulence extension due to the presence of the variability of the surroundings topography. The researched topographic complex model during the experimental tests in a wind tunnel is shown in Fig. 2. The average speed (daily, monthly and annual) constitutes an important feature for the definition of the technical condition of

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an area for wind generation. It is defined as Eq. (02). Z 1 t V¼ vðtÞdt T 0

ð02Þ

The dimensionless velocity U(z)d was defined by the quotient of Eq. (03) UðzÞd ¼

UðzÞ UðzÞpt06

ð03Þ

where: U (z) – wind velocity in the measurement height; U (z) pt 06 – wind velocity in the same height in the point 06 profile, plain terrain. Fig. 3 shows the location of measurement points evaluated in the complex area plan and cutting plan and Fig. 4 shows the coordinate points located in the main hill of the same environment.

5. Results and discussion In order to estimate the surface wind flow over a complex terrain, we analyzed velocity and turbulence profiles measured in models through an experimental study conducted in the wind tunnel. It was then possible to identify the different ABL profiles, focusing on the turbulence areas caused by the topography and the roughness of the environment. The velocity profiles of the measurement points from 01 to 12 shown in Fig. 3 were compared with the inflow velocity profile on a flat terrain, represented by point 06. Fig. 5(a)–(d) shows the comparison of normalized wind speed profiles with pt 06 profile-plain terrain employing two terrain

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categories, according to NBR Cat I-p¼0.11 and Cat III-IV-p¼ 0.23. The horizontal axis from comparisons represents the average velocity associated by the same height in the point 06-profile. The vertical axis represents the measurements z normalized by zH, which represents the hub height of the wind turbine that was considered to be 100 m. Subsequently, the wind experimental values normalized by point 6, for the points shown in Fig. 3 and Fig. 4, for two types of terrain in three heights 70 m, 100 m and 130 m are analyzed in Fig. 6 in (a) p ¼0.11 and (b) p¼ 0.23. The experimental study of the micrositing enables the identification of the turbulence areas, resulting from the site topography and wake effects, which leads to a reduction of the wind velocity. Turbulence intensity measured in 10 min is given by the standard deviation of longitudinal wind speed normalized by the mean wind speed and is expressed as a percentage according to Eq. (04): I¼

uðrmsÞ U

ð04Þ

where: u (rms) – ratio of the standard deviation to the mean wind speed; U – mean wind speed. The source of turbulence of the wind tunnel may have two causes: eddies (vortex shedding, boundary layer, shear stress, secondary flows) and noise (mechanical, vibration and aerodynamics). Fig. 7(a)–(d) shows the comparison among the turbulence intensity profiles for p¼ 0.11 and p ¼0.23 in points evaluated. The

Fig. 7. Comparison of turbulence intensity profiles in the points evaluated with the Standard Brazilian values for p¼ 0.11 and p ¼0.23: (a) points 01, 02 and 03; (b) points 04, 05 and 06; (c) points 07, 08 and 09; (d) points 10, 11 and 12.

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Fig. 8. Comparison of turbulence intensity profiles in all the points evaluated with the Standard Brazilian values for p ¼ 0.11 and p¼ 0.23. (a) p ¼ 0.11; (b) p ¼ 0.23.

vertical coordinates show height z normalized by rotor height zH, considered as 100 m. Fig. 8 shows the comparison of turbulence intensity profiles among all the points evaluated with the Standard Brazilian values in (a) p¼ 0.11 and (b) p ¼0.23. It is possible to notice that the turbulence occurs due to the influence of an adverse pressure gradient after the crest of the hill. It increases gradually along the lee-side of the hill, especially points 8, 9. Fig. 8(b) shows that the turbulence intensity levels is more intense for p ¼0.23 in all the points.

6. Conclusions Airflow was measured and analyzed over a complex area in a boundary layer wind tunnel. This study identified detachment, reattachment and the turbulence areas with the purpose of better understanding the topographic effects in the airflow upstream and downstream of the hill and their consequence for wind turbine in a complex micrositing. The results obtained are compatible with the research on the subject and can be summarized as follows. The locations of the upwind points from 01 to 06 of the main hill are representative of possible sites to install wind power generation. Point 01 represents the best potential of the micrositing, once speed up effect occurs in such point according to Fig. 5(a). Complex terrain areas are attractive potential turbine sites as they offer some of the highest wind resource available, since the power outcome is proportional to the cube of the local wind speed. However, characterizing the flow regime in these complex areas is a challenge as the flow is often highly turbulent as a result of flow separation. According to the evaluated declivity, the flow separation can be observed at points from 07 to 12 as shown in Fig. 5(c). These locations are not indicative for wind generation, since in these places the wind turbine would be immersed in the wake of the wind turbulence, caused by the topography of the main hill. The wake interference effects for the turbines located in the subsequent downstream rows cause severe influence in the overall power production according to Fig. 6(a) and (b) When the micrositing has different types of roughness and topographic variability, the average wind speed per generation point will probably be lower than the average wind speed calculated by all micrositing points. This implies in the feasibility of the projects, according to Fig. 6(a) and (b). In the points where wind speed is low, turbulence levels are high, as shown in Fig. 5 compared with Fig. 7 and Fig. 8(a) and (b). The lowest values of turbulence intensity (0.05%) occurs in points 1 and 2 for p ¼0.11, which represents the highest mean velocity

(Fig. 5(a) and Fig. 7(a)). Points 7, 8 and 9 are located in the region where the highest turbulence levels occur (from 0.5%). It is noticeable that the wake effect is mostly present in the downwind of the hill (Fig. 5(c) and Fig. 7(c)), and it extends to Points 10, 11, and 12(Fig. 5(d) and Fig. 7(d)). An increase in hub height could mean a significant reduction in these levels with the consequent improvement of wind velocity according to point 08. (Fig. 6 (a) and (b)). The results obtained by the anemometric tower do not provide sufficient technical information to follow the requirements of the technical and economic feasibility of the wind project. Such towers are not able to identify precisely the extension and limits of the turbulence intensity of the wake areas. In this case if they are the only wind energy assessment tool employed in the micrositing, unsuitable sites for wind energy generation may be included; Based on this, it can be assumed that the businessperson adopting only he anemometric tower methodology to construct the economic feasibility of the plant, will probability have problems, since in this case, the actual generated energy in the implementation of the power plant will be less than expected, once it depends on the topography and roughness surrounding each tower. This research proves how important the addition of experimental studies in wind tunnel for technical evaluation of micrositing projects is, especially for a detailed knowledge of the stratified wind profile and the turbulence intensity profile. This methodology is important to calculate the actual energy that will be generated in the plant, according to the auction project. The precise definition of all the geophysical variables that affect the construction of the ABL is a matter still under investigation in complex areas This experimental analysis has been established as an important design tool, validating the wind parameters, as well as the setting of the correct spacing between the turbines, so as to make power generation more accurate and reliable in complex micrositings.

Acknowledgment The authors gratefully thank the assistance of Fundação de Amparo Pesquisa – FAPERGS of Rio Grande do Sul, Brazil.

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