Campbell Scientific is a brand of datalogger that is widely used throughout the wind industry. Campbell recently integrated Lufft sensors into their Loggernet software and will sell Lufft sensors to customers upon request. Lufft USA offers Lufft sensors with a Campbell Scientific datalogger as a new turnkey wind and weather assessment package. Lufft sensors are available with optional heating depending on the power requirements at a site.

Intelligent wind sensing technology by Lufft USA is a reliable maintenance free way to measure the wind and weather. The WS series of compact intelligent weather sensors (WS200-WS600) along with Ventus and V200A models, are designed with different parameters of measurement to meet specific weather monitoring needs. The top-of-the-range model, WS600-UMB, measures temperature, humidity, precipitation, air pressure, wind direction and wind speed.

The DWS Series Anemometer measures wind velocities from 3.3 to 67 mph.

recently launched a line of dynamic wind sensors that can improve efficiency, enhance accuracy and provide safety in a variety of different applications.

Their DWS Series Anemometers measure wind velocity from 3.3 to 67 MPH (1.5 to 30 meters per second).  They are can be used for accurate monitoring with weather stations, and for providing added safety with outdoor hoists/cranes,  greenhouses and to inhibit large industrial doors from operating in high winds. The DWS Series feature selectable NPN and PNP transistor outputs, as well as a proportional output.

They also includes Wind Vanes for wind direction sensing, allowing the turbines to be rotated to the proper direction for maximum efficiency.

The DWS Series Anemometer housings are ruggedly designed with an operating temperature range of –4 to +140°F (-20 to +60°C). A special shielded cable is also included, thus making the sensors suitable for use on turbines, which typically generate quite a bit of electrical noise. Models are also offered that have built-in heating elements to prevent icing.

List prices begin at $785 and are available from Carlo Gavazzi’s North American network of sales offices, authorized stocking distributors or www.GavazziOnline.com.

Circulation control techniques blows compressed air from slots on the trailing edges of wings or hollow blades to change their aerodynamic properties. In aircraft, circulation-control wings improve lift, letting aircraft fly at much lower speeds, as well as take off and land in shorter distances. In helicopter rotor blades, the technique, also called “circulation control rotor”, simplifies the rotor and its controls, and produces more lift.

The airfoil is of a NASA/GTRI 2D CFD model for validation, not necessarily a turbine rotor cross section. Slots could be at the leading or trailing edges, depending on a required effect. A few slot dimensions are shown. Source: NASA.

Research aimed at adapting circulation control technology to wind turbine blades will be conducted by a California-based PAX Streamline, in collaboration with the Georgia Institute of Technology. The two-year project will lead to construction of a demonstration pneumatic wind turbine with support from a $3 million grant from the Advanced Research Projects Agency-Energy (ARPA-E) the federal energy research and development organization.

“Our goal will be to make generation of electricity from wind turbines less expensive by eliminating the need for the complex blade shapes and mechanical control systems used in existing turbines,” said Robert J. Englar, principal research engineer at the Georgia Tech Research Institute (GTRI).

“Because these new blades would operate effectively at lower wind speeds, we could potentially open new geographic areas to wind turbine use. Together, these advances could significantly expand the generation of electricity from wind power in the United States.”

The ARPA-E project will apply the technique to controlling the aerodynamic properties of wind turbine blades, which now must be made in complicated shapes and controlled by complex control mechanisms to extract optimal power from the wind.

“The wind speed at which these turbines would begin operating will be much lower than with existing blades,” said Englar. “Location that wind maps have previously indicated would not be suitable locations for wind turbines may now be useful. The blown technology should also allow safe operation at higher wind speeds and in wind gusts that would cause existing turbines to be shut down to prevent damage.

“Because they would produce more aerodynamic force, torque, and power than comparable blades, these blown structures being developed by Georgia Tech and PAX could also allow a reduction in the size of the wind turbines.

“If you need a specific amount of wind force and torque to generate electricity, we could get that force and torque from a smaller blade area because we’d have more powerful lifting surfaces,” says Englar.

A major question awaiting study is how much energy will be required to produce the compressed air the blown blades need to operate. Preliminary studies done by Professor Lakshmi Sankar in Georgia Tech’s School of Aerospace Engineering suggest that wind turbines with the blown blades could produce 30 to 40% more power than conventional turbines at the same wind speed, even when the energy required to produce the compressed air is subtracted from the total energy production.

The new turbine blades will be developed at GTRI’s low-speed wind tunnel research facility located in Cobb County, north of Atlanta.

Officials of PAX Streamline see the circulation control technology as key to development of a new generation of turbines that could significantly lower the cost of producing electricity from the wind. “This is a significant validation of our established turbine R&D,” said PAX CEO John Webley. “With this grant, we can rapidly accelerate our research program and, within the next two years, deploy a prototype wind turbine which demonstrates our game-changing technology.”

AcuSolve, a general-purpose finite-element-based flow solver, has evolved through years of development, experience, and feedback. The software’s advantages are its reliability, speed, and accuracy. Users can rapidly obtain quality solutions without iterating on solution procedures or worrying about mesh quality or topology. AcuSolve has developed many advanced, high fidelity CFD modeling techniques and capabilities to help predict and analyze wind turbine aerodynamics, loads, wake propagation, aeroacoustics, aeroelasticity and fluid/structure interaction (FSI). It can be used by designers and engineers of all level of expertise. In the accompanying picture, the software plots blade-tip deflection along with turbulence and a measure of pressure variations.

Acusim Software
Acusim.com

Such complex analyses are necessary for complex machines. Wind turbines, for instance, typically include:

• A bladed rotor for converting wind energy into rotational shaft energy;
• A nacelle housing a drive train, which usually consists of a gearbox to increase the rotational shaft speed, a electrical generator that produces a medium voltage, and a transformer that later increases the voltage of the electric power to reach its distribution voltage
• A tower to support the rotor and drive train, and
• Electronic equipment such as controls, electrical cables, ground support and interconnection equipment.

CFD simulation provides valuable insight into all aspects of wind-turbine development, from optimizing advanced blade designs to simulating and comparing the behavior of competing wind-turbine configurations. Engineers can evaluate various tip devices, such as spoilers, deployable gurney flaps, and other control devices to assess the impact of different hub and tower heights, and test and explore alternative scenarios and “what if” questions related to optimizing wind turbine designs.

This is especially important because many innovative designs being considered cannot be reliably modeled using conventional tools. For example, the classical Blade Element Momentum (BEM) method has been the prevailing approach for modeling wind turbines. While it is sufficient for modeling many applications, it is not able to adequately account for the impact of large 3D effects on flow, nor the impact of new blade geometries. Recent experimental work shows that CFD modeling can effectively simulate the behavior of novel blade geometries, with better results than from the BEM approach.

Reading the CFD Output The CFD simulation of NREL’s unsteady aerodynamic experiment in a downwind two-blade rotor configuration shows isosurfaces of vorticity magnitude colored by the local air velocity. The simulation was performed by DR. Christopher P Stone of Computational Science & Engineering LLC and Georgia Tech Prof. Marilyn Smith. The physics were simulated using a NASA CFD solver, Overflow2, and resulting data post-processed with FieldView software from Intelligent Light. Isosurfaces highlight vortex wake structures generated by the wind turbine’s rotor blades, the tower and nacelle, and how they interact with one another. The interaction between the wake structures and the rotor blades affects the noise generated by the turbine and also affects the overall costs of the machine. The colored surface on the ground and near the rotor’s disc center indicate general turbulence that drifts down-wind. Also, the tower generates vortices that shed, adding to the rotor’s vibration. White outlines on the blue ground indicate pressure variations.

In the typical CFD workflow, the post-processing phase brings the simulation data to life. By breaking large data into smaller, more specific and manageable pieces, post-processing tools such as FieldView lets researchers easily and efficiently interrogate simulation results, identify the most relevant features of a design, and in so doing, create an iterative design optimization process in which the results of one simulation are incorporated into subsequent simulations. This process can be automated using tools such as FieldView FVX.

Post-processing also produces 3D color graphics, plots, and animations that display the simulation results in a meaningful and easy-to-understand format for presentation to various stakeholder groups, many of whom are usually not experts in engineering or the wind-energy domain.

In addition to providing a more rigorous and reliable modeling, CFD-based simulation can result in significant time and cost savings by reducing the need for scale models, wind-tunnel tests, and field testing. Small-scale test data can be effectively investigated and extrapolated to reliably predict system behavior at a larger scale.

Site selection, microsite considerations

Site selection is of paramount importance in wind-energy projects. Its goal is to identify locations with the strongest, most sustained overall wind patterns while avoiding wind shadows and highly turbulent areas. An emerging discipline called micrositing evaluates localized wind patterns and terrain effects, and helps engineers place the wind turbines in the most advantageous location within the selected site.

When evaluating competing sites for wind-based power generation, three factors are particularly important:

• High average wind velocities
• Optimal time distribution of high winds. For instance, does the wind tend to blow more in the afternoon when the grid needs the energy, or does it tend to blow after midnight, when demand for electric power is lower?
• Low turbulence levels.

Site selection is directly influenced not just by prevailing wind patterns such as speed, direction, and regularity, but by factors such as turbulence and altitude, which impact air density. Changes in air density come from temperature differences that occur because of heating by the sun, cooling from rain, or variations in the terrain, such as rocky areas adjacent to areas covered by vegetation. Nocturnal jets – streams of high-speed, turbulent flow that descend from the upper atmosphere in some clear-sky conditions – must also be considered because they generates large structural loads on a turbine.

The amount of energy that wind contains is a function of the cube of its speed. That is, when the wind speed doubles, the amount of energy it contains increases by a factor of eight. As a result, potential geographic locations are given a wind rating based, in part, on the average prevailing wind speeds at the site. In general, locations with a designation of Class 4, 5, 6, or 7 are considered commercially viable sites. Unfortunately, they are not that common.

Far more prevalent are the Class 3 sites, which are characterized by lower average wind speeds. To operate wind turbines at Class 3 sites, the engineering community is actively working to develop and commercialize various passive and active engineering advances in blade design and materials to maximize energy yield, reduce the cost per kilowatt-hour, and minimize wear-and-tear on the wind turbine blades, drivetrain, and other components.

Improving site-assessment techniques has become another goal. The complexity of the airflow at any potential location requires thorough quantitative and qualitative analysis, and the size of the data places special demands on the engineer and tools used to interpret and manage the data. Using computational fluid dynamics (CFD) to model and simulate the environmental conditions associated with a given terrain lets engineers identify, characterize, and predict wind patterns, atmospheric turbulence, nocturnal jets, and other relevant factors quickly and effectively. Micrositing is also significantly enhanced by CFD modeling.

A typical CFD workflow begins with mesh generation and model development, and after specifying some of the prevailing flow conditions, such as wind speed and direction, a CFD solver runs to simulate and predict the density, velocity, and pressure of the airflow. The resulting large, unsteady datasets are then post-processed. Post-processing software such as FieldView from Intelligent Light, Rutherford, N.J. breaks the simulation data into smaller, specific, and more manageable bits, helping investigators more effectively interrogate the data to identify key flow features, characteristics, and visualize critical aspects of complex simulations. This improves overall data management and processing requirements, reduces the computational power needed, and increases the user’s speed and agility in analyzing and visualizing CFD results. Repetitive tasks can be automated with tools such as FieldView FVX, again speeding analysis and capturing a company’s knowledge and preferred calculations.

Wind Classifications at 10 M

Finally, post-processing produces high-resolution, 3D color graphics, plots, and animations that illuminate site aspects such as velocity vectors, pressure contours, and regions of constant flow-field properties. An ability to analyze and display the modeled results in a meaningful and easy-to-understand format is particularly important because these complex problems and modeled solutions must be shared with other stakeholder groups who are usually not experts in engineering or the wind-energy domain.

Today, CFD is being applied to a wide variety of issues in wind engineering. One major European wind turbine manufacturer, for instance, has successfully used STAR-CCM+ [CD-Adapco] and FieldView to develop design and siting tools. At the Sustainable Energy Solutions Group at Northern Arizona University in Flagstaff, researchers are working with the Navajo Tribal Utility Authority to study a wind site located in western Arizona. The location, Aubrey Cliffs, is a typical wind site in the southwestern U.S. It has numerous high elevations and ridge lines along the sides of mesa tops. These sites are thermally driven, with temperatures in nearby valleys (such as Phoenix) reaching more than 110F (43C). Researchers have been collecting wind data and predicting wind patterns in the area using flow solver codes such as Overflow from NASA and AcuSolve from Acusim Software, Mountain View, Calif. Post-processing with FieldView brings data sets from the solvers together and allows meaningful exploration of measured and simulated data.

Discuss ideas and comments at www.EngineeringExchange.com

::Windpower Engineering::

Distinguishing and recognizing drivetrain faults result in quicker field diagnostics and repair. The complete drivetrain consists of a two-stage planetary gearbox, a two-stage parallel-shaft gearbox with rolling or sleeve bearings, a bearing loader, a programmable magnetic brake, and a variable yaw-and-pitch scaled wind turbine. All elements of the WTDS maximize the number of drivetrain configurations to research gearbox dynamics and acoustic behavior by health monitoring, vibration-based diagnostic techniques, lubricant conditioning, or wear-particle analysis. It is rugged enough to handle heady loads and spacious enough for easy gear placement, setup, and installation of monitoring devices.

SpectraQuest Inc
www.spectraquest.com

Other software of interest to engineers in the wind industry includes Structural Solutions for modeling loads that are static, dynamic, linear, nonlinear, impact, and vibration. Loads can produce deflection, buckling, and contact. Software duplicates thermal conditions, rotor dynamics, fatigue, composite behavior, and mechanisms/rigid body/flexible body dynamics.

Wave Energy Devices simulate mooring systems; wave energy-mechanical energy coupling and random wave and survival wave conditions. Fluid Simulation Tools include thermal and fluid-flow analysis; single and multiphase flows with chemical reactions, multi-fluid interactions, and solid–fluid interactions. Fluid Structure Interactions allow analyzing changes in fluid behavior affecting a structure and vice-versa.

ANSYS Inc.
www.ansys.com

Trouble with turbulence
As companies build larger wind turbines, they place an increased emphasis on decreased spacing distances. This gives rise to a twofold problem: Shorter distances can reduce the production from downstream turbines, and result in a significant decrease to service life due to the increased load levels created by turbulence. Field tests and simulations show that operating in a turbulent wake field can increase equipment stress levels by 5 to 15%.
Typical turbine spacing is 6 to 8 times the rotor diameter.

Yellow turbine velocity: The velocity simulation is on a plane through the hub and nacelle. About 9 m/s wind slows a bit at the nose and behind the nacelle and speeds up at the sides.

The wind-resource use factor is independent of rotor diameter and ranges from 2.5 to 9 MW/km2. But a recent study shows that turbulence can be twice normal levels at 10 rotor diameters downstream, along with a 25% velocity deficit. In addition, some early indications are that the largest turbines may be particularly vulnerable to “horizontal wind shear”, which is created by the meandering wakes of upstream turbines.

Such conditions are of increasing concern to offshore developers and OEMs. To vividly illustrate the problem, Ed Salter, CTO and Co-founder of Greenward Technologies, Austin, Texas (greenward-technologies.com), points to an aerial photo of the Horns Rev  wind farm, the world’s largest offshore development. The photo shows clouds forming in low-pressure turbine wakes. The image reveals a lot. First of all, the rotating wake, referred to as “swirl”, does not dissipate quickly and extends back many rotors diameters. More importantly, the photo shows the swirl building as it passes through the rotor of each turbine.
“There are no terrain features offshore to break up the wakes, so they persist for long distances,” says Salter. “Also, they are reinforced in an additive manner at each row. Turbines operating downstream of other turbines in highly turbulent flow can experience greatly accelerated fatigue damage to all components in the primary load path.”

Could there be a way to eliminate or reduce the effects of rotor-induced wake swirl? Salter and Greenward CEO Larry Haworth think they have an answer in what they call a Quad Array. It consists of four counter-rotating turbines that feature 3-blade flexible, lightweight rotors that were first developed by Salter at Wind Power Systems Inc. in 1977. The four turbines are mounted on a streamlined “X” frame that rotates to service each turbine. The Quad Array frame also uses what Salter calls “flexible lightweight rotor technology” that almost eliminate “tower shadow” noise and vibration.

Quad Array: places four turbines on crossed arms letting their blades counter rotate to minimize turbulence.

The wake dynamics of the Quad Array are of particular interest, and a simplified wake analysis was done in February of 2008. This was followed by the design and construction of a functional wind tunnel scale model that Salter subjected to a series of preliminary controlled velocity tests.
The results led him to formulate what he calls the Wake Convergence and Swirl Cancellation hypothesis. The concept is simple enough – get the counter rotating wakes to converge and the opposing swirls will cancel each other. The implications are not so simple. “If we can prove this, it will change the industry,” says Salter. “We could be looking at something like a 10-fold improvement in the wind resource use factor, combined with a large reduction in turbulence levels.”
To back up his comments, Greenward has launched a collaborative program to analyze the wake of the Quad Array using the latest CFD codes, along with a comprehensive wind tunnel testing program. The company is looking for qualified collaborators, and plan on presenting their first paper at Windpower 2010.

Wakes from Discs: a detailed site analysis shows a measure of turbulence behind turbine rotors

Learning from larger machines
As OEMs design larger turbines, they are finding variations to the wind in just the area swept by the rotor. “One question is: What is the real loading on the blades?” asks Dennis Nagy, vice president of business development with CAE software developer CD-adapco, Melville, NY (cd-adapco.com). “It’s the fluctuation that matters. The amount of turbulence at the top of a blade versus at the bottom of the rotation leads to vibration and that leads to fatigue. Blades are longer and flex more on larger turbines so their designers also worry about them bending to the point where they could hit the tower. If that happens, the turbine would be shut down to prevent damage. In addition, blades that pass near the tower momentarily pass through slower moving air for a change in loading. That once-per-revolution condition adds to the vibration. So a good vibration analysis ultimately leads to a fatigue-of-composites study and that needs good CFD and wind data.
“If you do a good job on wind-farm analysis and know where the wakes will form and dissipate due to terrain variations, that would be better information to guide turbine placement than to, say, spacing them equally,” adds Nagy.

Here’s another problem with wind-farm predictions and it’s a big one: Almost all farms end up generating at least 10% less power than the values predicted during the planning and financing stage. Understandably, that disappoints owners.
It turns out that prospects often want OEMs to predict that if they sell this many turbines for a particular placement, the turbines will produce this much power. “The wind data is available from all year round for strength, turbulence, and directions. After evaluating all this stuff, OEMs come up with an estimate for how much the farm can yield, assuming downtime for maintenance. And then they guarantee some level of power output. Apparently, if the farms don’t meet the predicted output, the turbine builder pays penalties,” says Nagy. So OEMs guarantee an output. And should a competitor forecast a higher figure, the second OEM might get the job. So an accurate prediction is essential. To assist, Nagy says his company developed what it calls an actuated disc as a way to more economically model a turbine’s wake effects.

Using the company’s CFD software, wind-farm designers would place a stick to represent a tower and mount a disc on it with thickness and diameter to represent the turbine rotating blade set. “Wind hits the disc’s front side, extracts energy, and modified wind flows out the backside. Each model then predicts swirl and turbulence based on previous detailed studies adjusted or calibrated for wind speed. It’s one way to simplify the analysis,” says Nagy. “Although still compute intensive, we’ve done runs with over a dozen turbines on a landscape to examine wake formations.”
An additional challenge for turbine OEMs, says Nagy, has been to integrate CFD software into the work flow of their field-siting tasks. “For example, a prospect wants to build a wind farm on a particular parcel. One European OEM would then examine the wind and weather data for the topology and suggest placing turbines in precise locations. The field engineer, in the past, would take this data and feed it into proprietary in-house software that would make an assessment of each proposed turbine and decide whether it would be a normal or “complex” situation. If the wind is turbulent enough, or if the terrain is rugged enough to produce local wind effects, such as from a canyon or ridge, the in-house software would require a more detailed CFD study for the turbine. Then the field engineer would send wind and topology files to company headquarters and a group there will do the CFD and generate reports and return them to the field engineer. That would take about three weeks, not because it ran for three weeks, but more likely because it took some human intervention, for example, to transfer files among multiple modules (meshing, boundary conditions, solving), adjust the mesh, and monitor the run. After about three weeks, the field engineer had something to present to the client.

Nagy says his company was able to help significantly streamline the process for the OEM. “Now that complex, three-week task is fully automated and reduced to two hours. It’s literally push button. The user supplies the needed files and the STAR- CCM+ software does the rest.” Nagy acknowledges that significant credit for the short run time goes to the OEM running this process on a HPC cluster of some 1,200 cores.
Another task wind farm operators are looking at is forecasted winds. If a lull is forecasted, the owner might schedule some preventative maintenance. But if good wind is in the offing, he might plan on producing power.

::Windpower Engineering::

Discuss these considerations for designing turbine hydraulics at www.EngineeringExchange.com

A widget calculator includes a windpower conversion that considers air density, wind velocity, and a rotor area to calculate a theoretical maximum power (watts) in a particular breeze. Dry air has a density of about 1.2 kg/m³ at 20C and sea level, and a density of about 1.27 kg/m³ at 0C.

Another calculation on the widget finds the theoretical max power available in a volume of falling water. And a third calculation gives the specific carbon dioxide emissions (CO₂/kWh) from burning a particular fuel when users supply its specific carbon content (kg_c/kg_fuel), the specific energy content (kWh/kg_fuel), specific mass of carbon (kg/mol carbon), and the specific mass for carbon dioxide (kg/mol of CO₂). User can share the widget by clicking the Get This button at the bottom.