Researchers claim more accuracy for complex wind-farm flows

It’s no surprise that changing wind flows significantly affect wind-turbine performance and lead to substantial uncertainties when assessing wind resources. That makes it important to understand the flow field over space and time. Techniques pioneered by a research group in a recently published paper can offer project engineers and developers with an understanding that allows addressing and mitigating the impacts of uncertainties in wind flows.

The wind-flow patterns and turbulence intensity were simulated for 50m above ground for a northerly wind direction after (a) 1,300, (b) 1,700, (c) 2,000, and (d) 2,400 seconds of simulation.

The paper, published in the Journal of Wind Engineering and Industrial Aerodynamics, was written by Claude Abiven, Senior Technical Manager of Natural Power France; Oisin Brady, Director of Natural Power France; and Dr. Jose Palma from the University of Porto. In the paper, High-Frequency Field Measurements and Time Dependant Computational Modeling for Wind Turbine Siting, spectral analyses of the simulations show a model accurately reproducing frequencies recorded at the mast. These frequencies were then linked to a physical phenomenon using a technique (Empirical Orthogonal Functions) not often used in the wind-power community. The method allows characterizing complex flow structures such as zones of recirculation based on a single-mast measurement.

The work was based on high-frequency wind data from a complex site on the west coast of Scotland. Spectral analyses carried out on this dataset and showed the existence of complex-flow phenomena including turbulence, vortex shedding, and direction veer that changed rapidly over short time scales. CFD (Computational Fluid Dynamics) modeling was carried out using the Ventos CFD wind-flow model in time-dependent mode, a feature the software developer says is not readily available in other CFD packages. CFD modeling showed the complex and transient flows the result of complex terrain. The software accurately mapped the flows across the site, away from the measurement location.

To give a sample of detail possible from the software, consider the situation the authors presented. A steady-state solution for northerly winds over the site contained two large vortices downwind of hill B in the accompanying four-panel image. The vorticies are likely to detach and be advected by the flow towards the mast. A frame-by-frame analysis in the accompanying image is of a 7,000 second, time-dependent calculation. It reveals that the north-east (top-right) vortex spreads south before a new and smaller vortex originates east of the site, with a lifetime of 500 to 700 sec. What remains of the original north-east vortex slowly regains strength before the same process starts again. A complete cycle takes about 1,000 sec. Another steady-state formulation of atmospheric calculations suggests converge to one of the many possible solutions delivered by a time-dependent formulation (One is presented in the four-panel image) and therefore provides a largely limited view of the wind flow. The four-panel sequence is a situation of separated flow, a real-life 3D case in which the onset of separation cannot be determined with simple rule-of-thumb guidance, derived from linear models of wind flow over 2D hills.

Natural Power
www.naturalpower.com

Free web help for siting wind farms on complex terrain

Potential users must register before using the software. User-guides tell how to use the app.

International renewable-energy consultancy Natural Power says it has placed a no-cost-to-use, on the Web to estimate wind-flow complexity on potential wind farm sites. As potential development sites with, flat terrain become fewer, more projects are built in or near forests and on complex terrain. These complex-flow sites are often associated with harsh wind conditions likely to affect turbine performance. For example, complex terrain can induce wind turbulence and high loads on turbine blades.

As a result, increasing concerns arise about under-performing wind farms. Maintenance costs are higher than expected, performance is lower, and warranty claims are more common. Tools for designing efficient wind farms in complex terrain are available but there is less awareness of them within the industry. For example, CFD (Computational Fluid Dynamics) modeling, lidar measurements and well-planned measurement campaigns can help optimize wind turbine locations and provide manufacturers and investors the information they need to properly assess the site condition.

Whether to use such advanced tools rather than standard software is not always obvious. Although the use of RIX (Ruggedness Index) values help assess complex terrain, it is not always readily available to investors or developers, and does not take in to account the effect of land cover on flow complexity. In response, Natural Power has developed a complexity assessment applet available gratis on the company’s website. For an estimation of flow complexity, the applet requires turbine locations, which can be uploaded as Google earth place marks, and a simple estimation of forest coverage in the area of interest. The website then computes a complexity index, taking into account topographic features and approximate land cover. Results can be visualised in a report that summarises findings, recommendations, and next steps for development.

‘‘The complex site applet should help minimise the risk of underperforming wind farms by providing owners, investors, and wind specialists with an easily accessible estimation of risk at early project stage and thereby informing about the need for tools and methods appropriate for the site conditions,” says Director of Natural Power France Oisin Brady. “Our team can then suggest the best possible options for site development– from further CFD calculations which we perform with VENTOS, or a prescribed wind measurement campaign with traditional techniques such as met masts or more advanced assessment tools such as our ZephIR 300 lidar.’’

National Power

www.naturalpower.com/ventos-complex-flow-analysis/how-complex-is-your-site/introduction

Rotor blades will deflect this much-says simulation software

To bring costs down, wind-energy firms are recognizing the design benefits of numerical simulation. Transitioning to a simulation-based design process lets OEMs optimize performance and increase a turbine’s power output.

The turbine is an NREL Phase VI UAE design. An unstructured mesh can discretize the model without simplifying its relatively complex geometry.

A valuable tool in this process is Computational Fluid Dynamics (CFD) software. It has traditionally played a role in the design of rotorcraft, fixed-wing aircraft, and even wind-turbine blades. Previous limitations in computing power kept most simulations focused on small portions of a design with a limited inclusion of physical phenomena. Perhaps the most accepted use of CFD in the industry is for analysis of 2D airfoils.

Although an important application, recent advances in computing power and software provide greater capabilities for wind-turbine designers.
For example, multi-physics simulations are an important and emerging capability within computational fluid dynamics. Historically, CFD software focused on predicting fluid and thermal transport. Recent advances, however, now allow coupling or including additional physics into the simulation of the flow field. For instance, one technology with tremendous implication for wind-turbine design is Fluid-Structure Interaction (FSI). As turbine-blade designs become larger and use new materials, blade deflection under wind load becomes increasingly important.

Consider that in high wind, a blade could deflect to the point where it hits the tower. Turbine designers must also consider the change in the blade’s aerodynamic performance, and fatigue concerns can arise as a result of blade deflection. Engineering software providers such as ACUSIM enable engineers to efficiently and accurately simulate the behavior of rotating and deforming wind turbines.

ACUSIM’s finite-element based CFD solver, AcuSolve, contains two techniques for simulating such behavior in wind turbines. One technique uses Practical Fluid-Structure Interaction (P-FSI) technology for linear (small) structural deformations. This relies on a modal superposition approach to compute a structure’s deformed shape. The fluid loading on the blades provides excitation of the various modes of the structure, and the resulting deformation is the sum of each modal contribution. The approach is fast, reliable, and simple. All fluid and structural computations are performed by the CFD solver, so there is no run-time coupling of the solver to external codes. In the second technique, for nonlinear (larger) bending applications, the software supports run-time coupling to external structural dynamics codes using Directly Coupled Fluid-Structure Interaction methods. This lets users couple AcuSolve to a preferred structural solver, and requires no intervening middle ware to accomplish the coupling. The solver software handles the interpolation between dissimilar meshes (between the structural model and fluid model) and communication requirements. In addition, because the communication architecture relies on a standard socket connection between computers, structural and CFD codes can run on different compute resources using different operating systems.

Iso-surfaces of Q criterion are ones that outline flow regions with local rotation, indicating a turbulent eddy. “Q” is a mathematical quantity commonly used to illustrate turbulent vortices in a flow field.

To demonstrate the FSI technology, we have simulated a fully coupled fluid-structure interaction on the NREL Phase VI UAE wind turbine. This design has a 10-m diameter rotor with two twisted and tapered blades. A fully unstructured mesh speeds model construction and solutions. The illustration An unstructured mesh shows the model mesh and complexity of the geometry, including the instrumentation hardware mounted on the front of the rotor. The software’s reliability, speed, and accuracy on unstructured meshes make it unnecessary to perform excessive geometric simplifications to the model.

The constant speed turbine was simulated at 72 rpm with a uniform inflow wind speed of 10 m/s. The Spalart-Allmaras based Delayed Detached Eddy Simulation turbulence model provides high resolution of the turbulent content in the rotor wake. The flexible blades were represented by a total of 20 structural modes using AcuSolve’s P-FSI technology. The blades were “softened” or made less stiff to make their flexing more visible, and to show that the solver can handle large structural displacements. The transient was simulated for six rotations in two-degree time steps. Undeformed and displaced blades is a picture at one time step and reveals the degree to which the blades respond to the wind loading.

Streamwise blade-tip displacement shows the time history as a function of the rotation. It highlights the unsteadiness in the simulation. The time varying displacement is induced by an unsteady turbulent loading on the blades, along with the blade and tower interaction as the rotor turns.
Numerical simulation using CFD will become increasingly important as designers pursue more advanced wind turbines. Combining unstructured meshing with an efficient CFD solver and multiphysics capabilities provides a powerful tool for designing wind turbines. Designers can use the software for standard aerodynamic simulations using rigid structures and the advanced capabilities of the solver for simulating complex phenomena such as active flow control and variable-pitch blades.

Simulating the Turbine-Simulating the Site

Designing the most efficient and effective wind turbine calls for modeling tools that provide accurate, reliable numerical predictions of wind-turbine rotor performance over a machine’s full range of operating conditions. Simulating real-world conditions using computational fluid dynamics (CFD) lets users understand flow phenomena and their effects on the system, better predict the system’s power output, and analyze the types of vibration, fatigue, and other wear-and-tear the wind turbine may experience for the conditions modeled.

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.

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::Windpower Engineering::

CFD’s Role in the Windpower Industry

Recent advances in CFD (computational fluid dynamics) codes and the availability of large scale computer “clusters” are guiding the design work of the next generation of wind turbines. Developers should be able to model a turbine’s power output versus wind velocity, and then the power output for a wind farm with direction data. In addition, detailed information on wake turbulence and velocity deficits in large arrays of wind turbines will avoid the under-prediction of power outputs that plagued previous methods. Those are the goals. The devil, as usually, is in the details

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::

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