A few ideas for improving blade designs

Transitioning from manual to automated manufacturing is essential to reducing labor, tooling, and material costs and ultimately maximizing profits. Labor costs are obviously higher in well-developed economies. Hence, less established countries with lower expenses offer an alternative for cost cutting. However, access to proper skill sets is especially important in the area of composites, which use complex technology and require specialized training and knowledge. So product quality may suffer due to high turnover, lack of sufficiently sophisticated skills, and insufficient training for a low-cost workforce.

A master model is critical

Composite engineering software, such as VISTAGY’s FiberSIM, provides a detailed and accurate digital master model indispensable to automated manufacturing. The FiberSIM suite of software supports the unique and complex design and manufacturing methods necessary to engineer innovative, durable, and lightweight composite wind turbine blades.

A prerequisite to an efficient automated manufacturing process is a product-development system that creates a complete and detailed digital master definition of a composite product within a commercial 3D CAD system, such as Catia, Pro/Engineer, or NX. It is vital that the digital composite model of a blade contain all the information required to properly manufacture the part—including definition of all laminates and plies, associated flat patterns, manufacturing sequences and steps, accurate definitions of the cored panels and interface definitions for all mating parts. This enables seamless collaboration between engineering and manufacturing.

Such a master model must also enable so-called producibility simulations, or simulations of the manufacturing process, be it curing of prepreg layups, dry layups for resin infusion, or some other manufacturing method. Producibility simulations let design and manufacturing engineers predict manufacturing issues, such as composite fabric wrinkling or bridging that may appear during layup operations due to deformations imparted to materials when laid up in blade molds. By accurately predicting such issues, simulation software allows an early resolution of manufacturing issues without making many costly prototypes that lengthen development operations.

Data from a master model drives all downstream manufacturing operations. After creating the engineering master model and releasing it to manufacturing, all data sets necessary for production can be readily exported to the shop floor for manufacturing the parts. For example, all ply shapes will be exported to a nesting or cutting system for automated cutting. This can save a significant amount of time compared to manual cutting, and provide better repeatability and quality of the ply shape.

The simulation shows how fibers deviate from a specified orientation as a ply of composite material is draped over a tool for making NASA’s composite crew module. Areas highlighted in white indicate fibers with orientations that fall in an acceptable range from spec, while other colors indicate fibers that deviate slightly (yellow) or significantly (red). The software lets users understand the behavior of continuous fiber reinforced composite materials as they conform to complex curvatures, ensuring that it meets stiffness and strength requirements and validating that the manufactured part matches design intent.

Manufacturing flexibility

In the not-so-distant future, automated systems based on the robotic deposition of entire rolls of materials will be used to manufacture composite blades. Such systems will be fed data obtained from the master model to generate layup trajectories, spray the gelcoat, add adhesive beads, and finish the blades.

These systems will also need flexibility to handle hard-to-manufacture part areas, which will only work if the digital master model has been completed and prepared. Where a human can interpret and correct on the fly on the shop floor, a machine has to be fed accurate and appropriate information. WPE

Siting the urban turbine

Guilliam Dupont/Application Engineer, Meteodyn/Philadelphia/meteodyn.com

UrbaWind has modeled the wind flow and direction through a campus for an unspecified elevation. Yellow and red indicate areas of highest wind speeds and hence locations worth further investigation.

Compared to rural open spaces, the geometry of urban areas is more complex and unpredictable. The wind flows generated by buildings, such as Venturi-effects created by air flow between two buildings, or edges effects, make modeling urban flows more difficult. This made it necessary to develop a non-structured solver with an adaptive mesher. The solver in UrbaWind, MIGAL-UNS, has been frequently used for some years now and is fully validated on several academic cases. It is a fast, coupled multi-grid solver which allows a complete resolution of 3D equations for fluid mechanics. Moreover, an automatic mesh generator lets the software deal with the complex situations without long and tedious adjustments, generally a common issue when using CFD codes. The software generates refinements at key areas of the domain as well as boundary conditions.

All wind characteristics such as speed, turbulence, shear, or vertical wind, can be computed at the height needed. This is critical information for calculating fatigue problems and extreme loads on a turbine.

The input data includes CAD models of buildings and vegetation. It must be pointed out that on-site wind-speed data is not required. The use of meteorological data based on, for example, information from the nearest meteorological station and corrected from effects of the local topography, can be used to obtain a statistical description by Weibull fitting of wind production and wind roses at needed points. this makes it possible to get an accurate idea of wind speed at a proposed site for a small wind system in urban or built up areas.

A closer look at ground level shows wind flow through a plane aligned with three proposed vertical-axis wind turbines.

Results are visualized with vectors for direction and speed. A colored wind-energy map

Towers and building roofs are often considered good locations for turbines. Flow images such as this one from UrbaWind shows how much higher a turbine might be mounted to take advantage of wind there.

displays the most productive locations. Moreover, the software lets users consider roughness effects (asphalt, grass, or water) and wake effects, making the software useful for finding a best location for wind turbines in an urban area.

The software also assists in selecting a best turbine for a location. It does so by calculating energy production from the distribution of wind speeds at the proposed site when users provide a power curve often available from a turbine manufacturer. Users then have to check turbulence intensities and wind pressure on the turbine to choose the most suitable and profitable machine.

The software has been validated in several relevant cases, such as for groups of buildings with complicated shapes in urban areas. Values calculated by the software were compared with measurements in wind tunnels and by anemometers in cities. A team based in Philadelphia is available to make and assist with urban wind-resource assessments. WPE