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Catching A Second Wind: How Supercomputers Are Helping Neighboring Wind Farms Squeeze More Energy Out Of Their Turbines

Stroll behind a spinning wind turbine on a blustery day and the breeze washing over you slows noticeably as the turbine blades pull energy from the moving air. While that’s no surprise, the full extent of a modern turbine’s wind-dampening wake is little short of mind-boggling. According to a new paper in the journal Nature Energy, today’s giant turbines can cause detectable decreases in wind speeds up to 30 miles away, siphoning energy and revenue from neighboring wind farms.

As better, cheaper turbines drive the wind industry forward — generating capacity in the U.S. grew 50 percent in the last five years — wind power has turned into a valuable economic resource for any nation. Far less appreciated, however, is the extent to which wind is also a shared resource. While the world’s wind carries enough power to supply all of humanity’s energy, in any particular region there’s only so much to go around.

How wind turbines affect their neighbors is of particular fascination to Lawrence Cheung, a lead mechanical engineer at GE Research. Cheung has been studying wind turbines for six of his eight years at GE. He specializes in harnessing the computational firepower of modern supercomputers to create elaborate models of how the wind moves in the real world to make the most of its energy. “Normally when the airflow goes around the airfoil, it starts out nice and clean — and then it gets very complicated,” Cheung says.

When Cheung joined the wind turbine research team in 2012, other GE researchers were already using the power of an earlier generation of Cray supercomputers to analyze and improve the function of wind turbines. Back then, they focused on solving a different problem involving improving the individual operation of a turbine, in one case creating a model of hundreds of millions of individual water molecules as they turned into ice on a turbine blade. The models helped to create more “icephobic” blades, increasing their efficiency in frigid climates.

Top and above: Lawrence Cheung, a lead mechanical engineer at GE Research, specializes in harnessing the computational firepower of modern supercomputers to create elaborate models of how the wind moves in the real world to make the most of its energy. Images credit: GE Research.

Six years later, Cheung’s group is taking a different tack. Harnessing the power of the newest Cray XC supercomputer, the new models step back to look at the big picture. Supercomputers such as Cray’s specialize in handling enormous data sets and divvying up the computations between powerful “massively multicore” processors. That’s perfect for Cheung’s latest work, which can model the airflow across an entire wind farm that spans up to 5,000 acres, or more than 3,780 football fields.

The old way of understanding wind behavior at those distances was to set up radars or physical wind-speed detectors that could capture wind speeds after the turbines had already been installed. That produced spotty data and left engineers with no practical way to try out creative ideas to lay out a wind farm and spread turbines over the landscape in the optimal way.

Cheung’s supercomputer models, technically known as computational fluid dynamics simulations, break up huge wind farms into hundreds of millions of individual cubic meters. The simulations still take a week to run, but without the power of the Cray XC that job would take months, Cheung says.

Cheung’s goal isn’t to eliminate the wind-wake problem but rather to understand the precise impact of the slower air after it passes through a wind turbine in different wind farm configurations. That way, the cost of reducing wind wakes can be weighed against the price of building farms with more widely spaced turbines. Those costs include both the price of obtaining extra land (or ocean) and the additional cost that comes with building and maintaining larger farms.

“Normally when the airflow goes around the airfoil, it starts out nice and clean — and then it gets very complicated,” Cheung says. Image credit: GE Research.

Despite those trade-offs, the economic benefits of making turbines into more considerate neighbors are potentially enormous. The recent Nature Energy study gives a sense of the possibilities. By analyzing a trio of neighboring wind farms in Texas, researchers from the University of Colorado Boulder found that the upwind farm cut the speed of the wind at the downwind farm by 5 percent. That meant the farms cost each other $3.7 million in lost revenue between 2011 and 2015, according to the study.

That study, “Costs and Consequences of Wind Turbine Wake Effects Arising from Uncoordinated Wind Energy Development,” doesn’t predict how much the wind industry loses as a whole, but warns that the number could be large, given that nine out of 10 wind farms are within 25 miles of another farm. Different farms will be affected to different extents. In order for the wake effects to be felt over long distances, the wind needs to be blowing in a consistent direction over relatively flat terrain, which happens to describe the geography most conducive to wind farms.

Cheung’s models don’t yet extend beyond 5 kilometers, but he is planning to increase that range as he refines his models and Cray’s supercomputers grow ever more powerful. For now, there is plenty to learn about reducing wind wakes inside individual wind farms, where the impacts are exponentially higher.

 

 

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