Noise has become an issue with wind energy development – just a few months ago, Fairhaven, Mass., decided to shut down two 1.5-megawatt turbines between the hours of 7 p.m. and 7 a.m. in response to neighborhood noise complaints. Such complaints are often accompanied by claims of ill health effects, although little science can be found to back up those claims.
But whether the new insights GE and researchers at the Sandia National Laboratories are talking about will make a big difference there is hard to say: They seem to be more focused on the fact that production of noise also leads to reduction in power – so trimming what the scientists call “aerodynamic blade noise” is more about making wind turbines more powerful rather than reducing their bother to the neighborhood.
“There’s no question, aerodynamic noise is a key constraint in wind turbine blade design today,” Mark Jonkhof, wind technology platform leader at GE Global Research said in a statement. “By using high-performance computing to advance current engineering models that are used to predict blade noise, we can build quieter rotors with greater blade tip velocity that produce more power. This not only means lower energy costs for consumers, but also a significant reduction in greenhouse gas emissions.”
GE said it believes that a rotor design that trims noise by 1 decibel would boost energy yield over the course of a year by 2 percent.
The GE team credited the use of a supercomputer at Sandia National Laboratories for the new insights into blade technology:
GE’s testing involved Sandia’s Red Mesa supercomputer running a high-fidelity Large Eddy Simulation (LES) code, developed at Stanford University, to predict the detailed fluid dynamic phenomena and resulting wind blade noise. For a period of three months, this LES simulation of the turbulent air flow past a wind blade section was continuously performed on the Red Mesa HPC. The resulting flow-field predictions yielded valuable insights that were used to assess current engineering design models, the assumptions they make that most impact noise predictions, and the accuracy and reliability of model choices.
The researchers still have to put the knew knowledge to work, but they sound confident. “We believe that the results achieved from our simulations would, at the very least, lay the groundwork for improved noise design models,” Jonkhof said.