Game-Changing Research Unlocks New Wind Energy Potential

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cole Polytechnique Fdrale de Lausanne researchers have used a genetic learning algorithm to identify optimal pitch profiles for the blades of vertical-axis wind turbines.

École Polytechnique Fédérale de Lausanne researchers have used a genetic learning algorithm to identify optimal pitch profiles for the blades of vertical-axis wind turbines. Vertical-axis wind turbines with their high energy potential, have until now been vulnerable to strong gusts of wind. The explanatory open access paper has been published Nature Communications.

’s experimental VAWT blade Image Credit © UNFOLD CC BY SA. Le Fouest noted, “Our study represents, to the best of our knowledge, the first experimental application of a genetic learning algorithm to determine the best pitch for a VAWT blade.” Turning an Achilles’ heel into an advantage Le Fouest explained that while Europe’s installed wind energy capacity is growing by 19 gigawatts per year, this figure needs to be closer to 30 GW to meet the UN’s 2050 objectives for carbon emissions.

 

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