This week we discuss Vestas' system to determine the quality of wind turbine blades before recycling and Siemens Gamesa's noise reduction idea. Then Crosswind's blade pitching system to increase wake mixing and a seemingly common to patch a hole in the wall. Visit https://www.intelstor.com/ to inquire about their IP Prism services! Sign up now for Uptime Tech News, our weekly email update on all things wind technology. This episode is sponsored by Weather Guard Lightning Tech. Learn more about Weather Guard's StrikeTape Wind Turbine LPS retrofit. Follow the show on Facebook, YouTube, Twitter, Linkedin and visit Weather Guard on the web. And subscribe to Rosemary Barnes' YouTube channel here. Have a question we can answer on the show? Email us! Pardalote Consulting - https://www.pardaloteconsulting.comWeather Guard Lightning Tech - www.weatherguardwind.comIntelstor - https://www.intelstor.com Allen Hall: Welcome to Power-Up, the Uptime podcast focused on the new hot off the press technology that can change the world. Follow along with me, Alan Hall and IntelStor's, Phil Totaro, as we discuss the weird, the wild, and the game changing ideas that will charge your energy future. All right, Phil, this week, a number of really interesting ideas. This first one comes from Vestas and it is about recycling a wind turbine blades. And it's a, it's sort of a different approach. The quality of the material that they can recycle out of a wind turbine blade is obviously based upon how that blade has been treated or how, what its life looked like ahead of time. So they're The patent idea is to use machine learning to determine the quality of the recycled material up front, so they can process the blades more efficiently. That's an interesting approach. Come on. Vestas, Philip Totaro: obviously, very creative company. And to be able to characterize the, the lifespan of the material prior to trying to take it into the recycling phase. Because the quality of the material that you're recycling may end up impacting the, post recycling usage. So for instance, if you're trying to put it into concrete, you may need a certain grade of, fiber. That, that is something that could, as, as the industry continues to kind of grow with this recycling initiatives. This could come into play in the future, again, I don't know that you necessarily need machine learning to facilitate all this, I think that's a bit of a buzzword y, aspect of the invention, but Joel Saxum: In the grand scheme of things, the way I'm looking at this problem is this, recycling a blade engineer, or blades, hot topic. AI machine learning, hot topic, great way for Vestas to throw these together and boost this thing out for an ESG stamp that says, we're working on this and we're using AI to blah, blah, blah.