Comparative analysis of plasticity-based GND density estimation methods in crystal plasticity finite element models

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Turbulence is rarely that simple. It’s too scattered, too mercurial, too easily triggered by weather patterns that trigger other patterns in an endless cascade. “It’s not just one thing that’s going on,” Bob Sharman, an atmospheric scientist at NCAR, told me. “It’s not just atmospheric convection. It’s not just wind flowing over mountains. It’s everything going on all the time and interacting.” Sharman is one of the country’s preëminent authorities on turbulence prediction. The computer models that he has built can predict where rough air is most likely to arise. “The problem is,” he said, “when we go to meetings with the airline industry and suggest a probabilistic approach, a pilot will stand up and say, ‘No! I want you to tell me if there will be turbulence at this place, at this time.’ ” Sharman threw up his hands. “Nobody knows that. I understand that, in theory, you would want that. But, in practice, that is just not possible.”

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