By applying the AI that social networks use to identify people in photos, an Argonne engineer discovered a new way to predict the structure of a material, given its preferred properties. The discovery may help save time and money and allow businesses to use techniques once reserved for supercomputers.
The future of clean energy is hot. Temperatures hit 800 Celsius in parts of solar energy plants and advanced nuclear reactors. Finding materials that can stand that type of heat is tough. So experts look to Mark Messner for answers. A principal mechanical engineer at the U.S. Department of Energy’s (DOE) Argonne National Laboratory, Messner is among a group of engineers who are discovering better ways to predict how materials will behave under high temperatures and pressures. The current prediction methods work well, but they take time and often require supercomputers, especially if you already have a set of specific material properties—e.g., stiffness, density or strength—and want to find out what type of structure a material would need to match those properties. “You would typically have to run tons of physics-based simulations to solve that problem,” said Messner. Looking for a shortcut, he found that neural […]
Above: PepsiCo food, snack, and beverage product line-up/Source: PepsiCo PepsiCo turned to tooling with 3D printing...