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Artificial intelligence, 3D printing and advanced nuclear reactors

Mark Messner, chief mechanical engineer at Argonne National Laboratory in the United States, is one of the professionals who can predict how materials behave at high temperatures and pressures. Current forecasting methods work well, but are time consuming and often require supercomputers, especially if you already have a set of specific material properties — such as stiffness, density, or strength — and want to find out what type of structure a material would need to meet these properties.

“Usually, a lot of physics-based simulations need to be run to solve such a problem,” Messner said. Therefore, he sought a shorter path and realized that neural networks — a type of artificial intelligence (AI) that discovers patterns in vast datasets — could predict exactly what would happen to a substance under extreme conditions. And they can do this much faster and easier than traditional simulations.

Messner’s new method found the properties of a material more than 2,000 times faster than the traditional approach – read the Journal of Mechanical Design 2019 October article. Messner recognized that much of the computation could be done on a standard GPU laptop instead of supercomputers, which are often inaccessible to most businesses.

This was the first time someone had called a convolutional neural network. used to accurately identify the structural properties of a material. (The simpler type of convolution differs from any other neural network, but is ideal for recognizing patterns in photographs, for example.) This is one of the researchers’ first steps in accelerating the design and characterization of materials in the transition to a completely clean energy economy.

The role of cats on the Internet

Messner began his scientific career at the Lawrence Livermore National Laboratory, where a team was working with a 3D printer to make a micron, or a millionth of a meter structures. Although the research was at the forefront, it was slow. Therefore, the question arose as to whether artificial intelligence leads to results faster?

At that time, the technology giants of Silicon Valley had already begun to use convolutional neural networks to recognize faces and animals in images. This inspired Messner.

“My idea was that the structure of a material is no different from a 3D image. By implication, the 3D version of this neural network will do a good job of recognizing the properties of the structure. Just as a neural network learns that an image is a cat or something else, “the researcher said.

To test his theory, Messner took four steps:

  1. Designed and defined a square of bricks (quasi-pixels);
  2. Random samples were taken from this design, and created 2 million data points with a physics-based simulation. These points linked the design to the properties of the desired density and stiffness;
  3. It fed 2 million data points into the convolutional neural network. This taught the network to look for the right results;
  4. The genetic algorithm, another type of artificial intelligence designed to optimize results, along with the learned convolutional neural network used to find the overall structure corresponding to the desired properties.

The result was immediate. The new AI method found the appropriate structure 2760 times faster than the traditional physics-based model (0.00075 seconds versus 0.207 seconds).


  • New tools to enhance nuclear innovation

    This abstract engineers can design materials, especially those that must withstand high temperatures, high pressures, and corrosion conditions.

    Messner recently joined a group of engineers from Argonne and the Idaho and Los Alamos National Laboratories. team working with nuclear startup Kairos Power. The team will create artificial intelligence-based simulation tools to help Kairos design a nuclear reactor that, unlike current solutions, would use molten salt as the refrigerant. With these tools, the team will predict how a certain type of stainless steel, called 316H, will behave under extreme conditions for decades.

    “This is a small but vital part of our work for Kairos Power. “Kairos Power wants very accurate models of how the reactor components will behave in the reactor to support its license application to the nuclear regulator. We look forward to making these models available,” said nuclear engineer Rui Hu, who works for Argonne. directs the project.

    Another promising way for this type of work is 3D printing. Before 3D printing became widespread, it was difficult for engineers to actually build structures like the one Messner found using artificial intelligence in his 2019 study. Yet making a structure layer by layer with a 3D printer allows more flexibility than traditional manufacturing methods.

    “The future of engineering lies in the combination of 3D printing and new AI-based techniques. We would give a neural network structure for 3D printing and the equipment would print out with the features you want. We’re not there yet, but that’s hope, “said Messner.

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