Each constraint, or rule, picked by the researchers reflects decades of expert knowledge rooted in the laws of physics. Here, the researchers trained their agent to position the fuel rods under a set of constraints, earning more points with each favorable move. Deep reinforcement learning combines deep neural networks, which excel at picking out patterns in reams of data, with reinforcement learning, which ties learning to a reward signal like winning a game, as in Go, or reaching a high score, as in Super Mario Bros. The researchers wondered if deep reinforcement learning, an AI technique that has achieved superhuman mastery at games like chess and Go, could make the screening process go faster. Engineers have tried using traditional algorithms to improve on human-devised layouts, but in a standard 100-rod assembly there might be an astronomical number of options to evaluate. In an ideal layout, these competing impulses balance out to drive efficient reactions. In a typical reactor, fuel rods are lined up on a grid, or assembly, by their levels of uranium and gadolinium oxide within, like chess pieces on a board, with radioactive uranium driving reactions, and rare-earth gadolinium slowing them down. “By improving the economics of nuclear energy, which supplies 20 percent of the electricity generated in the U.S., we can help limit the growth of global carbon emissions and attract the best young talents to this important clean-energy sector.” “This technology can be applied to any nuclear reactor in the world,” says the study’s senior author, Koroush Shirvan, an assistant professor in MIT’s Department of Nuclear Science and Engineering. Their results were published in December 2020 in the journal Nuclear Engineering and Design. The AI system can also find optimal solutions faster than a human, and quickly modify designs in a safe, simulated environment. Researchers at MIT and Exelon show that by turning the design process into a game, an AI system can be trained to generate dozens of optimal configurations that can make each rod last about 5 percent longer, saving a typical power plant an estimated $3 million a year, the researchers report. Now, artificial intelligence is poised to give them a boost.
Reactor idle optimal layout trial#
Through decades of trial and error, nuclear engineers have learned to design better layouts to extend the life of pricey fuel rods. If the fuel rods that drive reactions there are ideally placed, they burn less fuel and require less maintenance. One of the key places to cut costs is deep in the reactor core, where energy is produced. nuclear fleet is aging, and operators are under pressure to streamline their operations to compete with coal- and gas-fired plants. Nuclear energy provides more carbon-free electricity in the United States than solar and wind combined, making it a key player in the fight against climate change. Researchers show that deep reinforcement learning can be used to design more efficient nuclear reactors. Colors correspond to varying amounts of uranium and gadolinium oxide in each rod.
Reactor idle optimal layout full#
MIT researchers ran the equivalent of 36,000 simulations to find the optimal configurations, which could extend the life of the rods in an assembly by about 5 percent, generating $3 million in savings per year if scaled to the full reactor. In this AI-designed layout for a boiling water reactor, fuel rods are ideally positioned around two fixed water rods to burn more efficiently.
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