US uses AI to boost efficiency, accuracy of nuclear technology licensing applications
EL.KZ Информационно-познавательный портал
The United States has used AI to streamline the nuclear regulatory process. The Department of Energy used AI mapping to convert a safety analysis document required under DOE’s authorization pathway for advanced reactor demonstrations into U.S. Nuclear Regulatory Commission (NRC) licensing documents for commercial deployment, El.kz cites Interesting Engineering.
The DOE revealed that this accomplishment shows the role AI can play in improving the efficiency and accuracy of nuclear technology licensing, and could one day help to accelerate timelines for the commercial deployment of advanced nuclear reactors.
AI-accelerated nuclear energy deployment
“Now is the time to move boldly on AI-accelerated nuclear energy deployment,” said Rian Bahran, Deputy Assistant Secretary for Nuclear Reactors.
“This partnership, combined with the President’s orders, represents more than incremental ‘uplift’ improvements. It has the potential to transform how industry prepares its regulatory submissions and deploys nuclear energy while upholding the highest standards of safety and compliance.”
Everstar’s Gordian AI solution, built on the Microsoft Azure platform, was recently used to convert the Preliminary Documented Safety Analysis for DOE’s National Reactor Innovation Center’s (NRIC) Generic High Temperature Gas Reactor (HTGR) into sections equivalent to an NRC license application, according to a press release.
Nuclear-grade technical work
The DOE revealed that the final 208-page document took one day to generate. Typically, the process takes a team of people between four and six weeks to complete the same task. The AI tool also comprehensively identified missing or incomplete information needed to successfully complete an NRC application.
Gordian was engineered for nuclear-grade technical work and is equipped with physics and engineering tools, as well as the ability to understand and integrate data through semantic ontology mapping, to ensure that the final output is computed and verified, not inferred, according to the DOE.
“Nuclear is poised to solve today’s critical energy challenges,” said Kevin Kong, CEO and Founder of Everstar. “We’re excited to partner with INL to meet the moment, working together to accelerate regulatory review and commercialization.”
The DOE also revealed that Gordian’s output was subsequently evaluated by an expert for accuracy, missing information, consistency, as well as grammar and structure to ensure that its results were correct and adhered to rigorous professional standards. The output was found to demonstrate quality, rigor, and depth, as well as the tool’s ability to identify and qualify its own gaps in data knowledge.
“Our collaborations with DOE, INL and across the industry are demonstrating how we can effectively bring secure, scalable AI technologies to solve key energy challenges and achieve the broader national and economic security goals envisioned by the Department’s Genesis Mission,” said Carmen Krueger, Corporate Vice President, US Federal, Microsoft.
The DOE also highlighted that currently, the nuclear licensing process involves multiple rounds of manual document reviews and minor clerical adjustments, which can take years to complete.
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