AI OPPORTUNITIES EXPLORED | SPECIAL REPORT
Left: Nuclear must embrace AI to remain competitive in the electricity generation market and attract investment, because AI is increasingly being used in competing generation technologies
● Resource extraction and use, where science-based models that include AI multi-modelling approaches can improve predictions of subsurface properties, to help find critical material resources such as uranium, and identify features such as geothermal reservoirs or water sources. This might be realised via a national subsurface AI and data testbed. AI can also accelerate the qualification of material through automation of materials testing, to support development of new energy technologies such as advanced nuclear reactors and new batteries.
To meet the challenge of AI and take advantage of these opportunities, the experts said fundamental developments are needed. Although US national laboratories already have hundreds
of petabytes’ worth of data, so far only small amounts of these data are catalogued, warehoused and ready for AI model ingestion. The national laboratories must establish a computing ecosystem to train and host data and foundation models at increasing scales. Fine-tuned models have to be developed for each domain that are coupled, where possible, with ‘ground-truths’ (ie directly observed data) and first-principles physics. Curation of one-of-a-kind, real-world data coupled with energy industry data will be essential to building models at scale. Most important, partnerships across laboratories, government, industry, and academia are essential to realising the benefits of AI.
Implications for nuclear In addition to these energy industry opportunities, the experts identified unique use cases in nuclear, which was named as one of five areas “vital to the energy future of the US”. (The others are the power grid, carbon management, energy storage, and energy materials.) Their report warns that nuclear is in an AI arms race.
Nuclear must embrace AI to remain competitive in the electricity generation market and attract investment, because AI is increasingly being used in competing generation technologies. But it says nuclear can realise more benefit from AI
because its intricate interdependencies pose challenges “well-suited for AI solutions”, in particular AI’s knowledge capture and capability to discern cross-disciplinary connections. AI can also maintain expertise over time. In its
assessment the report notes that during its lifecycle a nuclear plant will be the focus of different activities that
involve different skill sets, most multidisciplinary, and different people – such as moving from construction to operation. That can be costly: one reason for delays and cost overruns in reactor projects resulted from construction beginning before the design was completed. The report says, “These interdependencies and the challenges that they present are eminently addressable using AI,” because AI “offers an essentially limitless capability to store knowledge and the ability to recognise connections across disciplines where subject matter experts are inherently limited”. The report highlights three specific ‘grand challenges’
where it says AI/ML can outperform human teams in nuclear: streamlining the licensing and regulatory process; accelerating deployment; and facilitating unattended operation.
Licensing The report describes the US regulatory process as “slow, expensive and convoluted”, especially for nuclear. From design and construction to operations and eventual decommissioning, “every phase of a nuclear reactor project undergoes rigorous scrutiny from the regulator”. The process to obtain a construction permit and operating licence for a new reactor is at least five years – sometimes running into decades when pre-application engagement is taken into account – at a cost that can escalate to hundreds of millions of dollars. For example, in the most recent approval of a NuScale US600 the company had to invest more than $500m and two million labour-hours to prepare its licensing application. What is more, the regulatory process, which was designed for light-water reactors, may not be fit for other projects. Emerging AI technology can streamline and expedite the
nuclear regulatory licensing and compliance process and make it much more cost effective for both the licensee and the regulator (for more on this subject see page 20 in the June edition of NEI).
Accelerating Deployment The siting of new energy generation capacity is a complex challenge, balancing technology options, community needs, environmental factors and resiliency considerations. But in one DOE estimate it is found that the US will need 200 GW of new nuclear generation by 2050 to meet national decarbonisation targets. It is likely that a combination of large, small, and microreactors will be used which multiplies the number of sites required.
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