Ken Petersen
president@ans.org

Nuclear has been on a good roll lately and it is getting better. The 2022 Inflation Reduction Act (IRA) provides a nuclear power production tax credit. This has stopped the early retirement of deregulated units. The IRA also provides a benefit for the clean production of hydrogen. Many utilities have committed to a net-zero goal by 2050. Duke and other utilities have plans to transition coal plants to nuclear with small modular reactors.

And now, nuclear has a new supporter—tech companies.

The big U.S. utility companies (like Exelon, Duke, Dominion, Southern, and Entergy) are all projecting growth in electricity demand—primarily in the commercial sector but some residential growth is also expected. Commercial growth is being driven by new factories (thank you, IRA and CHIPS, that is, the Creating Helpful Incentives to Produce Semiconductors and Science Act). It is also being driven by data centers.

A team of engineers, physicists, and data scientists from Princeton University and the Princeton Plasma Physics Laboratory (PPPL) have used artificial intelligence (AI) to predict—and then avoid—the formation of a specific type of plasma instability in magnetic confinement fusion tokamaks. The researchers built and trained a model using past experimental data from operations at the DIII-D National Fusion Facility in San Diego, Calif., before proving through real-time experiments that their model could forecast so-called tearing mode instabilities up to 300 milliseconds in advance—enough time for an AI controller to adjust operating parameters and avoid a tear in the plasma that could potentially end the fusion reaction.

Researchers at Savannah River National Laboratory (SRNL), in concert with Lawrence Berkeley National Laboratory, Massachusetts Institute of Technology, Pacific Northwest National Laboratory, and Florida International University, are leading the Advanced Long-Term Environmental Monitoring Systems (ALTEMIS) project to move groundwater cleanup from a reactive process to a proactive process, while also reducing the cost of long-term monitoring and accelerating site closure.

The Department of Energy announced $29 million in funding for seven team awards for research in machine learning, artificial intelligence, and data resources for fusion energy sciences on August 31. In all, 19 institutions will build algorithms to address high-priority research opportunities in fusion and plasma sciences using interdisciplinary collaborations of fusion and plasma researchers teamed with data and computational scientists.

Imagine if your employees had a Virtual Assistant that could create condition reports, work requests, or plan work orders simply by asking for it. Or better yet, what if the Virtual Assistant could create work packages and plan your employees’ day automatically without even having to ask? How much more productive would we be if we all had a Virtual Assistant to help perform our work and capture and share our activities as they occurred?

To help plan and prepare for new technologies involving artificial intelligence, the Nuclear Regulatory Commission has released its Artificial Intelligence Strategic Plan (NUREG-2261) for fiscal years 2023–2027.

The NRC said that it expects license applications that include the use of AI technologies to be submitted to the agency for review and approval within the next few years. The strategic plan is meant to help ensure that NRC staff are prepared to review and evaluate such applications.

In the foreword, the NRC Office of Nuclear Regulatory Research director Raymond Furstenau introduces the strategic plan, writing, “We recognize that interest in AI is growing rapidly in both the public and private sectors. As such, I think [it] is important to lay the groundwork needed to ensure the safe and secure use of AI in NRC-regulated activities.”

Scientists are searching for new materials to advance the next generation of nuclear power plants. In a recent study, researchers at the Department of Energy’s Argonne National Laboratory showed how artificial intelligence could help pinpoint the right types of molten salts, a key component for advanced nuclear reactors.

The Nuclear Regulatory Commission has issued a request for comments as it develops a strategic plan for evaluating artificial intelligence in its regulations. Specifically, the NRC is asking for input on the agency’s overall AI strategy, as well as the strategic goals presented in the NRC’s draft report Artificial Intelligence Strategic Plan: Fiscal Year 2023–2027 (NUREG-2261).

The request for comments on the NRC’s AI Strategic Plan was issued in the July 5 Federal Register with a deadline of August 19. The NRC also plans to hold a public webinar on August 3 from 1–3 p.m. eastern time to receive comments on the draft plan.

Researchers from the Department of Energy’s Argonne National Laboratory are developing a “tool kit” based on artificial intelligence that will help better determine the properties of materials used in building a nuclear reactor.

Clifford

Lackey

Two student interns at Pacific Northwest National Laboratory looking for an easier way to monitor the acidity and phosphate concentrations of a process fluid like dissolved nuclear fuel have published research on a monitoring method that provides real-time data without the need for physical sampling of the substance. Their story was published on October 27 on PNNL’s website.

Student leaders: Hope Lackey conducted pH measurement and chemical analysis research during her Science Undergraduate Laboratory Internships (SULI) experience at PNNL in 2018 while she was working toward her undergraduate degree in environmental studies at the College of Idaho. Andrew Clifford, also a SULI intern and a student at the College of Idaho, partnered with Lackey between his junior and senior year, while studying for a dual bachelor’s in chemistry and math/physics.

Within the National Nuclear Security Administration, the Office of Defense Nuclear Nonproliferation Research and Development (DNN R&D) is leading efforts to drive advances in artificial intelligence and accelerate the adoption of AI-enabled technologies to solve nuclear nonproliferation and national security challenges.

The goal is to incorporate AI into advanced techniques for detecting nuclear weapons and materials. According to the NNSA, these detection capabilities support the nuclear nonproliferation and arms control goals of the United States, while also driving the development of new capabilities.

PPPL physicist Dan Boyer. (Photo: Amber Boyer/Kiran Sudarsanan)

Researchers at the Department of Energy’s Princeton Plasma Physics Laboratory are using machine learning to predict electron density and pressure profile shapes on the National Spherical Torus Experiment-Upgrade (NSTX-U), the flagship fusion facility at PPPL that is currently under repair.

The hope is that such predictions, generated by artificial neural networks, could improve the ability of NSTX-U researchers to optimize the components of experiments that heat and shape the fusion plasma.

“This is a step toward what we should do to optimize the actuators,” said PPPL physicist Dan Boyer, author of the paper, “Prediction of electron density and pressure profile shapes on NSTX-U using neural networks,” published by Nuclear Fusion, a journal of the International Atomic Energy Agency. “Machine learning can turn historical data into a simple model that we can evaluate quickly enough to make decisions in the control room or even in real time during an experiment.”

As predictive analytical tools, artificial intelligence (AI) and machine learning (ML) show promise in improving nuclear reactor safety while offering economic savings. To get a better understanding of current usage and future trends in AI and ML in the commercial nuclear power industry, the Nuclear Regulatory Commission is seeking comments from the public, the nuclear industry, and other stakeholders, as well as other interested individuals and organizations.

The Department of Energy on August 19 announced several awards to research teams applying artificial intelligence and machine learning to fusion energy. The planned total funding of $21 million is targeted at projects with time frames of up to three years; $8 million in fiscal year 2020 funding has already been committed to the work. Delivery of the balance-of-project funding will depend on future congressional appropriations.

“These awards will enable fusion researchers to take advantage of recent rapid advances in artificial intelligence and machine learning,” said Chris Fall, director of the DOE’s Office of Science. “AI and ML will help us to accelerate progress in fusion and keep American scientists at the forefront of fusion research.”

On August 4, the Department of Energy announced it will provide $57.5 million over five years to establish two multidisciplinary teams to take advantage of DOE supercomputing facilities at Argonne National Laboratory, Lawrence Berkeley National Laboratory, and Oak Ridge National Laboratory. The goal is to spur advances in the use of artificial intelligence and machine learning. Funds of $11.5 million have been made available for Fiscal Year 2020, with future funding contingent on congressional appropriations.

A quantum computer, such as this 50-bit version that IBM demonstrated at the International Consumer Electronics Show in 2018, is capable of solving tasks inaccessible to the most powerful “classic ” supercomputer. (Photo: IBM)

Rosatom, Russia’s state atomic energy corporation, and the Russian Quantum Center (RQC) on July 7 announced the creation of the first laboratory in Russia to research and develop machine learning and artificial intelligence (AI) methods on quantum computers, specializing in the application of these technologies in the nuclear industry. An agreement was signed between the RQC and Tsifrum, a Rosatom subsidiary that was created in 2019 to support the implementation of Rosatom’s digitalization strategy.

We often define noise as an unwanted disturbance, especially acoustic in nature. Neutron noise, by contrast, is a direct measure of the dynamics of a nuclear core. It can be used for core monitoring without disturbing plant operation and by using the existing core instrumentation. The European CORTEX project aims to develop an innovative core monitoring technique using neutron noise, while capitalizing on the latest developments in neutronic modeling, signal processing, and artificial intelligence.

The Department of Energy announced on March 4 that it will provide $30 million for new research on fusion energy. The funding will provide $17 million for research focused specifically on artificial intelligence (AI) and machine learning (ML) approaches for the prediction of key plasma phenomena, management of facility operations, and accelerated discovery through data science, among other topics. An additional $13 million under a separate funding opportunity will be devoted to fundamental fusion theory research, including computer modeling and simulation, focused on factors affecting the behavior of hot plasmas confined by magnetic fields in fusion reactors.