NRC seeks input on developing its AI strategy

July 6, 2022, 9:30AMNuclear News

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.

Using the “New math”: Artificial intelligence and machine learning applications for the nuclear power industry

June 10, 2022, 3:00PMNuclear NewsCurtis Smith, Ahmad Al Rashdan, and Vivek Agarwal

Artificial intelligence (AI) and machine learning (ML) are helping scientists, engineers, regulators, and plant decision makers in their research and development of clean energy production to achieve a net-zero carbon footprint. While this science is new in terms of actual applications, it is fostering innovation in a variety of domains, from material discovery and qualification to advanced reactor design to supporting efficiencies in current power plants and transforming the usability of nuclear power plant control rooms.

Researchers studying seismo-acoustic data application for nuclear nonproliferation

March 28, 2022, 7:09AMANS Nuclear Cafe
Aerial view of the High Flux Isotope Reactor. (Photo: ORNL)

The nonproliferation-related monitoring of nuclear reactor operations received a boost from a new study focusing on the use of seismic and acoustic data for such purposes, ScienceDaily reported last week. The study, conducted by investigators at Oak Ridge National Laboratory, was published March 9 in the journal Seismological Research Letters.

Machine learning and environmental remediation

January 28, 2022, 9:29AMANS Nuclear CafeAndrew Amann

Due to the large amount of water used by nuclear power plants, measuring the water’s impact on the environment is a huge data processing task. It is impossible to manually measure millions of gallons, along with tracking wildlife and the weather. The data computation needed to understand environmental patterns takes massive amounts of storage and strong algorithms to uncover anomalies.

Enhanced monitoring of fuel reprocessing relies on machine learning

November 8, 2021, 9:30AMNuclear News

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.

AI-based model makes predicting fusion profiles faster

June 28, 2021, 7:00AMNuclear News

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.”

NRC seeks comments on AI’s role in U.S. nuclear power fleet

April 22, 2021, 3:04PMNuclear News

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.

Federal dollars support AI/machine learning for fusion research

August 25, 2020, 3:00PMNuclear News

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.”

Two cross-lab teams get funding for computing innovations

August 7, 2020, 10:28AMNuclear News

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.