Russia builds lab for developing quantum artificial intelligence

China is on track to overtake the United States in nuclear power capacity by 2026, according to GlobalData, a U.K.-based research and analytics company.

More than 160 GW of nuclear capacity will likely be added globally between 2020 and 2030, some 66 percent of which is anticipated to take place in China, India, and Russia, the company reported on September 9. China alone is set to account for more than 50 percent (83 GW) of the new capacity, followed by India with 8.9 percent (14.5 GW) and Russia with 6.4 percent (10.5 GW). GlobalData also projects that during the same period, more than 76 GW of nuclear capacity will be retired.

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.

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 International Atomic Energy Agency has emphasized that elevated concentrations of cesium-134, cesium-137, and ruthenium-103 recently detected over Northern Europe pose no risk to human health or the environment, even as the agency continues to investigate their origin.

Leningrad:Unit II-1 has been credited with a nearly 15 percent decrease in cooling water usage at the plant. Photo: Rosatom

Using a VVER-1200 reactor for Leningrad II Unit 1 has resulted in a nearly 15 percent reduction in cooling water usage at the Leningrad nuclear power plant, according to Rosatom, Russia’s state atomic energy corporation. “The design features of the new power units can significantly reduce the amount of water consumed by a nuclear power plant from natural reservoirs,” said Vladimir Pereguda, director of the plant. He credited the replacing of RBMK-1000 units with VVER-1200 ones for a decrease of 730.7 million cubic meters of seawater withdrawn from Kopory Bay, a 14.8 percent drop in 2019 compared to 2018. Kopory Bay is located in the southern part of the Gulf of Finland on the Baltic Sea. “We will continue to observe such indicators to reduce the environmental impact, since the Leningrad [plant] is gradually replacing RBMK-1000 units with VVER-1200 units,” Pereguda added.

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.