When the Department of Energy’s Advanced Research Projects Agency–Energy launched the Optimizing Nuclear Waste and Advanced Reactor Disposal Systems (ONWARDS) program in 2022, it posed a challenge that the nuclear industry had never seriously confronted before: how to design waste management solutions that anticipate the coming shift to advanced reactors and not merely retrofit existing systems built for an older generation of technology. The program’s objectives were ambitious—reduce disposal footprint, enable scalable pathways for unfamiliar waste streams, and build the technical foundations for future disposal—yet also tightly grounded in the realities of emerging nuclear fuel cycles. For the nuclear community, this was a timely call. Advanced reactors were accelerating toward deployment, but the waste management systems needed to support them had not kept pace.

Researchers with the Massachusetts Institute of Technology, working with scientists from Lawrence Berkeley National Laboratory and the University of Orléans, have modeled radionuclide behavior in deep geologic formations, offering a tool for developing a defensible safety case for the underground disposal of radioactive waste.

Researchers at the DIII-D National Fusion Facility, the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory (LBNL), and the Energy Sciences Network (ESnet) are teaming up to make the high-performance computing (HPC) powers of NERSC available to DIII-D researchers through ESnet—a high-speed data network. Their collaboration, described in a May 29 news release, in effect boosts the computing power behind DIII-D’s diagnostic tools to make more data from fusion experiments available to researchers at DIII-D in San Diego and to the global fusion research community.

Research with the Department of Energy’s Thomas Jefferson National Accelerator Facility (Jefferson Lab) has revealed new insights into short-range correlations—the brief pairings of nucleons (protons with neutrons, protons with protons, or neutrons with neutrons) in the nuclei of atoms. The study, published in Nature, used precision measurements to determine that short-range correlations differ depending on the density of the nucleus, that is, how many nucleons it contains.

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.