ANS is committed to advancing, fostering, and promoting the development and application of nuclear sciences and technologies to benefit society.
Explore the many uses for nuclear science and its impact on energy, the environment, healthcare, food, and more.
Explore membership for yourself or for your organization.
Conference Spotlight
2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
Latest Magazine Issues
Mar 2026
Jan 2026
Latest Journal Issues
Nuclear Science and Engineering
March 2026
Nuclear Technology
February 2026
Fusion Science and Technology
April 2026
Latest News
NN Asks: What hurdles stand in the way of nuclear power’s global expansion?
Jake Jurewicz
Nuclear technology is mature. It provides firm power at scale with minimal externalities and has done so for decades. The core problem isn’t about the technology—it is how the plants are built. Nuclear construction has a well-documented history of cost and schedule overruns. Previous nuclear plants often spent more than twice what was first budgeted, making nuclear among the power technologies with the largest average cost overruns worldwide.
Recent projects illustrate how severe the problem can be. In South Carolina, the V.C. Summer nuclear expansion saw projected costs rise from roughly $10 billion to more than $25 billion before the project was abandoned in 2017, by which time more than $9 billion had already been spent and customers were stuck paying for a site they have yet to benefit from.
Zhenze Li, Thanh Son Nguyen, Matthew Herod, Julie Brown, Hamed Mozafarishamsi
Nuclear Technology | Volume 210 | Number 9 | September 2024 | Pages 1535-1548
Research Article | doi.org/10.1080/00295450.2023.2240160
Articles are hosted by Taylor and Francis Online.
Natural analogues are systems that have evolved over geological timescales with features similar to one or several components of a deep geological repository (DGR). Natural analogues complement short-duration laboratory studies since they are existing reflections of many long-term processes that might affect the performance of a repository. Mathematical models are often used for the post-closure safety assessment of a DGR. Confidence in the models’ predictions is enhanced when the models successfully simulate the past evolution of a natural analogue. This paper summarizes the Canadian Nuclear Safety Commission’s (CNSC’s) recent research on natural analogues to inform on (1) glacial erosion, (2) engineered barrier system, and (3) uranium reactive transport in the context of DGRs for radioactive wastes. Glaciation and its erosion are prominent factors impacting the performance of future DGRs at high latitudes in the northern hemisphere. The authors have reviewed the field data from the Greenland Analogue Project, developed a conceptual and mathematical model for the simulation of the thermal conditions within the Greenland ice sheet, as well as the thermal-hydraulic conditions at its base and the ice sheet velocity, and eventually estimated the erosion rate at the site.
The Cigar Lake Analogue demonstrates the long-term radionuclide containment capability of the illite clay zone enveloping the ore body, serving as an analogy to the engineered clay barriers. The CNSC and University of Ottawa analyzed 129I in the Cigar Lake core samples, and modeled and correlated the diffusion-dominated transport of radionuclides over the geological evolution of the Cigar Lake deposit. The results provide information on the mobility of fission products and significant radionuclides in conditions analogous to the source, engineered barriers, and near-field host rock of a DGR.
The reactive transport and geochemistry of the Kiggavik-Andrew Lake uranium deposit mineralization and remobilization was another natural uranium deposit analogue studied by the CNSC. A reactive transport model was established according to the conceptualized geochemical processes and run under specified boundary and initial conditions to validate the geochemical processes. The geometry, timing, geochemistry, and fluid composition were used as model constraints.