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
April 2026
Nuclear Technology
February 2026
Fusion Science and Technology
Latest News
Going Nuclear: Notes from the officially unofficial book tour
I work in the analytical labs at one of Europe’s oldest and largest nuclear sites: Sellafield, in northwestern England. I spend my days at the fume hood front, pipette in one hand and radiation probe in the other (and dosimeter pinned to my chest, of course). Outside the lab, I have a second job: I moonlight as a writer and public speaker. My new popular science book—Going Nuclear: How the Atom Will Save the World—came out last summer, and it feels like my life has been running at full power ever since.
Zhe Chuan Tan, Zhi Yuan Feng, Kan Wang
Nuclear Science and Engineering | Volume 200 | Number 1 | March 2026 | Pages S456-S465
Research Article | doi.org/10.1080/00295639.2025.2456895
Articles are hosted by Taylor and Francis Online.
With growing interest in accident-tolerant dispersion fuels such as fully ceramic micro-encapsulated fuel, explicit modeling processes play an increasingly important role in the precise simulation of particle transport in stochastic media. The Random Sequential Addition (RSA) method and Discrete Element Method (DEM) are considered the more mature and accurate explicit modeling processes. RSA is inherently inhibited by an upper limit to the particle packing fraction whereas DEM can become very computationally expensive as the latter simulates physical interactions between each particle in contact. An Iterative RSA-DEM method is proposed to solve the high computational requirements of DEM. An initial number of particles are placed inside the stochastic medium via RSA, and the particles are then subject to free fall via DEM for a preindicated amount of time. Particles are then placed in the remaining unoccupied space of the stochastic medium via the Improved RSA method and once again are subject to free fall via DEM. This process thus iterates until the desired particle packing fraction is achieved. The particles are then redistributed throughout the stochastic medium using a mesh-filling method. The time savings are calculated in two ways: first, by maintaining the particle packing fraction and reducing the particle radius, and second, by directly changing the particle packing fraction. Last, the correctness of Iterative RSA-DEM is verified by comparing the calculated effective multiplication factors with those from the original DEM.