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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.
Akio Yamamoto, Tomohiro Endo, Satoshi Takeda, Kazuya Yamaji, Hiroki Koike, Koji Asano
Nuclear Science and Engineering | Volume 200 | Number 1 | March 2026 | Pages S91-S104
Research Article | doi.org/10.1080/00295639.2024.2384236
Articles are hosted by Taylor and Francis Online.
An improved sampling method for flight distance is proposed for Monte Carlo analysis of TRISO fuel particles using the statistical geometry (STG) method. The statistically uniform distribution of fuel particles, which is usually assumed as a default sampling method of flight distance of a neutron between fuel particles, shows considerable bias on k-infinity when coating layers of a TRISO fuel particle are homogenized with a graphite matrix. The proposed new sampling method almost resolves the difference between the no-coating-layer model (coating layers are homogenized with a graphite matrix) and the explicit-coating-layer model (explicitly considers coating layers, reference). By adopting the present method, the no-coating-layer model can be used without significant loss of accuracy in the STG method of a Monte Carlo analysis. The computation time with a continuous energy Monte Carlo code for a typical fuel compact cell of a high-temperature gas-cooled reactor is reduced to one-seventh of the explicit-coating-layer model when the no-coating-layer model is used.