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The transformation of the NRC: 50 years of commissioners
The dust is beginning to settle following the whirlwind of changes at the Nuclear Regulatory Commission over the past year, and 2025 ultimately may be viewed as a transformative year, as well as the year the NRC celebrated its golden anniversary. The 12 months of that milestone year brought more change to the agency in its composition, its mandate, and its relationship to the executive branch than any comparable period in the preceding four decades.
Now at 51 years and counting, the NRC is working with a full commission and issuing new rulemakings to both regulate and support the next round of nuclear deployments. With the turbulence of 2025 still fresh in our minds, Nuclear News decided it was a good time to revisit the professional backgrounds of all 42 NRC commissioners who have served over the agency’s 50-year history to see how the composition of the commission has evolved over time.
Akihiro Kitano (JAEA), Ken Nakajima (Kyoto Univ)
Proceedings | 2018 International Congress on Advances in Nuclear Power Plants (ICAPP 2018) | Charlotte, NC, April 8-11, 2018 | Pages 1205-1210
In the Nuclear facilities, especially Fukushima daiichi nuclear power plant, radiation exposure reduction measures have to be carried out appropriately so as to be able to work in the place. Therefore, we need to grasp the radioactive contaminations level in the area. In order to specify the place and the density of the radioactive contamination, we had to estimate the radioactive contamination density of various locations by material sampling measurement, surface smear measurement, or surface dose rate measurement with collimated radiation detectors conventionally. However, these methods require a lot of time and work. To solve this problem, we are developing the estimation method of the radioactive contamination distribution with machine learning from the spatial dose rate that can be acquired easily.
The estimation of the radioactive contamination from the spatial dose has two issues mainly. One is the difficulty of the improving estimation accuracy because of radiation scattering and attenuation with the structure in the building. The other is that it takes much time to make the accurate model with simulation and so on. With machine learning, we will be able to estimate the contamination distribution quickly, and it will lead to exposure reduction of workers. In this study, we constructed the building model of the Operating floor of Fukushima daiichi unit3(1F-3), and set the radioactive contamination on the floor divided to 10×13 mesh. We trained the relationship of the spatial dose distribution with the radioactive contamination densities, locations, and the material structures in the area.
As the result, in the case of setting the various contamination densities to the each mesh, the estimated contamination densities were consistent with the setting contamination densities. Therefore, the feasibility of this method was confirmed.
