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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Remembering ANS member Gil Brown
Brown
The nuclear community is mourning the loss of Gilbert Brown, who passed away on July 11 at the age of 77 following a battle with cancer.
Brown, an American Nuclear Society Fellow and an ANS member for nearly 50 years, joined the faculty at Lowell Technological Institute—now the University of Massachusetts–Lowell—in 1973 and remained there for the rest of his career. He eventually became director of the UMass Lowell nuclear engineering program. After his retirement, he remained an emeritus professor at the university.
Sukesh Aghara, chair of the Nuclear Engineering Department Heads Organization, noted in an email to NEDHO members and others that “Gil was a relentless advocate for nuclear energy and a deeply respected member of our professional community. He was also a kind and generous friend—and one of the reasons I ended up at UMass Lowell. He served the university with great dedication. . . . Within NEDHO, Gil was a steady presence and served for many years as our treasurer. His contributions to nuclear engineering education and to this community will be dearly missed.”
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.
