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Conference Spotlight
2025 ANS Winter Conference & Expo
November 8–12, 2025
Washington, DC|Washington Hilton
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FPoliSolutions demonstrates RISE, an RIPB systems engineering tool
The American Nuclear Society’s Risk-informed, Performance-based Principles and Policy Committee (RP3C) has held another presentation in its monthly Community of Practice (CoP) series. Former RP3C chair N. Prasad Kadambi opened the October 3 meeting with brief introductory remarks about the RP3C and the need for new approaches to nuclear design that go beyond conventional and deterministic methods. He then welcomed this month’s speakers: Mike Mankosa, a project engineer at FPoliSolutions, and Cesare Frepoli, the company’s president, who together presented “Introduction to RISE: A Digital Framework for Maintaining a Risk-Informed Safety Case for Current and Next Generation Nuclear Power Plants.”
Watch the full webinar here.
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.
