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Conference Spotlight
Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Nuclear energy for maritime shipping and coastal applications
The Boston-based Deon Policy Institute has published a white paper that examines the applications of nuclear energy in the maritime sector—specifically, floating nuclear power plants and nuclear propulsion for commercial vessels. Topics covered include available technologies, preliminary cost estimates, and a status update on the regulatory framework.
Unique opportunity: The paper points out that nuclear energy has the potential to benefit the shipping industry with high energy efficiency, lower operating costs, and zero carbon emissions. The report has a special focus on Greece, a nation that controls about 20 percent of the global commercial fleet and thus has an opportunity to take a leading role in the transition to nuclear-powered shipping.
Robert Nshimirimana, Ajith Abraham, Gawie Nothnagel, Andries Engelbrecht
Nuclear Technology | Volume 207 | Number 1 | January 2021 | Pages 147-166
Technical Paper | doi.org/10.1080/00295450.2020.1740562
Articles are hosted by Taylor and Francis Online.
A manual approach to radiography process optimization is a time-consuming and labor-intensive process. Therefore, a virtual environment in which all of the processes of optimization for a desired radiography experiment or setup are conducted is highly desirable. Such an environment should be able to provide the capability to arrive at radiographic scanning parameters that are optimized to within preset criteria for design purposes. In this paper, a simplified approach toward achieving this is described, and calculated radiography results are benchmarked against experiments. A ray-tracing technique combined with the exponential law of attenuation was used to provide the primary function of such a virtual environment, which is the modeling of the radiography system. Radiography quality parameters such as contrast, penetration, unsharpness, and resolution were calculated using predefined definitions and fed directly into a particle swarm optimization routine that searched for the best radiography design parameters in an iterative feedback loop between the simulator and the optimizer modules. The aim of this paper is to show that a rather simple radiography simulation approach can already provide sufficient data for system design optimization purposes without the need to develop or utilize a comprehensive, competitive radiography simulator. The simplified approach provides a direct “uncomplicated” virtual environment for basic radiography training and basic experimental planning.