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2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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Latest News
IAEA again raises global nuclear power projections
Noting recent momentum behind nuclear power, the International Atomic Energy Agency has revised up its projections for the expansion of nuclear power, estimating that global nuclear operational capacity will more than double by 2050—reaching 2.6 times the 2024 level—with small modular reactors expected to play a pivotal role in this high-case scenario.
IAEA director general Rafael Mariano Grossi announced the new projections, contained in the annual report Energy, Electricity, and Nuclear Power Estimates for the Period up to 2050 at the 69th IAEA General Conference in Vienna.
In the report’s high-case scenario, nuclear electrical generating capacity is projected to increase to from 377 GW at the end of 2024 to 992 GW by 2050. In a low-case scenario, capacity rises 50 percent, compared with 2024, to 561 GW. SMRs are projected to account for 24 percent of the new capacity added in the high case and for 5 percent in the low case.
Andrew E. Johnson, Dan Kotlyar, Stefano Terlizzi, Gavin Ridley
Nuclear Science and Engineering | Volume 194 | Number 11 | November 2020 | Pages 1016-1024
Technical Paper | doi.org/10.1080/00295639.2020.1723992
Articles are hosted by Taylor and Francis Online.
The serpentTools Python package is presented as a useful and efficient alternative for processing Serpent results. One positive attribute of Serpent is that many output files are exported directly in a MATLAB format, allowing for results to be loaded with minimal to no effort. However, some files for larger analyses may require immense amounts of memory to load and store all the data, leading to long wait times. To expedite the process of data handling and ease common analyses, the Computational Reactor Engineering lab at the Georgia Institute of Technology has released and is maintaining the serpentTools Python package: a set of data parsers and containers intended to streamline analysis with Serpent outputs. The parsers are capable of processing large outputs with ease, and yield all data to the user in a simple object-oriented framework. Data can be read into Python in comparable or better times than MATLAB, with the option to store only data needed for a specific purpose. Furthermore, common analyses are implemented directly into the package to expedite frequent analysis, including plotting meshed data and flux specta. serpentTools is designed to be a useful and practical manner by which the Serpent community can load and analyze data inside a Python environment. This paper presents the Python package, highlighting some basic features, and compares capabilities to similar platforms.