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Division Spotlight
Fuel Cycle & Waste Management
Devoted to all aspects of the nuclear fuel cycle including waste management, worldwide. Division specific areas of interest and involvement include uranium conversion and enrichment; fuel fabrication, management (in-core and ex-core) and recycle; transportation; safeguards; high-level, low-level and mixed waste management and disposal; public policy and program management; decontamination and decommissioning environmental restoration; and excess weapons materials disposition.
Meeting Spotlight
2024 ANS Annual Conference
June 16–19, 2024
Las Vegas, NV|Mandalay Bay Resort and Casino
Standards Program
The Standards Committee is responsible for the development and maintenance of voluntary consensus standards that address the design, analysis, and operation of components, systems, and facilities related to the application of nuclear science and technology. Find out What’s New, check out the Standards Store, or Get Involved today!
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Nuclear Science and Engineering
June 2024
Nuclear Technology
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Latest News
U.S. nuclear capacity factors: Ideal for data centers?
Baseload nuclear generation doesn’t get the respect it deserves, if you ask nuclear operators. But the hyperscale data centers that process our digital lives—like the one right next to the Susquehanna plant in northeastern Pennsylvania—are pushing electricity demand up. Clean, reliable capacity now looks a lot more valuable.
Siyao Gu, Miltiadis Alamaniotis
Nuclear Technology | Volume 210 | Number 1 | January 2024 | Pages 100-111
Research Article | doi.org/10.1080/00295450.2023.2226914
Articles are hosted by Taylor and Francis Online.
Ever since the attack on the World Trade Center on September 11, prevention of nuclear terrorist attacks in urban environments has been a major focus for homeland security. To that end, mobile radiation sensor networks that are deployed within a specific area to acquire consecutive measurements are a first line of defense against the illicit movement of nuclear threats. However, sensor network deployment is a complex process imposed on physical and financial constraints and dynamically varying conditions. In this work, reinforcement learning (RL) is applied to control the sequential deployment of a mobile radiation sensor network within a specific geographic area. RL is utilized for dynamically learning of the environment and subsequent decision making on the optimal position of the network sensors driven by shared mutual information. RL has the benefit of allowing the network to learn and update a deployment strategy online from an initially unknown state.
The performance of the RL method is demonstrated through self-contained exploration and interaction between sensors in a source search scenario for detecting a radioactive source with a set of mobile detectors within the space of the University of Texas at San Antonio campus. Results exhibit the efficiency and efficacy of (a-sequential) RL in comparison to the sequential placement of the mobile sensors, showcasing optimality in accuracy and efficiency in source detection.