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Conference Spotlight
2026 ANS Annual Conference
May 31–June 3, 2026
Denver, CO|Sheraton Denver
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AI at work: Southern Nuclear’s adoption of Copilot agents drives fleet forward
Southern Nuclear is leading the charge in artificial intelligence integration, with employee-developed applications driving efficiencies in maintenance, operations, safety, and performance.
The tools span all roles within the company, with thousands of documented uses throughout the fleet, including improved maintenance efficiency, risk awareness in maintenance activities, and better-informed decision-making. The data-intensive process of preparing for and executing maintenance operations is streamlined by leveraging AI to put the right information at the fingertips for maintenance leaders, planners, schedulers, engineers, and technicians.
Kazuyoshi Miki, Kotaro Inoue
Nuclear Science and Engineering | Volume 59 | Number 2 | February 1976 | Pages 161-169
Technical Paper | doi.org/10.13182/NSE76-A15686
Articles are hosted by Taylor and Francis Online.
A new calculation code, the Hot Spot Probabilistic Evaluation Code (HOSPEC), is presented for evaluating hot-spot factors in a fast reactor. This code calculates the probability distribution of temperature in the whole core by means of a Monte Carlo method. Each Monte Carlo trial involves a complete thermal conduction analysis, thereby reducing errors due to assumptions in analytic procedures currently in use. With this code it is possible to determine the probability that fuel pellets, fuel pins, or subassemblies will exceed the limiting temperature, as well as determine the number of such “hot spots” that will develop. A quantitative comparison is made of the results obtained from this code with those from other analyses of a prototype fast reactor. The comparison has indicated, among other points, the following findings: 1. For zero hot spots, a conventional analytic evaluation code SHOSPA gives conservative results, i.e., ∼20°C at the fuel center, at a 3σ confidence level. 2. It is of crucial importance to take into account the temperature dependence of the properties of the materials. Neglecting such dependence leads to a much more conservative temperature prediction, e.g., ∼50°C at the fuel center.