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Conference Spotlight
2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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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Latest News
Shifting the paradigm of supply chain
Chad Wolf
When I began my nuclear career, I was coached up in the nuclear energy culture of the day to “run silent, run deep,” a mindset rooted in the U.S. Navy’s submarine philosophy. That was the norm—until Fukushima.
The nuclear renaissance that many had envisioned hit a wall. The focus shifted from expansion to survival. Many utility communications efforts pivoted from silence to broadcast, showcasing nuclear energy’s elegance and reliability. Nevertheless, despite being clean baseload 24/7 power that delivered a 90 percent capacity factor or higher, nuclear energy was painted as risky and expensive (alongside energy policies and incentives that favored renewables).
Economics became a driving force threatening to shutter nuclear power. The Delivering the Nuclear Promise initiative launched in 2015 challenged the industry to sustain high performance yet cut costs by up to 30 percent.
Han Gon Kim, Soon Heung Chang, Byung Ho Lee
Nuclear Science and Engineering | Volume 115 | Number 2 | October 1993 | Pages 152-163
Technical Paper | doi.org/10.13182/NSE93-A28525
Articles are hosted by Taylor and Francis Online.
The Optimal Fuel Shuffling System (OFSS) was developed for the optimal design of pressurized water reactor (PWR) fuel loading patterns. An optimal loading pattern is defined in which the local power peaking factor is lower than a predetermined value during one cycle and the effective multiplication factor is maximized to extract the maximum energy. The OFSS is a hybrid system in which a rule-based system, fuzzy logic, and an artificial neural network (ANN) are connected with each other. The rule-based system classifies loading patterns into two types by using several heuristic rules and a fuzzy rule. The fuzzy rule is introduced to achieve a more effective and faster search. Its membership function is automatically updated in accordance with the prediction results. The ANN predicts core parameters for the patterns generated from the rule-based system. A backpropagation network is used for fast prediction of the core parameters. The ANN and fuzzy logic can be used to improve the capabilities of existing algorithms. The OFSS was demonstrated and validated for cycle 1 of theKori-1 PWR.
