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Conference Spotlight
2025 ANS Winter Conference & Expo
November 9–12, 2025
Washington, DC|Washington Hilton
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Researchers use one-of-a-kind expertise and capabilities to test fuels of tomorrow
At the Idaho National Laboratory Hot Fuel Examination Facility, containment box operator Jake Maupin moves a manipulator arm into position around a pencil-thin nuclear fuel rod. He is preparing for a procedure that he and his colleagues have practiced repeatedly in anticipation of this moment in the hot cell.
Floyd Gelhaus, John Hallam, Tor Sauar
Nuclear Science and Engineering | Volume 64 | Number 2 | October 1977 | Pages 684-693
Technical Paper | doi.org/10.13182/NSE77-A27400
Articles are hosted by Taylor and Francis Online.
The level of reliability of fuel rods operating in commercial nuclear power plants has been less than desired for a number of reasons. Several of these causes have been successfully minimized, but pellet-clad interaction failures persist. Since power and power change are dominant parameters in this failure mode, restrictions on operational maneuvers have been recommended by all U.S. fuel suppliers. Slower-than-design-allowable maneuvers decrease the plant capacity factor, which can cost a utility up to $7 million per year per plant. To assist utility engineering and operations personnel in their day-to-day decisions in this regard, the Electric Power Research Institute (EPRI) is initiating a project, designated RP895, to develop a fully computerized Power Shape Monitoring System (PSMS) for core-wide fuel rod reliability prediction. This paper describes the PSMS system and details some of the hardware/software requirements as they are now perceived. Salient results from a just-completed complementary EPRI-funded study, RP509, are described; this effort employed hand data acquisition and many man-machine interfaces that will be fully integrated and automated in the PSMS. The capabilities of the PSMS will derive from the use of modern minicomputer hardware and software and from accurate computational modules that enable near-real-time predictive capability.