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ANS Student Conference 2025
April 3–5, 2025
Albuquerque, NM|The University of New Mexico
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Colin Judge: Testing structural materials in Idaho’s newest hot cell facility
Idaho National Laboratory’s newest facility—the Sample Preparation Laboratory (SPL)—sits across the road from the Hot Fuel Examination Facility (HFEF), which started operating in 1975. SPL will host the first new hot cells at INL’s Materials and Fuels Complex (MFC) in 50 years, giving INL researchers and partners new flexibility to test the structural properties of irradiated materials fresh from the Advanced Test Reactor (ATR) or from a partner’s facility.
Materials meant to withstand extreme conditions in fission or fusion power plants must be tested under similar conditions and pushed past their breaking points so performance and limitations can be understood and improved. Once irradiated, materials samples can be cut down to size in SPL and packaged for testing in other facilities at INL or other national laboratories, commercial labs, or universities. But they can also be subjected to extreme thermal or corrosive conditions and mechanical testing right in SPL, explains Colin Judge, who, as INL’s division director for nuclear materials performance, oversees SPL and other facilities at the MFC.
SPL won’t go “hot” until January 2026, but Judge spoke with NN staff writer Susan Gallier about its capabilities as his team was moving instruments into the new facility.
William S. Grenzebach, Carolyn D. Heising, Thomas J. Marx
Nuclear Technology | Volume 108 | Number 3 | December 1994 | Pages 421-433
Technical Paper | Reactor Operation | doi.org/10.13182/NT94-A35024
Articles are hosted by Taylor and Francis Online.
In today’s operating environment of nuclear power plants, setpoints are established for key plant parameters, such as temperature, pressure, and flow rate. Reducing excursions beyond these setpoints would save millions of dollars as a result of improved plant availability and improve plant safety as well. The statistical method of maximum likelihood factor analysis is presented, and the results of two computer runs are given. The results of the statistical analysis indicate that it is possible to consistently rank order the eleven tracked variables of the reactor coolant system. Implementation of the maximum likelihood factor method would permit the decision maker to predict unanticipated transients and reduce plant unavailability.