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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.
Seung-Hyuk Lee, Hyun-Koon Kim, Sang-Ryeol Park, Soon-Heung Chang
Nuclear Technology | Volume 94 | Number 3 | June 1991 | Pages 407-415
Technical Paper | Heat Transfer and Fluid Flow | doi.org/10.13182/NT91-A15818
Articles are hosted by Taylor and Francis Online.
A statistical core thermal design methodology for generating the limit departure from nucleate boiling ratio (DNBR) is proposed and used in assessing the best-estimate thermal margin in a reactor core. This new methodology adopts a modified Latin hypercube sampling method. In this method, the independencies of the input variables are verified through a correlation coefficient test for statistical treatment of their uncertainties. Next, the DNBR response distribution is determined through a goodness-of-fit test. Finally, a limit DNBR with a one-sided 95% probability and a confidence level of 0.95 is estimated. This methodology is simpler than the conventional statistical method using the response surface and Monte Carlo simulation technique, but it maintains the same level of confidence in the limit DNBR result. This methodology is applied to the Yonggwang Nuclear Units 3 and 4 reactor cores using preliminary design data. From this study, it is deduced that the proposed methodology is useful for design application.