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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.
D. Elbèze, D. van Houtte, E. Delchambre
Fusion Science and Technology | Volume 75 | Number 5 | July 2019 | Pages 405-411
Technical Paper | doi.org/10.1080/15361055.2019.1603534
Articles are hosted by Taylor and Francis Online.
In the Reliability, Availability, Maintainability, and Inspectability (RAMI) engineering approach used in nuclear fusion research, criticality identifies the failure modes that have the greatest impact on the availability of the studied system. Criticality is expressed as the product of the occurrence level with the severity level of failure modes. The analytical calculation shows that this formulation is equivalent to their availability provided that the duty cycle of basic functions is introduced to adjust the occurrence and the scales of occurrence and severity are homogeneous.
To consolidate the results obtained with a Reliability Block Diagram analysis, we performed a probabilistic study using an advanced Monte Carlo simulation code: the Primavera® Quantitative Schedule Risk Analysis. This method associates failure modes with conditional activities in a schedule and provides the density distribution of failures and tornado graphs to identify the highest criticality failures.
Statistical tests were performed for two operational systems, and we showed that the criticality evaluated with the RAMI approach was in good agreement with the results of the other methods. Thus, in many cases, the analytical formulas can be used during the Failure Mode, Effects, and Criticality Analysis to quickly assess availability by using a spreadsheet.