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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.
Tetsuo Tamaoki, Masuo Sato, Ryoichi Takahashi
Nuclear Technology | Volume 100 | Number 3 | December 1992 | Pages 378-389
Technical Paper | Reactor Operation | doi.org/10.13182/NT92-A34732
Articles are hosted by Taylor and Francis Online.
An advanced diagnostic method is proposed that uses automated pattern recognition for reactor noise. The method enables intensive diagnosis of known anomalies and extensive detection of unknown plant states. It also enables automatic learning of reference noise patterns for an unknown plant state and monitoring of the subsequent state change by regarding the new reference patterns as those for a known plant state. Application results for the method used on artificial noise data produced by a fast breeder reactor noise simulator are presented. A diagnostic system based on the proposed method will make it possible to automatically accumulate and make the most of anomaly data from actual power plants, although it is still difficult to identify the cause of an abnormality automatically.
