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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.
Todd J. Urbatsch, R. Arthur Forster, Richard E. Prael, Richard J. Beckman
Nuclear Technology | Volume 111 | Number 2 | August 1995 | Pages 169-182
Technical Paper | Nuclear Criticality Safety Special / Fission Reactor | doi.org/10.13182/NT95-A35128
Articles are hosted by Taylor and Francis Online.
The Monte Carlo code MCNP has three different, but correlated, estimators for calculating keff in nuclear criticality calculations: collision, absorption, and track length estimators. The combination of these three estimators, the three-combined keff estimator, is shown to be the best keff estimator available in MCNP for estimating keff confidence intervals. Theoretically, the Gauss-Markov theorem provides a solid foundation for MCNP’s three-combined estimator. Analytically, a statistical study, where the estimates are drawn using a known covariance matrix, shows that the three-combined estimator is superior to the estimator with the smallest variance. Empirically, MCNP examples for several physical systems demonstrate the three-combined estimator’s superiority over each of the three individual estimators and its correct coverage rates. Additionally, the importance of MCNP’s statistical checks is demonstrated.
