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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.
Mohammad Pourgol-Mohamad, Mohammad Modarres, Ali Mosleh
Nuclear Technology | Volume 165 | Number 3 | March 2009 | Pages 333-359
Technical Paper | Thermal Hydraulics | doi.org/10.13182/NT165-333
Articles are hosted by Taylor and Francis Online.
This paper discusses an integrated thermal-hydraulic (TH) uncertainty analysis methodology with an application to the Loss-of-Fluid Test (LOFT) test facility large-break loss-of-coolant accident (LBLOCA) transient. The methodology is intended for applications to best-estimate analyses of complex TH codes. The goal is to develop an integrated method to make such codes capable of comprehensively supporting the uncertainty assessment with the ability to handle important accident transients. The proposed methodology considers the TH code structural uncertainties (generally known as model uncertainty) explicitly by treating internal submodel uncertainties and by propagating such model uncertainties in the code calculations, including uncertainties about input parameters. The methodology is probabilistic, using the Bayesian approach for incorporating available evidence in quantifying uncertainties in the TH code predictions. The types of information considered include experimental data, expert opinion, and limited field data, in treating both model and input parameter uncertainties. The code output is further updated through additional Bayesian updating with available experimental data from the integrated test facilities. The methodology uses an efficient Monte Carlo sampling technique for the propagation of uncertainty, in which a modified Wilks' sampling criteria of tolerance limits is used to significantly reduce the number of simulations. This paper describes the key elements of the uncertainty analysis methodology and summarizes its application to the LOFT test facility LBLOCA.