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ORNL–General Atomics partnership on ceramic matrix composites
A memorandum of understanding has been signed by Oak Ridge National Laboratory and General Atomics Electromagnetic Systems (GA-EMS) with the objective of working together on advanced ceramic matrix composite materials for applications in extreme environments. Materials that can withstand extreme temperatures, radiation, corrosion, and mechanical stress are required in aerospace, defense, energy, and other sectors.
According to the agreement, the San Diego–based GA-EMS will use resources from ORNL’s Manufacturing Demonstration Facility to develop “scalable, efficient manufacturing techniques for extreme environment materials including precursors, fibers, composites, and coatings utilized in carbon/carbon (C/C), carbon/silicon carbide (C/SiC), and SiC/SiC composite systems.”
Tom Burr, Jeremy Conlin, Jianwei Hu, Jack Galloway, Vladimir Henzl, Howard Menlove, Martyn Swinhoe, Stephen Tobin, Holly Trellue, Timothy Ulrich
Nuclear Science and Engineering | Volume 172 | Number 2 | October 2012 | Pages 180-192
Technical Paper | doi.org/10.13182/NSE11-73
Articles are hosted by Taylor and Francis Online.
Estimating plutonium (Pu) mass in spent nuclear fuel assemblies (SFAs) helps inspectors ensure that no Pu is diverted. Therefore, nondestructive assay (NDA) methods are being developed to assay Pu mass in SFAs. Uncertainty quantification is an important task in most assay methods, and particularly for SFA assay. A computer model (MCNPX) is being used to predict isotope masses and the spatial distribution of masses in virtual SFAs for 64 combinations of initial fuel enrichment (IE), fuel utilization [burnup (BU)], and cooling time (CT) values. Additional MCNPX modeling for the same 64 virtual SFAs provided the expected detector responses (DRs) for several NDA techniques such as the passive neutron albedo reactivity method and the 252Cf interrogation with prompt neutrons method.A previous paper describes one uncertainty quantification approach involving Monte Carlo (MC) simulation using individually any of six new NDA options together with IE, BU, and CT. This paper provides an interpretation of the MC approach that is suited for a numerical Bayesian alternative, separately assesses the impact of MCNPX interpolation error, and compares several options to use subsets of IE, BU, CT, and one DR.
