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Isotopes & Radiation
Members are devoted to applying nuclear science and engineering technologies involving isotopes, radiation applications, and associated equipment in scientific research, development, and industrial processes. Their interests lie primarily in education, industrial uses, biology, medicine, and health physics. Division committees include Analytical Applications of Isotopes and Radiation, Biology and Medicine, Radiation Applications, Radiation Sources and Detection, and Thermal Power Sources.
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DOE tags Los Alamos to lead $9.25 million molten salt computational research program
The Department of Energy has chosen Los Alamos National Laboratory to lead a $9.25 million collaborative project to model the behavior and properties of structural materials in molten salt through the Scientific Discovery through Advanced Computing (SciDAC) program and announced the news August 9. The team working on the five-year project includes experts from LANL; Carnegie Mellon University; and Idaho, Lawrence Berkeley, and Sandia national laboratories.
Dan Gabriel Cacuci
Nuclear Science and Engineering | Volume 186 | Number 3 | June 2017 | Pages 199-223
Technical Paper | dx.doi.org/10.1080/00295639.2017.1305244
Articles are hosted by Taylor and Francis Online.
Using the problem of inverse prediction from detector responses in the presence of counting uncertainties of the thickness of a homogeneous slab of material containing uniformly distributed gamma-emitting sources, this work investigates the possible reasons for the apparent failure of the traditional inverse-problem methods based on the minimization of chi-square-type functionals to predict accurate results for optically thick slabs. This work also compares the results produced by such methods with the results produced by applying the Predictive Modeling of Coupled Multi-Physics Systems (PM-CMPS) methodology for optically thin and thick slabs. For optically thin slabs, this work shows that both the traditional chi-square-minimization method and the PM-CMPS methodology predict the slab’s thickness accurately. However, the PM-CMPS methodology is considerably more efficient computationally, and a single application of the PM-CMPS methodology predicts the thin slab’s thickness at least as precisely as the traditional chi-square-minimization method, even though the measurements used in the PM-CMPS methodology were ten times less accurate than the ones used for the traditional chi-square-minimization method. For optically thick slabs, the results obtained in this work show that: (1) the traditional inverse-problem methods based on the minimization of chi-square-type functionals fail to predict the slab’s thickness; (2) the PM-CMPS methodology underpredicts the slab’s actual physical thickness when imprecise experimental results are assimilated, even though the predicted responses agree within the imposed error criterion with the experimental results; (3) the PM-CMPS methodology correctly predicts the slab’s actual physical thickness when precise experimental results are assimilated, while also predicting the physically correct response within the selected precision criterion; and (4) the PM-CMPS methodology is computational vastly more efficient while yielding significantly more accurate results than the traditional chi-square-minimization methodology.