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Nuclear Energy Conference & Expo (NECX)
September 8–11, 2025
Atlanta, GA|Atlanta Marriott Marquis
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Deep Space: The new frontier of radiation controls
In commercial nuclear power, there has always been a deliberate tension between the regulator and the utility owner. The regulator fundamentally exists to protect the worker, and the utility, to make a profit. It is a win-win balance.
From the U.S. nuclear industry has emerged a brilliantly successful occupational nuclear safety record—largely the result of an ALARA (as low as reasonably achievable) process that has driven exposure rates down to what only a decade ago would have been considered unthinkable. In the U.S. nuclear industry, the system has accomplished an excellent, nearly seamless process that succeeds to the benefit of both employee and utility owner.
Dan Gabriel Cacuci
Nuclear Science and Engineering | Volume 186 | Number 3 | June 2017 | Pages 199-223
Technical Paper | 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.