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Going Nuclear: Notes from the officially unofficial book tour
I work in the analytical labs at one of Europe’s oldest and largest nuclear sites: Sellafield, in northwestern England. I spend my days at the fume hood front, pipette in one hand and radiation probe in the other (and dosimeter pinned to my chest, of course). Outside the lab, I have a second job: I moonlight as a writer and public speaker. My new popular science book—Going Nuclear: How the Atom Will Save the World—came out last summer, and it feels like my life has been running at full power ever since.
John F. Carew, Kai Hu, Gabriel Zamonsky
Nuclear Science and Engineering | Volume 136 | Number 2 | October 2000 | Pages 282-293
Technical Paper | doi.org/10.13182/NSE99-96
Articles are hosted by Taylor and Francis Online.
Recently, a uniform equal-weight quadrature set, UEn, and a uniform Gauss-weight quadrature set, UGn, have been derived. These quadratures have the advantage over the standard level-symmetric LQn quadrature sets in that the weights are positive for all orders,and the transport solution may be systematically converged by increasing the order of the quadrature set. As the order of the quadrature is increased,the points approach a uniform continuous distribution on the unit sphere,and the quadrature is invariant with respect to spatial rotations. The numerical integrals converge for continuous functions as the order of the quadrature is increased.The numerical characteristics of the UEn quadrature set have been investigated previously. In this paper, numerical calculations are performed to evaluate the application of the UGn quadrature set in typical transport analyses. A series of DORT transport calculations of the >1-MeV neutron flux have been performed for a set of pressure-vessel fluence benchmark problems. These calculations employed the UGn (n = 8, 12, 16, 24, and 32) quadratures and indicate that the UGn solutions have converged to within ~0.25%. The converged UGn solutions are found to be comparable to the UEn results and are more accurate than the level-symmetric S16 predictions.