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Godzilla is helping ITER prepare for tokamak assembly
ITER employees stand by Godzilla, the most powerful commercially available industrial robot available. (Photo: ITER)
Many people are familiar with Godzilla as a giant reptilian monster that emerged from the sea off the coast of Japan, the product of radioactive contamination. These days, there is a new Godzilla, but it has a positive—and entirely fact-based—association with nuclear energy. This one has emerged inside the Tokamak Assembly Preparation Building of ITER in southern France.
Kirk Mathews, Glenn Sjoden, Bryan Minor
Nuclear Science and Engineering | Volume 118 | Number 1 | September 1994 | Pages 24-37
Technical Paper | doi.org/10.13182/NSE94-A19019
Articles are hosted by Taylor and Francis Online.
The exponential characteristic spatial quadrature for discrete ordinates neutral particle transport in slab geometry is derived and compared with current methods. It is similar to the linear characteristic (or, in slab geometry, the linear nodal) quadrature but differs by assuming an exponential distribution of the scattering source within each cell, S(x) = a exp(bx), whose parameters are root-solved to match the known (from the previous iteration) average and first moment of the source over the cell. Like the linear adaptive method, the exponential characteristic method is positive and nonlinear but more accurate and more readily extended to other cell shapes. The nonlinearity has not interfered with convergence. We introduce the Technical Paperexponential moment functions,” a generalization of the functions used by Walters in the linear nodal method, and use them to avoid numerical ill-conditioning. The method exhibits O(Δx4) truncation error on fine enough meshes; the error is insensitive to mesh size for coarse meshes. In a shielding problem, it is accurate to 10% using 16-mfp-thick cells; conventional methods err by 8 to 15 orders of magnitude. The exponential characteristic method is computationally more costly per cell than current methods but can be accurate with very thick cells, leading to increased computational efficiency on appropriate problems.