B. Minor et K. Mathews, EXPONENTIAL CHARACTERISTIC SPATIAL QUADRATURE FOR DISCRETE ORDINATES RADIATION TRANSPORT WITH RECTANGULAR CELLS, Nuclear science and engineering, 120(3), 1995, pp. 165-186
The exponential characteristic (EC) spatial quadrature for discrete or
dinates neutral particle transport previously introduced in slab geome
try is extended here to x-y geometry with rectangular cells. The metho
d is derived and compared with current methods. It is similar to the l
inear characteristic (LC) quadrature (a linear-linear moments method)
but differs by assuming an exponential distribution of the scattering
source within each cell, S(x) = a exp(bx + cy), whose parameters are r
ootsolved to match the known (from the previous iteration) spatial ave
rage and first moments of the source over the cell. Similarly, EC assu
mes exponential distributions of flux along cell edges through which p
articles enter the cell, with parameters chosen to match the average a
nd first moments of flux, as passed from the adjacent, upstream cells
(or as determined by boundary conditions). Like the linear adaptive (L
A) method, EC is positive and nonlinear. It is more accurate than LA a
nd does not require subdivision of cells. The nonlinearity has not int
erfered with convergence. The exponential moment functions, which were
introduced with the slab geometry method, are extended to arbitrary d
imensions (numbers of arguments) and used to avoid numerical ill condi
tioning. As in slab geometry, the method approaches O(Delta x(4)) glob
al truncation error on fine-enough meshes, while the error is insensit
ive to mesh size for coarse meshes. Performance of the method is compa
red with that of the step characteristic, LC, linear nodal, step adapt
ive, and LA schemes. The EC method is a strong performer with scatteri
ng ratios ranging from O to 0.9 (the range tested), particularly so fo
r lower scattering ratios. As in slab geometry, EC is computationally
more costly per cell than current methods but can be accurate with ver
y thick cells, leading to increased computational efficiency on approp
riate problems.