Multigroup Adjoint Transport Solution Using the Method of Cyclic Characteristics
Bibliographic record
Abstract
Most perturbation theory calculation methods for neutron transport problems are based on the assumption that the solution to the adjoint transport problem is known. Here we develop an adjoint transport solution based on the method of cyclic characteristics (MOCC) for two-dimensional fuel assembly problems with isotropic scattering. The main advantages of the MOCC method are (a) it requires lower computing time and memory spaces than the collision probability (CP) method and (b) it does not require the boundary surface currents as for the method of characteristics with isotropic tracking. In the MOCC the adjoint characteristics equations associated with a cyclic tracking line are formulated in such a way that a closed form for the adjoint angular function can be obtained. The mathematical relationship between the adjoint function obtained by CP method and the adjoint function by MOCC is also presented. In order to speed up the MOCC solution algorithm, group-reduction and group-splitting techniques based on the structure of the adjoint scattering matrix are implemented. In addition, a combined forward flux/adjoint function iteration scheme, based on the group-splitting technique and the common use of large numbers of variables storing tracking-line data and exponential values, is proposed to reduce the computing time. To demonstrate the efficiency of these algorithms, calculations are performed on a 17 × 17 pressurized water reactor lattice, a 37-pin CANDU [Canada deuterium uranium reactor] cell, and the Watanabe-Maynard benchmark. Comparisons of adjoint function and keff results obtained by the MOCC and the CP method are presented.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".