Development of Applicable Benchmark Experiments for (Th,Pu)O2 Power Reactor Designs Using TSUNAMI Analysis
Bibliographic record
Abstract
When simulating reactor physics experiments, uncertainties in nuclear data result in a bias between simulated and experimental values. For new reactor designs or for power reactor designs the bias can be estimated using a set of experiments. How- ever, the experiments used to estimate the bias must be applicable to the power reactor design of interest. Similarity studies can be performed to ensure this is the case. Here, potential experiments in the ZED-2 heavy water critical facility at Chalk River Laboratories were developed that would be applicable to the multiplication factor bias calculation of three thoria plutonia fuelled power reactor designs. The power reactor designs that were analyzed were the CANDU 6 with 37-element fuel bundles and 43-element fuel bundles, and a Canadian SCWR design with 78- element fuel assemblies. The power reactors were simulated using the code package SCALE 6.1 under burnup conditions that were determined using the lattice code DRAGON 3.06H and the diffusion code DONJON 3.02A. The intermediate results from DRAGON and DONJON were used to compare the benefits of various reactor designs. Various critical core configurations were then simulated in the ZED-2 re- actor using the SCALE 6.1 package. The similarities between the potential ZED-2 reactor experiments and the power reactors were analyzed. These results were used to design a set of experiments having sufficiently high completeness that they can be used as part of a bias calculation using the generalized linear least squares method. To do so a methodology was developed to guide the experiment set design process in which the fuel type, lattice arrangements, and coolant type are modified and the effects on the sensitivity coverage analyzed. A set of six experiments was designed for which all of the power reactor designs had a completeness of 0.7 or higher.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".