MODELING STUDIES AND CODE-TO-CODE COMPARISONS FOR PRESSURE TUBE HEAVY WATER REACTOR CORES
Bibliographic record
Abstract
Preliminary, conceptual studies have been performed previously using deterministic lattice physics (WIMS-AECL) and core physics codes (RFSP) to estimate performance and safety characteristics of various thorium-based fuels and uranium-based fuels augmented by small amounts of thorium for use in pressure tube heavy-water reactors (PT-HWRs). To confirm the validity of the results, the WIMS-AECL/RFSP results are compared against predictions made with the stochastic neutron transport code MCNP. This paper describes the development of a method for setting up an MCNP core model of at PT-HWR for comparison with WIMS-AECL/RFSP results, using a core with 37-element natural uranium fuel bundles as a test case for sensitivity studies. These studies included evaluating the sensitivity of the bias of the effective neutron multiplication factor (k eff ), a source convergence study, uncertainties correction with multiple independent simulations, the impact of irradiation map binning methods, and the impact of reflector models. A Python-based software scripting tool was developed to automate the creation, execution, and post-processing of reactor physics data from the MCNP models. The software tool and algorithm for creating an MCNP core model using data from the WIMS-AECL and RFSP models are described in this paper. Based on the preliminary evaluations of the simulation parameters with the base model, reactor physics analyses were performed for PT-HWR cores with thorium-based fuels in a 35-element bundle type. Code-to-code results demonstrate good agreement between MCNP and RFSP, giving confidence in the method developed and its applicability to other fuels and core types.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".