Advanced Modeling of the Core of a Prismatic Block Gas-Cooled Reactor
Bibliographic record
Abstract
Pursuant to Canada’s small modular reactor (SMR) action plan to achieve net-zero emissions, a SMR based on gas-cooled reactors (GCRs) is one of the potential concepts among the non-water-cooled reactor technologies to be considered for deployment in the near future in Canada. A coupled modeling approach to account for feedback effects using higher-fidelity solutions is becoming increasingly viable due to improvements in phenomenological models and advanced computing. This study aims to improve simulation accuracy for the prismatic block GCR scenarios of interest using an integrated coupled modeling toolset that loosely couples computational fluid dynamics (CFD), system thermalhydraulics (TH), and neutronics codes.The differentiating aspect of this study is the execution of three-way coupled analyses for the GCRs that can account for both the component- and system-level scales. Within this study, a part of the reactor core was simulated using CFD, coupled with a one-dimensional system TH code for the solution of the rest of the reactor circuit. The power feedback effects inside the reactor core simulated with CFD were obtained from a neutronics solver. An explicit representation of the core components was undertaken in the CFD model to facilitate the detailed modeling of the coolant flow within the channels and its interaction with the reflectors, fuel, and control rods.Two industrially relevant test operating scenarios, fully withdrawn and incremental increase of insertion level of control rods, were simulated to showcase the suitability of the coupled analyses. The reactivity coefficients and control rod worth using the coupled multiphysics analyses are presented. The results demonstrate the capability of the developed algorithm for coupling three modeling disciplines for its application in GCR core simulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".