Development and Testing of TRACE/PARCS ECI Capability for Modelling CANDU Reactors with Reactor Regulating System Response
Bibliographic record
Abstract
The use of the USNRC codes TRACE and PARCS has been considered for the coupled safety analysis of CANDU reactors. A key element of CANDU simulations is the interactions between thermal-hydraulic and physic phenomena with the CANDU reactor regulating system (RRS). To date, no or limited development has taken place in TRACE-PARCS in this area. In this work, the system thermal-hydraulic code TRACE_Mac1.0 is natively coupled with the core physic code PARCS_Mac1.0, and RRS control is implemented via the exterior communications interface (ECI) in TRACE. ECI is used for coupling the external codes to TRACE, including additional physical models and control system models. In this work, a Python interface to the TRACE ECI library is developed, along with an RRS model written in Python. This coupling was tested using a CANDU-6 IAEA code coupling benchmark and a 900 MW CANDU model for various transients. For the CANDU-6 benchmark, the transients did not include RRS response, however, the TRACE_Mac1.0/PARCS_Mac1.0 coupling and ECI script functionality was compared to the previous benchmark simulations, which utilized external coupling. For the 900 MW CANDU simulations, all aspects of the ECI module and RRS were included. The results from the CANDU-6 benchmark when using the built-in coupling are comparable to those previously achieved using external coupling between the two codes with coupled simulations taking 2x to 3x less execution time. The 900 MW CANDU simulations successfully demonstrate the RRS functionality for the loss of flow events, and the coupled solutions demonstrate adequate performance for figure-of-eight flow instability modeling.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".