Investigation of ATHLET System Code for Supercritical Water Applications
Bibliographic record
Abstract
SuperCritical Water-cooled Reactors (SCWRs) are one of six Generation-IV nuclear-reactor concepts. They are expected to have high thermal efficiencies within the range of 45–50% owing to the reactor’s high pressures and outlet temperatures. Efforts have been made to study the supercritical phenomena both analytically and experimentally. However, codes that have been used to study the phenomena analytically have not been validated for supercritical water. The thermal-hydraulic computer code ATHLET (Analysis of THermal-hydraulics of LEaks and Transients) is used for analysis of anticipated and abnormal plant transients, including safety analysis of Light Water Reactors (LWRs) and Russian Graphite-Moderated High Power Channel-type Reactors (RBMKs). The range of applicability of ATHLET has been extended to supercritical water by updating the fluid- and transport-properties packages, thus enabling a transition from subcritical to supercritical fluid states. This extension needs to be validated using experimental data. In this work, the applicability of ATHLET code to predict supercritical-water behaviour in various heat-transfer conditions is assessed. Several well-known heat-transfer correlations for supercritical fluids are added to the code and applied for the first time in ATHLET simulations of experiments. A numerical model in ATHLET is created to represent an experimental test section and results for the heat transfer coefficient, bulk fluid temperature, and the tube inside-wall temperature are compared with the experimental data. The results from the ATHLET simulations are promising in the Normal and Enhanced Heat-Transfer Regimes. However, important phenomena such as Deteriorated Heat Transfer are currently not accurately predicted. While ATHLET can be used to develop preliminary design solutions for SCWRs, a significant effort in analysis of experimental work is required to make further advancements in the use of ATHLET for SCW applications.
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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) | 0.000 | 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".