Simulation study of tritium atmospheric dispersion of loss of vacuum accident of a fusion reactor
Bibliographic record
Abstract
BackgroundTritium can be released into the environment in a loss of vacuum (LOVA) scenario in a fusion reactor. The simulation of the atmospheric dispersion behaviour of tritium is one of the core components of the assessment of the radioactive consequences.PurposeThis study aims to analyse the behaviour of tritium dispersion in the atmosphere after a fusion reactor accident.MethodsBased on the Gauss model and the Pasquill stability classification method, an analytical model of tritium dispersion was developed for transient cases considering the effects of gravitational settling, smoke lifting, and wind speed, etc. The calculation of the model for dry settling at the ground boundary was improved by adding ground reflection coefficients to the Gauss model. Finally, the Canadian tritium release experiment and the tritium release accident at the Savannah River plant in the United States were used to verify the applicability of the model.ResultsVerification results show that the accuracy of the developed model is the same as that of UFOTRI and the HotSpot 3.0 code. For the LOVA scenario of International Thermonuclear Experimental Reactor (ITER), the atmospheric dispersion behaviour of tritium is obtained for multiple release heights, different wind velocities and tritium phased releases.ConclusionsThe phased release of tritium results in two highly radioactive regions along the downwind direction, and the increase in release height and wind speed will enhance the atmospheric diffusion behaviour of tritium and thus reduce the accumulation of radioactivity in the near field.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".