Computational Fluid Dynamics Thermal Analysis of ITER Pressure Suppression Tanks
Bibliographic record
Abstract
Abstract The aim of the paper is to present the results of the investigation of the thermal conditions (temperature distribution, heat losses) in the support system of the vapor suppression tank (VST) of the vacuum vessel pressure suppression system (VVPSS), a safety important system of ITER fusion reactor, protecting the vacuum vessel (VV) against overpressures. The VVPSS includes four VSTs of identical volume and installed as two stacked assemblies. The study focuses on the optimization of the design of the thermal insulation at the bottom of the VSTs, interfacing with the basement and also on the identification of the thermal loads at the interface between the tank support and the tank pressure boundary. A computational fluid dynamics (CFD) analysis of the VST has been performed for four different insulation configurations and considering both steady-state and transient loads following accidental conditions. The results of the analysis are used to provide recommendation on the optimum configuration of the thermal insulation. Measures for minimization of the thermal gradient in the critical area of the joint between the tank hemispherical head and support skirt to limit the thermal fatigue on the welds are also suggested.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".