Numerical analysis of non-equilibrium steam condensing flows in various Laval nozzles and cascades
Bibliographic record
Abstract
When steam is used in fluid machinery, phase transition can occur that affects not only the flow fields but also machine performance. Therefore, to achieve an accurate prediction of steam condensing flow using computational fluid dynamics (CFD), phase-transition phenomena should be considered and a non-equilibrium wet-steam model is required. Such a model is implemented in this study using the in-house code T-Flow, and the flow fields – including phase-transition phenomena – in various Laval nozzles are examined. The results for multi-phase flows can be obtained in relatively short time by using mixture assumption and an inner-iteration method. The calculated results reflect the characteristics of the condensing flows well and are comparable with those obtained experimentally. Also, it was found that the superheating level of incoming steam can explain the tendency of condensation in the nozzles considered in a simple way. In addition, steam condensing flows in the blade cascades were simulated. As a result, the predicted blade loading agreed well with the experimental data and the superheating level at inlet was responsible for the condensation trend not only in the nozzles but also in the cascades. In future work, the characteristics of steam condensing flow in a steam turbine where complex flows and phase transition occur can be investigated using the presented model.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".