Modelling of condensing steam flows in Laval nozzles with ANSYS CFX
Bibliographic record
Abstract
The results of the International Wet Steam Modelling Project presented at the Wet Steam Conference in Prague in 2016 again gave rise to a discussion about the suitability of the flow solver ANSYS CFX for the prediction of spontaneous condensation. In this article, the main reason for the discrepancy between results obtained with CFX and measurement data is identified. It could be found that in CFX the temperature of already existing droplets is used for evaluating the fluid properties involved in the nucleation process. This is not in agreement with the isothermal classical nucleation theory, which is based on the assumption that clusters of critical radius are formed at vapour temperature. The most dominant parameter affected by this is the surface tension, the evaluation of which can be altered easily by the user. The influence of the correction is illustrated by means of standard Laval nozzle test cases, and a significant improvement of the results compared to measurement data can be observed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".