Sensitivity Analysis of Phase Change Modeling for Non-Ideal Fluids in Turbomachinery Applications
Bibliographic record
Abstract
Abstract Investigating phase change phenomena with numerical methods reveals a strong dependence on the selected model equations. The phase change from gas to liquid, or condensation, is primarily governed by nucleation and droplet growth. Numerous model equations for both processes exist in the literature, typically based on the law of ideal gases and empirical factors historically fitted for water. Despite this fluid-specific calibration, results show significant model sensitivity even for water. This effect increases for other fluids, such as CO2, which exhibit strong nonideal thermophysical properties. To enable reliable modeling for such fluids, it is essential to first identify which model parameters exert dominant influence. This study contributes by systematically varying model approaches and parameters. For both water and CO2, a test case of supersonic flow through a Laval nozzle is considered. The sensitivity study examines variations in the calculation of the critical energy barrier for nucleation, droplet growth rate, and type of modeling the droplet size distribution. A comparison of two fundamentally different numerical schemes further distinguishes the influence of physical modeling from that of numerical variability. Results for CO2 show a sensitivity to droplet size distribution modeling comparable to water, but with a broader spread for phase change modeling due to fluid nonidealities. These findings support a systematic uncertainty estimate for phase change modeling in turbomachinery operated with CO2 and point to the need for experimental validation of condensation models in nonideal fluids.
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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".