Numerical Method for Simulating High Pressure CO2 Flows With Nonequilibrium Condensation
Bibliographic record
Abstract
A numerical method for compressible flows with nonequilibrium condensation is reconstructed for simulating supercritical CO2 flows with nonequilibrium condensation under high pressure conditions. Thermophysical properties are interpolated from pressure-temperature look-up tables and density-internal energy look-up tables, which are generated using the polynomial equations in REFPROP. We employ the high pressure nonequilibrium condensation model in which the critical radius of a liquid droplet is modified by considering non-ideal gas. We simulate high pressure CO2 flows through a Laval nozzle, which was experimentally investigated by Lettieri et al. High-pressure CO2 passes through the nozzle, leading to a decrease in its pressure and temperature. It reaches the supercooled condition near the throat. Nucleation and the subsequent growth of droplets lead to an increase in the condensate mass fraction in the diverging area. The proposed method for real gas reproduced the peak of pressure distribution owing to the release of latent heat, whereas the numerical result assuming ideal gas is different from the experimental result. The nucleation region obtained using the present method is earlier and narrower than that in the case of ideal gas. The early and rapid nucleation leads to the high mass condensate rate at the outlet. These results show that considering the real gas effect and nonequilibrium condensation is crucial for developing the impeller of a compressor for the supercritical CO2 Brayton cycle.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".