Characterization of Nonequilibrium Condensation of Supercritical Carbon Dioxide in a de Laval Nozzle
Bibliographic record
Abstract
Carbon capture and storage could significantly reduce carbon dioxide (CO2) emissions. One of the major limitations of this technology is the energy penalty for the compression of CO2 to supercritical conditions. To reduce the power requirements, supercritical carbon dioxide compressors must operate near saturation where phase change effects are important. Nonequilibrium condensation can occur at the leading edge of the compressor, causing performance and stability issues. The characterization of the fluid at these conditions is vital to enable advanced compressor designs at enhanced efficiency levels but the analysis is challenging due to the lack of data on metastable fluid properties. In this paper, we assess the behavior and nucleation characteristics of high-pressure subcooled CO2 during the expansion in a de Laval nozzle. The assessment is conducted with numerical calculations and corroborated by experimental measurements. The Wilson line is determined via optical measurements in the range of 41–82 bar. The state of the metastable fluid is characterized through pressure and density measurements, with the latter obtained in a first-of-its-kind laser interferometry setup. The inlet conditions of the nozzle are moved close to the critical point to allow for reduced margins to condensation. The analysis suggests that direct extrapolation using the Span and Wagner equation of state (S–W EOS) model yields results within 2% of the experimental data. The results are applied to define inlet conditions for a supercritical carbon dioxide compressor. Full-scale compressor experiments demonstrate that the reduced inlet temperature can decrease the shaft power input by 16%.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".