Numerical Simulation of Non-Equilibrium Condensation in Supercritical CO2 Compressors
Bibliographic record
Abstract
Abstract There is increasing interest in supercritical CO2 processes, such as Carbon Capture and Storage, and electric power production, which require compressors to pressurize CO2 above the critical point. For supercritical compressor operation close to the critical point there is a concern that the working fluid could cross into the subcritical regime which could lead to issues with compressor performance if condensation was to occur in regions where the fluid dropped below the saturation point. Presently, the question of whether there is sufficient residence time at subcritical conditions for condensation onset in supercritical CO2 compressors is an unresolved issue. A methodology is presented towards providing a validated simulation capability for predicting condensation in supercritical CO2 compressors. The modeling framework involves the solution of a discrete droplet phase coupled to the continuum gas phase to track droplet nucleation and growth. The model is implemented in the CRUNCH CFD® Computational Fluid Dynamics code that has been extensively validated for simulation at near critical conditions with a real fluid framework for accurate predictions of trans-critical CO2 processes. Results of predictions using classical nucleation theory to model homogeneous nucleation of condensation sites in supersaturated vapor regions are presented. A non-equilibrium phase-change model is applied to predict condensation on the nuclei which grow in a dispersed-phase droplet framework. Model validation is provided against experimental data for condensation of supercritical CO2 in a De Laval nozzle including the Wilson line location. The model is then applied for prediction of condensation in the compressor of the Sandia test loop at mildly supercritical inlet conditions. The results suggest that there is sufficient residence time at the conditions analyzed to form localized nucleation sites, however, droplets are expected to be short lived as the model predicts they will rapidly vaporize.
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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) | 0.001 | 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".