Experiments and Heat Transfer Correlation Validations of Low-Parameter Region of sCO2 Flow in a Long Thin Vertical Loop
Bibliographic record
Abstract
The focus of this study is to accurately predict the convective heat transfer of CO2 to ensure the safe and efficient design of supercritical and trans-critical CO2 energy systems. The heat transfer performance of CO2 is crucial for the stable operation of these systems. This research study explored the flow and heat transfer behavior of CO2 in a long thin vertical loop through experiments. A range of key parameters were set in the experiments to ensure the broad coverage of operating conditions. The inlet temperature was set between 10 °C and 45 °C, the pressure ranged from 6.0 to 9.0 MPa, mass fluxes varied from 500 to 1500 kg/m2s, and the heat flux reached up to 300 kW/m2. Experiments were performed at Reynolds number 104. By adjusting these parameters, the experiments were able to simulate CO2 heat transfer performance under various real-world conditions. Additionally, numerical simulations were employed to further analyze CO2’s flow and heat transfer behavior. Different turbulence models were tested, and the results showed that the SST k-ω model can best predict CO2 convective heat transfer, effectively capturing the complex heat transfer characteristics under varying flow conditions. The research outcomes were compared with established correlations through the Nusselt number, and while a ±30% uncertainty was observed, the overall agreement was satisfactory. This indicates that the experimental and simulation results are within a reasonable range, confirming their reliability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".