Supercritical CO2 heat transfer enhancement in large diameter horizontal tubes by internal fins
Bibliographic record
Abstract
CO2 is a natural refrigerant with low global warmingpotential, widely employed in refrigeration, heat pumps, and powergeneration cycles. The thermophysical properties of supercritical CO2(s-CO2) near the pseudo-critical point exhibit significant variationwith temperature. In horizontally heated tubes, buoyancy force withinthe gravitational field causes the hotter, gas-like s-CO2 to accumulatenear the top, while the cooler, liquid-like s-CO2 remains concentratedin the center and lower regions. This non-uniform temperaturedistribution reduces heat transfer coefficients along both the axialand circumferential directions, particularly at the top of the tube.To mitigate this issue, this study investigates the incorporation ofeight thin internal fins within a horizontally oriented, large-diametersteel tube to enhance heat transfer performance in a high buoyancyforce (i.e., Richardson number > 0.1). A numerical investigationis performed for a 3D horizontal tube with an inner diameter of22.14 mm, subjected to a high heat flux boundary condition. Thenumerical results are validated against experimental data for a smoothhorizontal tube, demonstrating good agreement with a maximumtemperature error of less than 5%. The fins facilitate heat transfertowards the cooler regions of the s-CO2, significantly modify flowcirculation, and promote mixing, thereby improving heat transferefficiency. The results reveal that the internally finned tube achievesthe same heat transfer rate using 5.7% less tube solid mass andreduces the maximum temperature at the end of the heated section by50.8%. These findings underscore the potential of internally finnedhorizontal tubes to enable more cost-effective and efficient heatexchanger designs.
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 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 | 0.000 |
| 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 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".