Optimization of the Thermal Performance of a CO2 Geothermal Thermosyphon
Bibliographic record
Abstract
Summary This paper showcases the utilization of response surface methodology (RSM) to optimize the performance of a CO2 geothermal thermosyphon. The design parameters include the filling ratio, the flow rate of the cooling fluid and the difference between the ground and the cooling fluid inlet temperatures, while the response parameters are the heat transfer rate (Q) and effectiveness (εff). Using the RSM, two models were developed to establish correlations between input parameters and corresponding response. Overall, it is concluded from the RSM that the flow rate and the temperature difference between the ground and the heat transfer fluid inlet temperatures are the factors that have the greatest impact on Q and εff, while the filling ratio has only a slight effect on Q and no effect on εff. The maximum heat transfer rate and effectiveness achieved are 1.86 kW and 47.8%, respectively. Moreover, these optimal values are associated with different flow rate levels, indicating distinct operating regions for maximizing Q and εff within the GT system. Therefore, a multi-response optimization approach is essential to simultaneously optimize both Q and εff.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".