Analysis of the enhanced heat transfer characteristics of supercritical <scp> CO <sub>2</sub> </scp> and <scp> CO <sub>2</sub> </scp> / <scp>Xe</scp> mixture working fluids in <scp>PCHE</scp> and prediction of heat transfer correlation
Bibliographic record
Abstract
Abstract To investigate the enhanced heat transfer behaviour of supercritical carbon dioxide and CO 2 /Xe mixed working fluids in printed circuit heat exchangers (PCHE), and to improve the cycle efficiency of the Brayton cycle, the flow and heat transfer performance of the mixed working fluid were analyzed by varying the Xe mass fraction in CO 2 , the inlet mass flow rate, and the inlet temperature. Results show that under supercritical conditions, the change in the mass fraction of Xe is combined with the heat transfer characteristics of mixed working fluids and the thermal efficiency of the Brayton cycle, filling a gap in the combination of these two research areas. As the mass fraction of Xe increases from 0% to 30%, the peak heat transfer coefficient decreases by 36.8%, but the thermal efficiency is significantly improved. When the Reynolds number reaches 77,000, the comprehensive heat transfer evaluation index (PEC) increases with increasing Xe mass fraction, improving the heat transfer performance. As the mass flow rate increases from 400 to 1000 kg/(m 2 s), the peak heat transfer coefficient increases by 378.29%, and the average value of the comprehensive heat transfer evaluation index increases by 100.21%. Before the inlet temperature reaches the critical temperature, as the temperature increases, the peak heat transfer coefficient increases by 40.73%, the average Nusselt number increases by 38.09%. A heat transfer correlation for CO 2 /Xe mixtures was derived with an error range within ±20%. The research results will provide a theoretical foundation for the design of CO 2 /Xe binary mixed working fluid PCHEs.
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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 | 0.000 |
| 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) | 0.001 | 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".