The experimental data of performance mapping of propane thermoelectric sub-cooler
Bibliographic record
Abstract
As responsible for 7.8% of global greenhouse emissions, the heat pump and refrigeration sectors are important in achieving sustainable development goals [1]. To reduce emission levels, traditional environmentally harmful working fluids that cause ozone depletion, which are listed in the Montreal Protocol [2], are being phased out. Furthermore, energy efficiency studies were conducted to improve the performance of the standard cycle. One way to increase the performance of the cycle is to use subcooling methods, that is, further cooling the working fluid in the condenser/gas-cooler [3]. Among the alternatives, the thermoelectric subcooling method is promising because of easy implementation, serving both in heating and cooling with thermoelectric modules (TEM), and adaptability to different capacities. The main aim of this study is to create a performance mapping of a thermoelectric subcooler (TESC) under different operating conditions and generate comparative results of two different manufacturing methods, conventional drilling and welding, and 3-D printing, that were used on the R290 heat exchanger of the TESC. According to the results, it was shown that the 3-D printed R290 heat exchanger was superior to a conventionally manufactured R290 heat exchanger in terms of thermal performance. Results showed that 3-D printed TESC can provide 270 W of additional cooling capacity with a cooling coefficient of performance of 1.22, while simultaneously providing 490 W of heating capacity with a heating coefficient of performance of 2.22.
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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 | 0.001 |
| 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) | 0.004 | 0.001 |
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".