Parametric Quantification of Low GWP Refrigerant for Thermosyphon Driven Solar Water Heating System
Bibliographic record
Abstract
Modern lifestyle, industrialization and economy thrive on energy which is getting expensive overtime. Thermosyphon based systems are getting attraction for their promising heat transfer efficiency and zero energy utilization. Refrigerants having ozone depletion potential (ODP) and high Global Warming Potential (GWP) have been banned or under time bared permission under Montreal (1987) and Kyoto (1997) protocols. We have devolved a Refrigerant Parametric Quantification (RPQ) method for the choice of optimal refrigerant for density driven solar water heaters. A set of 29 refrigerants are simulated Using REFPROP under various temperature and pressure conditions. The optimal parameters of thermosyphon system are identified from governing equations, international environment safety protocols and open literature. The proposed RPQ method shows most appropriate refrigerant for given temperature range. In second part, the proposed system is simulated in TRNSYS using forced circulation method. In the end, a glass evacuated tube collector is developed and tested on the principal of thermosyphon, employing the best refrigerant emerged from simulation study.
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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 | 0.001 |
| 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".