Energy and Exergy Analyses of a New Triple-Staged Refrigeration Cycle Using Solar Heat Source
Bibliographic record
Abstract
Abstract In this paper, energy and exergy analyses of a new solar-driven triple-staged refrigeration cycle using Duratherm 600 oil as the heat transfer fluid are performed. The proposed cycle is an integration of absorption refrigeration cycle (ARC), ejector (EJE) refrigeration cycle (ERC), and ejector expansion Joule–Thomson (EJT) refrigeration cryogenic cycles which could produce refrigeration output of different magnitude at different temperature simultaneously. Both exergy destruction and losses in each component and hence in the overall system are determined to identify the causes and locations of the thermodynamic imperfection. Several design parameters, including the hot oil outlet temperature, refrigerant turbine inlet pressure, and the evaporator temperature of ERC and EJT cycle are also tested to evaluate their effects on energy and exergy performance. It is observed that largest contribution to cycle irreversibility comes from the central receiver and heliostat field with the heat recovery vapor generator (HRVG), condenser, and ejector of ERC itself also contributing considerably. The exergy efficiency of the solar-driven triple-staged refrigeration cycle is 4% which is much lower than its energy efficiency of 10%, respectively. The results clearly reveal that thermodynamic investigations based on energy analysis alone cannot legitimately be complete unless the exergy concept becomes a part of the analysis.
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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) | 0.001 | 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".