Demonstration of a Solar-Driven Ejector Chiller Assisting the Air Conditioning System of a Building
Bibliographic record
Abstract
Days with greater amounts of sunshine often have higher cooling demands. This makes solar energy one of the best solutions to mitigate the use of fossil fuels in cooling systems. On the other hand, scientific studies on ejector technology have demonstrated promising improvements in terms of enhancing the efficiencies of cooling and refrigeration systems. This work involved field testing of a solar thermal plant combined with an ejector-compression system for space cooling applications in buildings. The thermal plant uses parabolic solar collectors that focus a large area of sunlight toward tubes where circulating oil captures the energy. This energy activates the ejector system, which produces a nominal 15-kW cooling effect. A solar ejector cooling system is integrated into the CanmetENERGY Research Centre’s building, covering part of its air conditioning load, and consequently decreasing the electrical consumption of the main building’s chiller. The system has operated with a coefficient of performance (COP) of up to 0.27 at this location. Design characteristics of the system are presented and the mode of operation and analysis of collected data are elaborated.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".