Simulation and experimental investigation of two hybrid solar domestic water heaters with drain water heat recovery
Bibliographic record
Abstract
In this paper, the performance of two solar domestic hot waters (SDHW) with drain water heat recovery (DWHR) units is investigated. Both SDHW systems are recently installed at the Archetype Sustainable Twin Houses at Kortright Center, Vaughan, Ontario. The first SDWH system in House A consists of a flat plate solar thermal collector in combination with a gas boiler and a DWHR unit. The second SDHW system in House B includes an evacuated tube solar collector, an electric tank, and a DWHR unit. Both systems are modeled in TRNSYS, and the models are validated by experimental data. The addition of the DWHR and the flat-plate solar thermal collector would result in 1831 kWh of annual energy saving in House A. While the addition of the DWHR and the evacuated tube collector in House B would result in an annual energy saving of 1771 kWh. Subsequently, the models are used to investigate the performance of similar systems for five major Canadian cities of Halifax, Montreal, Toronto, Edmonton, and Vancouver. The conjunctions of solar thermal collectors with DWHR units are found most beneficial in Edmonton. It is also noted from experimental and simulated results that flat-plate solar collector-based water heater produced more thermal energy than the system based on the evacuated tube solar collector for all major Canadian cities. Copyright © 2015 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".