Modeling and comparative assessment of solar thermal systems for space and water heating: Liquid water versus air-based systems
Bibliographic record
Abstract
This work pertains to the transient modeling and comparative study of active solar thermal space and water heating systems using liquid and air-type solar thermal collectors as the main energy source. The study utilizes TRNSYS to simulate the two systems in the context of Taxila's weather data (located at 33.74°N, 72.83°E), with the goal of meeting peak space and domestic water heating demands of 20 kW and 200 lit/day, respectively. The liquid water-based system (S-1) is primarily composed of a liquid solar collector, thermal storage, an auxiliary heater, connections to the hot water supply, and the space heating load through a water–air heat exchanger. In contrast, the air-based system (S-2), employs a pebble bed storage to store heat extracted from the solar thermal air collector. The heated air is subsequently used directly for space heating and passed through an air–water heat exchanger for water heating. Dynamic simulations of both systems span the entire winter season, and various performance metrics, including solar fraction, primary energy savings, and solar collector thermal efficiency, are computed. The results revealed that at the same collector area, the liquid water-based system (S-1) shows a higher solar fraction than the air-based systems (S-2) while the primary energy savings of the S-1 resulted in lower values than S-2 at smaller collector areas (< ∼30 m2) but surpasses the S-2 with increasing collector size. The optimal collector tilt for both systems is determined to be 50°, while specific storage volumes corresponding to maximum primary energy savings are estimated to be 100 and 40 L/m2 for S-1 and S-2, respectively.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".