Thermal performance of a solar-assisted slinky foundation heat exchanger coupled with a heat pump in a cold climate
Bibliographic record
Abstract
Using the excavation of a building’s foundation offers a cost-effective solution to alleviate the high installation costs hindering the widespread adoption of ground-source heat pump systems. However, limited land space in urban areas and higher heating loads in cold climates pose challenges. Issues like ground thermal imbalances and prolonged freezing around the heat exchanger can impair performance. To address these, a novel solar-assisted ground source heat pump with a slinky foundation ground heat exchanger and a solar-heated recovery heat exchanger loop embedded in the building’s foundation is proposed. A 3D transient finite element numerical model is developed to evaluate the performance of the proposed system. Realistic building energy loads obtained from a building energy simulation with time-varying ambient temperature and solar irradiation are coupled to the foundation heat exchanger to predict the long-term transient performance of the system. Results show that implementing a solar-assisted foundation heat exchanger system reduces soil freezing from 58.3 % to 32.4 % of the year, and heat pump shut-off occurrences caused by low entering fluid temperature drop from 38.9 % to 5.8 %. Additionally, incorporating an auxiliary heater eliminates heat pump shut-offs and reduces the soil freezing period to 6.3 %. Moreover, extending the heat exchangers beyond the footprint of the house mitigates the soil freezing problem completely and reduces the demand for auxiliary heating.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".