Numerical investigation of an energy pile-based solar-assisted ground source heat pump system for space heating and cooling
Bibliographic record
Abstract
While ground source heat pump systems are a sustainable method to climatize buildings, their long-term performance in cold climates may lead to ground overcooling and, thereby, performance deterioration over time, especially when the loads are significantly imbalanced. Solar-assisted ground source heat pump systems are an alternative to mitigate the ground thermal imbalance by injecting solar thermal energy into the ground to balance energy extraction by the heat pump while also improving the heat pump performance. This study numerically evaluates the performance of a solar-assisted ground source heat pump system for three different climate zones and three modes of operation using a thoroughly validated finite volume-based computational model. A 0.139 m diameter helical steel pile installed to a depth of 18.288 m below the ground was used in this study as the ground heat exchanger. Realistic building load profiles were obtained using OpenStudio for residential applications in Calgary, Toronto, and New York and coupled with a computational fluid dynamics solver to evaluate the overall system performance. The results show that the solar-assisted system shows a significant performance improvement of 16.3% over a non-solar enhanced system for the coldest climatic zone studied. Out of the three system operation modes studied (mode 1: solar loop is turned off at low irradiance, mode 2: solar thermal energy is redirected based on the season, mode 3: solar thermal energy is redirected based on the heat pump operating mode), mode 3 yields the best overall heat pump performance for all the locations (COP: 3.72, 3.74, 4.24 for Calgary, Toronto, and New York, respectively). Interestingly, the results indicate the best-suited system operating mode depends on the degree of dominance of the building's heating load.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".