Design considerations for thermal energy storage systems in subarctic climate.
Bibliographic record
Abstract
Borehole thermal energy storage system (BTES) is a mature technology to provide heating needs of buildings. It can thus contribute to the transition to sustainable green energies in northern Canada. Its efficiency strongly depends on the design and the subsurface conditions in which it is implemented. This study presents a sensitivity analysis of the main parameters influencing BTES operating in the subsurface near freezing conditions. Numerical simulations were performed in FEFLOW to estimate the average heat pump coefficient of performance (COP) of 68 different scenarios of a BTES with 25 borehole heat exchangers (BHE). An initial scenario was constructed and the COP was averaged during heat extraction periods. Then, 17 parameters were varied at constant steps (10% and 30% of their initial value) and their averaged COP was compared to the base case scenario. Results highlight parameters that need to be accurately estimated and optimized in order to maximize BTES efficiency. Thermal power injection/extraction, surface/volume ratio, BHE spacing, BTES layout compared to local groundwater direction and subsurface thermal properties are the parameters with the highest influence on the operating temperature. BTES initial scenario averages a COP of 2.92 over 4 years of operation. Worst-case scenario shows a mean COP of 2.74, whereas best-case scenario averages a COP of 3.05. This leads to a 13 GJ (+5.3%) energy gain difference between the worst and the best-case scenario over ~5.6 years of operation of heat extraction.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".