Numerical Modeling of a Soil‐Borehole Thermal Energy Storage System
Bibliographic record
Abstract
Core Ideas Borehole thermal energy storage is studied with a 3D transient fluid flow and heat transfer model. BTES heat extraction efficiency increases with decreasing soil thermal conductivity. BTES efficiency decreases with convective heat losses associated with high soil permeability. Borehole thermal energy storage (BTES) in soils combined with solar thermal energy harvesting is a renewable energy system for the heating of buildings. The first community‐scale BTES system in North America was installed in 2007 at the Drake Landing Solar Community (DLSC) in Okotoks, AB, Canada, and has since supplied >90% of the thermal energy for heating 52 homes. A challenge facing BTES system technology is the relatively low efficiency of heat extraction. To better understand the fluid flow and heat transport processes in soils and to improve BTES efficiency of heat extraction for future applications, a three‐dimensional transient coupled fluid flow and heat transfer model was established using TOUGH2. Measured time‐dependent injection temperatures and fluid circulation rates at DLSC were used as model inputs. The simulations were calibrated using measured soil temperature time series. The simulated and measured temperatures agreed well with a subsurface having an intrinsic permeability of 1.5 × 10 −14 m 2 , thermal conductivity of 2.0 W m −1 °C −1 , and a volumetric heat capacity of 2.3 MJ m −3 °C −1 . The calibrated model served as the basis for a sensitivity analysis of soil thermal and hydrological parameters on BTES system heat extraction efficiency. Sensitivity analysis results suggest that: (i) BTES heat extraction efficiency increases with decreasing soil thermal conductivity; (ii) BTES efficiency decreases with background groundwater flow; (iii) BTES heat extraction efficiency decreases with convective heat losses associated with high soil permeability values; and (iv) unsaturated soils show higher overall heat extraction efficiency due to convection onset at higher intrinsic permeability values.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".