Numerical modeling of thermally induced ground deformations around potential geothermal energy storage wells in northern Quebec
Bibliographic record
Abstract
Literature from the past two decades demonstrates the feasibility of utilizing borehole geothermal energy storage (BTES) system for the heating of buildings in the cold climate region like Northern Quebec. However, BTES systems would generate an increase in temperature in surrounding soil formations, which may induce ground deformation and result in unexpected accidents. This study investigates the effect of BTES systems of 50-year service life period on thermal consolidation of surrounding soil formations in northern Quebec, where BTES sites are covered by a large quantity of unconsolidated glacial tills. A fully coupled thermal-hydro-mechanical modeling is conducted using Abaqus, and cases with different configuration and operation temperature are modeled. Thermally induced pore pressure generation and dissipation processes are simulated and analyzed. Thermally induced deformation of soil formations is also addressed. The glacial till in the upper layer is prone to sustain strain softening and the glacial till in the lower layer is prone to sustain strain hardening during the periodical thermal operation. A BTES system with a large geometric scale or with a high operation temperature (60°C in this study) would impose significant influence on the glacial till formation, which is displayed by significant changes in pore water pressure and ground deformations.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".