Sustainability, climate resiliency, and mitigation capacity of geothermal heat pump systems in cold regions
Bibliographic record
Abstract
Geothermal heat pump (GHP) systems have recently gained popularity in providing heat to buildings. In this study, the short-term and long-term performance of closed-loop horizontal GHP systems in cold regions and the effectiveness of seasonal balancing were investigated. A period of 50 years was simulated to cover the life of a conventional system. A repeating block of heat exchangers at three depths was modeled in a series of 3D multi-physics finite element simulations. Two operation modes were defined, representing the non-balanced mode and the seasonal balancing through the injection of lake water in summer. The climate conditions at the ground surface were explicitly modeled by defining a temperature boundary condition. A novel AI framework was developed to generate the projections of ground surface temperatures from air temperature and other atmospheric variables. An artificial neural network was trained using the air and ground temperature measurements at the nearby weather station. Several training approaches were compared to minimize prediction errors. The model was then used to convert the air temperatures from downscaled outputs of the CanESM2 climate model into surface temperatures. Finite element models, representing the two operation modes under three climate change scenarios and various heat exchanger spacing, were simulated and verified by comparing the resulting ground temperatures with the local measurements. The outputs were processed into extraction power, thermal output, and carbon emissions. Results suggest that in order to attain a stable heat extraction throughout a year, heat exchangers should be placed at depths that are not affected by seasonal variations of surface conditions. Simulations revealed thermal depletion and its inverse correlation with heat exchanger spacing. The seasonal balancing operation approach showed enhancement in total thermal output by 55%. The system was found to be resilient when subjected to the major climate pathways. In a rough estimate based on the average carbon footprint of electricity production in Canada, a system with 100 heat exchangers was found to save up to 800 tons and 1300 tons of equivalent CO2 emissions over its life, under the non-balanced and balanced operation modes, respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".