Seasonal ground cold energy storage potential for data center cooling using thermosyphon: A comparative study of five cities in Canada for carbon footprint reduction
Bibliographic record
Abstract
Artificial Ground Freezing (AGF), using two-phase-closed-thermosyphon (TPCT) device, is an emerging technique for storing the cold energy of the winter season in the ground. The stored energy can later be used in the summer season for data center cooling, building comfort, etc. However, this energy storage potential has not been quantified so far. This study is conducted using an experimentally validated numerical model that simulates the ground freezing phenomenon in five Canadian cities over a 2-years period for 1%, 5%, 10% and 20% soil porosity. Each city offers a different energy storage potential based on ambient conditions and soil porosity — ranging from 3.31 – 23.19 MWh per TPCT, annually. In addition, up to 4,851 kg of carbon footprint can be reduced per TPCT in each city. This study examines and ranks the cities based on the potential for cold energy storage and associated carbon footprint for new and commissioned (already operating) cooling-intensive facilities. Yellowknife is ranked number one for commissioned cooling-intensive facilities and Montreal is ranked number one for new cooling-intensive facilities. It also provides a pre-feasibility guideline for selecting the geographic location to implement the novel idea in cities with similar weather conditions. A new parameter, carbon emission efficiency, is also introduced and used for ranking.
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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".