Performance Characterization of Novel Caisson-Based Thermal Storage for Ground Source Heat Pumps
Bibliographic record
Abstract
<p>To avoid the catastrophic results of climate change, a major shift towards clean, sustainable and renewable energy technologies is inevitable. Since buildings are capable of on-site thermal energy generation and exhibiting predictive patterns of heating and cooling, many attempts are currently underway to incorporate more renewable energy (e.g., geothermal heating and cooling) systems in buildings. Subsurface geothermal resources represent a great potential for direct use of energy; put another way, the planet is a six sextillion (10²¹) metric ton battery that is continually being replenished by solar radiation, lightning, and heat from its deep-down molten core. Despite the enormous energy potential, geothermal systems are not adopted widely due to three main reasons: high drilling costs, energy imbalance in the ground, and lack of drilling space. To address these challenges, a novel foundation-based geothermal heat exchanger system incorporating phase change material (PCM) has been designed. The proposed technology, utilizing a foundation caisson, reduces the construction and installation costs by integrating energy and structural systems together into a single ground installation. The present study aims to characterize the thermal performance of the proposed system through a numerical model that is validated and calibrated using the experimental data generated from a demonstration site that hosts a foundation caisson. Efficacy of the use of PCM on improving the thermal performance of the system is characterized in terms of energy savings and greenhouse gas (GHG) emission reduction. Sensitivity studies demonstrate improvement in the performance of the caisson with the use of PCM with increased thermal conductivity. Also, as the seasonal heat injection to extraction ratio deviates further away from unity, the PCM helps improve the thermal performance as evidenced through reduced primary energy consumption.</p>
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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".