Application of the image‐well method for transient borehole thermal energy storage systems with complex boundaries
Bibliographic record
Abstract
Abstract This study introduces a new analytical framework that employs the image‐well method to simulate the spatial and temporal temperature distribution in vertical borehole thermal energy storage (BTES) systems. The model accommodates complex boundary shapes and conditions, including insulation, convection, and constant temperature, without requiring iterative solutions at each time step. The model's accuracy and utility are demonstrated through an application to a borehole heat exchanger cluster arranged in an octagonal shape with insulating boundaries, based on a BTES site in Drake Landing (Canada). Model predictions are validated against a finite element model, showing a root‐mean‐square error of 0.012°C. A global sensitivity analysis highlights the influence of thermal parameters on system performance, identifying the heat flux of the borehole heat exchanger as the most sensitive parameter. Overall, this approach combines the advantages of analytical and numerical techniques to provide a clear and efficient tool for evaluating BTES systems, offering significant potential for advancing sustainable energy solutions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".