Thermal response functions of borehole heat exchangers with horizontal pipes
Bibliographic record
Abstract
Accurate prediction of subsurface heat transfer is essential to the efficient operation of ground-source heat pump systems. Thermal response functions (TFs) are commonly used for this purpose and can be derived from physical models or reconstructed from monitoring data. In a recent study, TFs were reconstructed from fluid temperature measurements of 40 borehole heat exchangers, capturing not only the 100 m vertical boreholes but also horizontal pipe sections ranging from 3 to 46 m in length. Slight variations in heat exchange capability were observed, primarily linked to differences in horizontal pipe lengths. To investigate these variations, we develop a 3-D numerical model of borehole heat exchangers with horizontal connecting pipes of varying lengths. The model simulates matching model-based TFs for all 40 data-driven TFs. The model-based and data-driven TFs were then compared. While all data-driven TFs provided acceptable temperature predictions, only 38% of the model-based TFs did. The comparison highlights respective limitations of both approaches, with data-driven TFs depending on measurement quality and model-based TFs on modeling assumptions. Given their accuracy and speed, data-driven TFs are promising for modeling, with physical models reserved for validation. Overall, the findings enhance understanding of individual borehole performance and support improved design practices that account for horizontal pipe sections.
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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 | 0.001 |
| 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 | 0.001 | 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".