A New Method Based on Thermal Response Tests for Determining Effective Thermal Conductivity and Borehole Resistivity for Borehole Heat Exchangers
Bibliographic record
Abstract
Research on borehole heat exchangers is described on the development of a method for the determination, based on thermal response tests, of the effective thermal conductivity and the thermal resistivity for borehole heat exchangers. This advance is important, because underground thermal energy storage increasingly consists of systems with a large number of borehole heat exchangers, and their effective thermal conductivities and thermal resistivities are significant parameters in the performance of the system (whether it contains a single borehole or a field of boreholes). Borehole thermal energy storages provide a particularly beneficial method for using ground energy as a clean thermal energy supply. This benefit is especially relevant in cities with significant smog in winter. Here, the authors describe, in detail, the development of a formula that is a basis for the thermal response test that is derived from Fourier’s Law, utilizing a new way of describing the basic parameters of the thermal response test, i.e., the effective thermal conductivity and the thermal resistivity. The new method is based on the resistivity equation, for which a solution giving a linear regression with zero directional coefficient is found. Experimental tests were performed and analyzed in support of the theory, with an emphasis on the interpretation differences that stem from the scope of the test.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".