A Review of Thermal Response Test Analysis Using Pumping Test Concepts
Bibliographic record
Abstract
The design of ground-coupled heat pump systems requires knowledge of the thermal properties of the subsurface and boreholes. These properties can be measured with in situ thermal response tests (TRT), where a heat transfer fluid flowing in a ground heat exchanger is heated with an electric element and the resulting temperature perturbation is monitored. These tests are analogous to standard pumping tests conducted in hydrogeology, because a system that is initially assumed at equilibrium is perturbed and the response is monitored in time, to assess the system's properties with inverse modeling. Although pumping test analysis is a mature topic in hydrogeology, the current analysis of temperature measurements in the context of TRTs is comparatively a new topic and it could benefit from the application of concepts related to pumping tests. The purpose of this work is to review the methodology of TRTs and improve their analysis using pumping test concepts, such as the well function, the superposition principle, and the radius of influence. The improvements are demonstrated with three TRTs. The first test was conducted in unsaturated waste rock at an active mine and the other two tests aimed at evaluating the performance of thermally enhanced pipe installed in a fully saturated sedimentary rock formation. The concepts borrowed from pumping tests allowed the planning of the duration of the TRTs and the analysis of variable heat injection rate tests accounting for external heat transfer and temperature recovery, which reduces the uncertainty in the estimation of thermal properties.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".