Global and Local Sensitivity Analysis of Heat Transport in Fractured Rock Using a Modified Implementation of the <scp>LH</scp> ‐ <scp>OAT</scp> Method
Bibliographic record
Abstract
Abstract Thermal remediation of contaminated sites in fractured bedrock is complicated by the characterization and identification of heat transfer between fractures and matrix, and the complex interactions between the hydrogeological and thermodynamic parameters. A three‐dimensional numerical model was applied to investigate these issues using global and local sensitivity analyses for the significance of six variables that potentially influence the heating performance in fractured rock. These variables include the radius and energy delivery strength of the heat source (which were used to study the scale effect and heating processes), the fracture aperture, fracture spacing, groundwater flow velocity, and the thermal conductivity of the rock matrix. A discrete Latin Hypercube‐One‐at‐A‐Time (LH‐OAT) scheme is proposed and utilized as an experimental design and data analysis method for the discrete variables that apply to this case. The results show that at all four monitoring points within the heating area, the radius of the source and energy delivery strength are the most sensitive parameters. To minimize heat dissipation, additional heating wells are demonstrated to be effective for a small or pilot scale site (5 < r < 10 m), while the increase of energy delivery strength is more applicable for larger sites. Extra efforts should be invested to minimize heat dissipation when large fractures (2 b > 1600 μm) are identified in the heating area. Re‐samplings and re‐evaluations with one‐way perturbation in both positive and negative directions are thus suggested to avoid biased results caused by perturbations that occur in only positive or negative directions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".