A Method to Improve the Performance of Capillary Barriers Using Heated Air Flow
Bibliographic record
Abstract
The investigation described in this thesis is aimed at determining the effect of heated air flow on the behaviour of capillary barriers. In order to achieve the objectives of this investigation, a number of tasks were undertaken, as described hereinafter. First, a laboratory scale testing was carried out to determine the effect of heated air flow on the volumetric water content (VWC) and matric suction in a layer of soil representing the coarse grained soil layer of a capillary barrier. Several types of instruments were used to measure the VWC, matric suction, and temperature at different locations within the soil. Next, a numerical analysis was undertaken to simulate the behaviour of the soil mass subjected to thermal changes in the laboratory experiments. Lastly, two case studies were analyzed with and without the heated air flow in the coarse grained soil layer to validate the proposed model. The method used in this investigation was based on: (1) application of temperature change at the perimeters of the pipes installed in the coarse grained soil layer near the interface between the fine grained and coarse grained soil layers; and, (2) application of suction as a boundary condition at the perforated parts of the pipes to decrease VWC and increase matric suction in the soil mass. Using this specific method, the results of the finite element analyses of the laboratory experiments and the two case studies demonstrated that the heated air flow through the coarse grained soil layer of a capillary barrier would improve its performance as a soil cover for a number of engineering applications. Comparisons of measured and calculated values of VWC and matric suction showed good agreement providing further proof of the validity of the method.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".