Thermal Protection of a Ground Layer With Phase Change Materials
Bibliographic record
Abstract
Conventional ground surface insulation can be used to protect power line foundations in permafrost regions from the adverse effects of seasonal freezing and thawing cycles. But previous studies have shown ineffective thermal protection against the receding permafrost with conventional insulation. In this paper, an alternative thermal protection method (phase change materials (PCMs)) is analyzed and studied experimentally. Seasonal ground temperature variations are estimated by an analytical conduction model, with a sinusoidal ground surface temperature variation. A compensation function is introduced to predict temperature variations in the foundation, when the ground surface reaches a certain temperature profile. Measured data are acquired from an experimental test cell to simulate the tower foundation. With thermal energy storage in the PCM layer, the surface temperature of the soil was modified, leading to changes in temperature in the foundation. Measured temperature data show that the PCM thermal barrier effectively reduces the temperature variation amplitude in the foundation, thereby alleviating the seasonal freezing and thawing cycles. Different thermal effects of the PCM thermal barrier were obtained under different air temperature conditions. These are analyzed via melting degree hours and freezing degree hours, compared with a critical number of degree hours.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".