A COMSOL-based numerical approach to improve heat-pulse measured frozen soil thermal properties
Bibliographic record
Abstract
• COMSOL modeling improves the accuracy of frozen soil thermal conductivity. • Phase change parameter optimization improves accuracy at −4 to 0 °C. • Numerical simulation overcomes the limitations of analytical solutions. • Heating strategy affects phase transition parameter selection. Heat pulse (HP) is the most widely used transient technique determining soil thermal properties (STPs) in unfrozen conditions, yet its application to frozen soils introduces significant challenges. At high subfreezing temperatures (−5 to 0 °C), the HP measurements induce thawing and refreezing of ice, dynamically altering the frozen soil thermal properties (FSTPs) being measured. Conventional analytical solutions fail to account for these phase change effects, leading to substantial errors in estimation. Although various approaches have been developed to improve FSTPs determination, achieving accurate measurements remain challenging. This study employed a COMSOL-based numerical model to solve heat conduction equations incorporating latent heat and compared the results with that obtained with traditional analytical solutions. The results revealed that analytical solutions consistently underestimate frozen soil thermal conductivity (FSTC) at temperatures above −3 °C, even with optimized heating strategies. Numerical simulations demonstrated that phase transition parameters critically influence temperature evolution, particularly above −5 °C, and the COMSOL improved FSTC estimates between −4 and 0 °C, though the performance depended on heating strategies. To facilitate parameter selection, linear regression models were derived for phase transition interval (Δ T p , R 2 = 0.37) and phase transition point ( T pc , R 2 = 0.30). These advancements enhance the accuracy of HP-measured FSTPs, providing a more reliable approach for cold-region researches and applications.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".