A new model for predicting thermal conductivity of unsaturated soils using the soil-water characteristic curve
Bibliographic record
Abstract
• Introduced a novel model for predicting thermal conductivity of unsaturated soils. • Utilized the Soil-Water Characteristic Curve (SWCC) for providing modeling foundation. • Model incorporates two parameters linked to soil pore size distribution. • Validated the model with 30 soil samples, showing strong predictive accuracy. Heat and mass transfer processes in porous media, such as soils, strongly depend on thermal conductivity. In contrast to homogeneous materials, the thermal conductivity of soils, especially when they are unsaturated, is highly complex due to the intricate interactions among solid, water, and air phases. Water saturation is one of the most important factors influencing the thermal conductivity. Current models for predicting thermal conductivity, whether empirical, based on mixing theories, or grounded in percolation theory frequently exhibit limitations under varied environmental conditions. To address these challenges, in this study a new model is developed for predicting the thermal conductivity of unsaturated soils, utilizing the Soil-Water Characteristic Curve (SWCC) as a fundamental tool. The proposed approach explicitly links pore-scale thermal conductivity to pore size distribution, subsequently upscaling this relationship to predict normalized thermal conductivity at the macroscale. The model incorporates two parameters, n 1 and η , both of which are strongly related to the pore size distribution. The parameter n 1 is derived from the SWCC while an empirical correlation is suggested between n 1 and η , facilitating practical implementation. The model’s accuracy is validated against a wide range of experimental datasets, demonstrating reliable prediction performance across various soil types and temperature conditions. This model can be effectively used in thermo-hydro-mechanical (THM) coupled modeling for unsaturated soils.
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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) | 0.001 | 0.000 |
| 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) | 0.001 | 0.001 |
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".