Numerical Modeling of Heat and Water Transport with Heat Exchange in Unsaturated-Saturated Porous Media Including Heat Influence on Flow
Bibliographic record
Abstract
We discuss the numerical modelling of heat transport with contaminated water into unsaturated-saturated porous media.We focus on the determination of a heat exchange with the matrix of the porous media and adsorption of a contaminant.Numerical modelling includes the influence of temperature and adsorbed contaminant on hydraulic permeability.We also discuss the water volume extension due to the temperature change.This is motivated by hydrothermal isolation properties of building facades under the influence of external weather conditions.Also, contaminant dissolved in the water could be interpreted, e.g., as the salt which can degrade the quality of concrete and other building elements.Dependence of hydraulic permeability on the temperature, concentration of a contaminant, amount of adsorbed contaminant and saturation is discussed on the base of van Genuchten empirical law for unsaturated porous media.An efficient numerical approximation is proposed by means of which we solve the direct and inverse problems of the complex model.The determination of the model parameters we solve by inverse methods.In our numerical experiments, we show the significant sensitivity of hydraulic permeability on temperature and adsorption and the weak sensitivity on water extensions in the physically relevant temperature change.For the solution of inverse problems in laboratory experiments, we suggest the 3D sample in a cylindrical form which enables many experimental scenarios created by suitable boundary conditions.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".