Electro‐Thermal Subsurface Gas Generation and Transport: Model Validation and Implications
Bibliographic record
Abstract
Abstract Gas generation and flow in soil is relevant to applications such as the fate of leaking geologically sequestered carbon dioxide, natural releases of methane from peat and marine sediments, and numerous electro‐thermal remediation technologies for contaminated sites, such as electrical resistance heating. While traditional multiphase flow models generally perform poorly in describing unstable gas flow phenomena in soil, Macroscopic Invasion Percolation (MIP) models can reproduce key features of its behavior. When coupled with continuum heat and mass transport models, MIP has the potential to simulate complex subsurface scenarios. However, coupled MIP‐continuum models have not yet been validated against experimental data and lack key mechanisms required for electro‐thermal scenarios. Therefore, the purpose of this study was to (a) incorporate mechanisms required for steam generation and flow into an existing MIP‐continuum model (ET‐MIP), (b) validate ET‐MIP against an experimental lab‐scale electrical resistance heating study, and (c) investigate the sensitivity of water boiling and gas (steam) transport to key parameters. Water boiling plateaus (i.e., latent heat), heat recirculation within steam clusters, and steam collapse (i.e., condensation) mechanisms were added to ET‐MIP. ET‐MIP closely matched observed transient gas saturation distributions, measurements of electrical current, and temperature distributions. Heat recirculation and cluster collapse were identified as the key mechanisms required to describe gas flow dynamics using a MIP algorithm. Sensitivity analysis revealed that gas generation rates and transport distances, particularly through regions of cold water, are sensitive to the presence of dissolved gases.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".