Impact of low‐temperature electrical resistance heating on subsurface flow and transport
Bibliographic record
Abstract
The effects of subboiling electrical resistance heating (ERH) on subsurface flow and transport were examined in a series of two‐dimensional tank experiments, with temperatures reaching 50°C. To analyze the experiments and determine the dominant mechanisms affecting flow and transport, a fully coupled two‐dimensional finite difference electrothermal model was developed to simulate electrical current flow, temperature‐dependent fluid flow, and mass transport. The model incorporates temperature‐dependent equations for density, viscosity, diffusion coefficient, and electrical conductivity, capturing the nonisothermal processes dominant in the subsurface. The model was validated with laboratory‐scale experiments in which voltage distribution, instantaneous electrical power, temperature, and tracer transport were measured. Tracer experiments and transport modeling indicated that temperature‐induced buoyant flow and contaminant movement could be significant when applying ERH in the subsurface, even at 50°C. A sensitivity study was performed to assess the impact of including temperature‐dependent properties such as water density, viscosity, and electrical conductivity. A change in water density of 1.3% (at 50°C) resulted in buoyant flow and increased velocity through the heated zone, indicating that heating contaminated zones to 50°C can have a large impact on mass transport. Temperature‐dependent electrical conductivity had a direct impact on ERH power consumption as well as the time to reach desired temperatures.
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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) | 0.000 | 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".