A Reduced-Order Fluid Flow Model for Gas Injection into Porous Media: For Application in Carbon Sequestration in Mine Tailings
Bibliographic record
Abstract
One method to accelerate carbon sequestration within mine tailings from remote mines involves the injection of diesel generator exhaust into dry stack tailings. The techno-economic feasibility of this approach heavily depends on understanding the flow characteristics inside the perforated injection pipes embedded within the tailings. Two distinctive yet dynamically coupled transport phenomena were identified and evaluated: (i) gas transport inside the pipe and (ii) gas injection into the porous body of the tailings. This paper presents two models to investigate these transport phenomena, a three-dimensional (3D) and a one-plus-one-dimensional (1 + 1)D model. An experimental investigation of the pressure profile through the injection pipe was carried out to validate the models at the experimental scale. To apply the (1 + 1)D model to larger scales, the results were compared with those of the 3D model, as the (1 + 1)D model required significantly less computational resources and time. To include the effect of the perforations in the pipe on the pressure profile of the (1 + 1)D model, an analytical fluid velocity profile was developed in relation to geometric and physical parameters. The performance of the (1 + 1)D model with an impact factor was then evaluated against the 3D model results for the inlet pressure, pressure profile and gas outflow distribution under various conditions than those investigated experimentally. The developed (1 + 1)D model can be used to design an energy-efficient approach for large-scale implementation with a wide range of desired operating parameters.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".