Modeling of Methane Flow through Nanopores: Insights from Molecular Dynamics Simulations
Bibliographic record
Abstract
Methane transport through nanoscale shale pores occurs under high Knudsen number conditions, where the velocity distribution deviates from the no-slip boundary assumption, making it challenging to determine the flow rate accurately. In this study, molecular dynamics (MD) simulations were performed to investigate the flow of methane within nanoscale quartz slits. The effects of pressure, pressure gradient, temperature, and pore width on methane transport and characteristic velocities (centerline and boundary velocities) were systematically examined to elucidate the mechanisms governing the boundary slip. The results show that pressure and pressure gradient determine the external force acting on methane molecules. The centerline velocity increases linearly with the applied force and scales with the square of the pore width. The boundary velocity exhibits a linear relationship with both the applied force and pore width under low-force conditions but becomes proportional to the square of the applied force and shows a nonlinear dependence on pore width at high forces. Temperature has a negligible effect on the centerline velocity but significantly enhances boundary velocity. Boundary slip originates from the collective motion of methane molecules, reflecting the combined influence of external forces, methane-wall interactions, and intermolecular forces. Finally, this study developed a model to predict the mass flow rate of methane transport through nanopores, which shows good agreement with MD simulation results and greater accuracy than the existing models.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".