Investigation on spontaneous liquid–liquid imbibition in capillaries with varying axial geometries using lattice Boltzmann method
Bibliographic record
Abstract
Spontaneous liquid–liquid imbibition in capillaries with irregular axial geometries is common in the petroleum industry. Monitoring the real-time dynamic contact angle (DCA) of the meniscus is crucial during such processes. In this work, we extend the Bell–Cameron–Lucas–Washburn (BCLW) equation by considering the axial shape of the capillaries, inertial force, and non-wetting fluid viscosity. We also develop a cascaded multi-component Shan–Chen lattice Boltzmann method (CLBM) with a modified mass-conservative curved boundary scheme to accurately simulate imbibition processes in sinusoidal capillaries. The results indicate that the DCA is highly sensitive to variations in the axial geometry of the capillary during imbibition, displaying a periodic time evolution pattern. When the axial geometry diverges, the DCA increases, and when it converges, the DCA decreases. The viscosity ratio affects the imbibition velocity, controlling the evolution period and extreme values of the DCA. A critical contact angle exists for a fixed capillary axial geometry and viscosity ratio. Continuous spontaneous imbibition occurs if the static contact angle is smaller than this critical value. However, if it exceeds this threshold, imbibition ceases within regions where axial geometry divergence. Moreover, we noticed a discrepancy in imbibition lengths predicted by the extended BCLW equation that ignores the DCA compared to those computed through the CLBM. To address this issue, we employed CLBM to monitor the DCA in real time and used the gathered data to refine the extended BCLW equation. As a result, the prediction of imbibition lengths by the extended BCLW equation for coupling the DCA became more accurate.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".