Modeling multiphase flow with a hybrid model based on the Pore-network and the lattice Boltzmann method
Bibliographic record
Abstract
Lattice Boltzmann method (LBM) simulations provide an excellent description of two-phase flow through porous media. However, such simulations require a significant computation time. In order to optimize the computation resources, we propose a hybrid model that combines the efficiency of the pore-network approach and the accuracy of the lattice Boltzmann method at the pore scale. The hybrid model is based on the decomposition of the granular assembly into small subsets, in which LBM simulations are performed to determine the main hydrostatic properties (entry capillary pressure, capillary pressure - liquid content relationship and liquid morphology for each pore throat). A primary drainage of a random packing of spheres is presented and contrasted to the results of the same problem fully resolved by the LBM. Liquid morphology and invasion paths are correctly reproduced by the hybrid method.
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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".