A Comprehensive Numerical Simulation Model for Non-Darcy Flow including Viscous, Inertial and Convective Contributions
Bibliographic record
Abstract
Abstract In this paper, a comprehensive numerical simulation model is introduced to resolve the problem of non-Darcy flow in porous media. As represented by this model, both pressure gradient and velocity profile predicted are based on the two viscous terms of Darcy and Brinkman, Forchheimer’s inertial term and Navier-Stokes’ convective term. At the point of departure from the Darcian domain to the non-Darcian domain it has been found that this model predicted the dimensionless term "Be" to be zero which agrees with Forchheimers model prediction. At 5% deviation from the Darcian flow the proposed model predicts "Be" to be 0.0756 as compared to 0.0526 predicted by Forchheimers model. The difference is due to higher flow velocity prediction by the proposed comprehensive model. The proposed model is expected to have wide applications in the field of reservoir simulation and fluid flow in porous media in both gas and oil reservoirs. Replacing the traditional model used to predict pressure gradient at any point of space and time by the proposed model would resolve the problem of inaccurate predictions associated with non-Darcy flow. This model predicts the correct pressure gradient and flow velocity regardless of the source of deviation. The model is continuous in Darcian regime as well as non-Darcian regime.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".