Numerical and Experimental Modeling of Non-Darcy Flow in Porous Media
Bibliographic record
Abstract
Abstract A numerical simulation of the newly derived Forchheimer's diffusivity equation has been achieved, careful selection of the non-Darcy coefficient (β) and a fruitful discussion on the use of this coefficient has been presented. Although the proposed model has been designed for single phase flow in porous media, with minor modification it can be applied to multi-phase flow cases. Results show that the proposed numerical model is valid for single-phase flow and is functioning in all ranges of flow in porous medium. The point at which the flow breaks from the Darcian trend has been determined and a new dimensionless term, "Be", has been suggested to define the point of deviating from Darcian flow. "Be" has been found to be 0.0526 at 5% deviation from the Darcian behavior. An experimental model has been designed to examine certain critical parameters experimentally for the purpose of comparing results with the numerical model predictions. A horizontal fracture has been induced to a homogeneous synthetic sample in the direction of flow with changing aperture to study the effect of fractures on flow behavior. Experimental results have been compared to the numerical model predictions; a satisfactory agreement within the domain of testing has been concluded which in turn encourages the implementation of this model on a field scale. Unlike other approaches, the non-Darcy coefficient "β" has been determined experimentally which reflects the authors' belief that when possible, "β" should be evaluated based on the same set of data used to determine permeability in the laboratory.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".