Sensitivity analysis of Kozeny-Carman and Ergun equations
Bibliographic record
Abstract
In the article a sensitivity analysis of linear and nonlinear terms in the Kozeny-Carman and Ergun equations was shown. In the first case the impact of the porosity, tortuosity, specific surface of the porous body and the model constant was investigated. In the second case the porosity, the particle diameter and the sphericity function were taken into account. To express the model sensitivity by numbers, an earlier developed method was used. In this way the order and the importance of the impact of individual parameters was determined. The motivations to create this article were questions, which occurred during developing a novel investigation method, linking the Discrete ElementMethod and the CFD techniques. The first aim was to predict what will happen, if individual parameters will be set with an error: which data should be set as accurately as possible and which data are not very important for the result value. The second intention was to state which of parameters used in porous media investigations should be expressed by functions and which by constant values. The article may be treated as set of pointers helping in using of Kozeny-Carman and Ergun laws or as an example of research methodology based on the sensitivity analysis.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".