Calibrating the multiple orifice mathematical model using physical scale model foam at low Reynolds number
Bibliographic record
Abstract
Recently, gelcast ceramic foams are being considered as potential diesel particulate filter substrates. Consequently, a mathematical model known as the Multiple Orifice Mathematical (MOM) model for the study of fluid flow and the determination of pressure gradients across the foam filters was developed and calibrated by some researchers. However, there was need to establish the model application on a wider range of pore sizes of the foam filters. Hence, this work is to establish the dynamic similarity of the physical scale model used for the calibration and the ceramic foams. Following the conceptual model employed in the development of the MOM model, generic physical scale foam models and a fluid flow rig was fabricated. The pressure drops across the generic physical model foam obtained from experiments over different ranges of low Reynolds number were graph-fitted against the MOM model to determine the kinetic correction factors. The values for the kinetic correction coefficient determined from the generic physical model at low Reynolds number is within the range obtained by other researchers in the calibration of the MOM model, which implies that the MOM model can be applied to a wide range of pore sizes found in gelcast ceramic foam filters. Key words: Diesel particulate trap, gelcast ceramic foam, kinetic correction coefficient, generic foams, foam filters, pressure gradients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".