A numerical approach for determining the resistance of fine mesh filters
Bibliographic record
Abstract
Characterizing the resistance of mesh filters, in terms of pressure drop as a function of flow velocity, is an important part of modeling any filtration process. Most commonly, filters are characterized experimentally, which can be costly and time-consuming. This motivates a generalized numerical approach for characterizing the resistance of mesh filters based on the flow through a representative segment of a filter. There is uncertainty, however, in the correct specification of boundary conditions such that the numerical results for flow through the small segment match the overall behaviour of the filter. In this work, an experimentally validated numerical approach is developed by examining the velocity and turbulence intensity experienced across the filter. It has been shown that the flow resistance results are not sensitive to the turbulence intensity, but depend greatly on the imposed flow velocity. Specifying the peak velocity as the boundary condition in the filter simulations resulted in a good match with experiments, while using the bulk velocity did not reproduce the experimental results.
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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".