Semi-empirical pressure loss model for viscous flow through high aspect ratio rectangular orifices
Bibliographic record
Abstract
A predictive model is developed for the pressure loss coefficient for a viscous flow through a rectangular orifice on a pipe-installed thick plate. The model is developed based on the 1-dimensional Navier-Stokes equation and an asymptotic increase in velocity modeled to have a direct relation with the flow convergence in the near-inlet region. Here, the flow velocity increases asymptotically from the steady mean upstream value to the orifice velocity. This phenomenon is represented by a convergence parameter, ϕ, used in the velocity transition model to quantify the length of the convergence zone. The static pressure drop is measured experimentally for varying orifice aspect ratio, AR, at creeping Reynolds numbers (0.01 ≤ Re ≤ 0.1). A significantly wider range of AR is covered (1 ≤ AR ≤ 250), compared to related works in the literature. Results show that the relative dominance of the convergence phenomenon is affected by AR. The maximum length of convergence is for the square orifice (AR = 1), as the flow experiences comparable convergence from all directions, whereas for higher AR, convergence becomes less dominant in one of the two midplanes of investigation. The loss coefficient thus decreases as AR increases. At constant Re, higher AR generally leads to higher pressure drop but lower values of the loss coefficient. The velocity gradient in the convergence zone is also determined as a function of AR and Re which verifies that lower AR takes a longer distance for the velocity transition due to increased convergence.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".