Evaluating resistance coefficients of straight‐through diaphragm control valves
Bibliographic record
Abstract
Abstract Despite the extensive use of straight‐through diaphragm valves in many diverse industrial applications, very few studies of frictional pressure loss for straight‐through diaphragm valves have been reported in the open literature. The few that are available are for fully opened valves based on the tacit assumption that different sized diaphragm valves are geometrically similar. In this study, the pressure drops across five straight‐through diaphragm valves were measured in four aperture positions. Resistance coefficients were determined by experimentally establishing pressure gradients upstream and downstream of the valves. The experiments were carried out using Newtonian and non‐Newtonian fluids over a wide range of Reynolds numbers with the emphasis on obtaining laminar flow data. The Hooper 2‐k correlation was corroborated and found to be valid at Reynolds numbers <10 for straight‐through diaphragm valves. At higher Reynolds numbers the resistance coefficient is shown to be dependent on the size and the opening of the valve. Three different approaches (domain separation, simple summation, and selective combination) using a two‐constant model to predict the experimental resistance coefficients were explored. Comparison of the correlations with experimental data and existing models has shown that the simple summation two‐constant model approach has substantial merit. Considering the complexities of accounting for the valve size, the valve opening position, the type of fluid over a Reynolds number range of 0.1–100 000, this model gives pipeline design engineers a simple, semi‐empirical correlation for the estimation of resistance coefficients of straight‐through diaphragm control valves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".