A Procedure for Performance Experimental Analysis of a Globe Control Valve
Bibliographic record
Abstract
Control valves are known as the final control element in hydraulic closed/open loops of modern process industries around the world. Proper selection of control valve leads to enhanced performance curve of the hydraulic systems and therefore increases the efficiency, reliability, profitability and safety of the system. Flow coefficient (CV) of a control valve describes the relation between the pressure drop across the valve and the flow passing through it. Despite many computational efforts for calculating the exact value and curve of CV, the experimental procedure of the CV test has not been documented well. We used a control valve test-set up designed based on the standards ANSI/ISA-75.02-1996 and IEC 60534-2-3 (2013) to evaluate the performance of a 3 in. control valve. Upon extracting the results in terms of inlet, and outlet pressure and flow, the characteristic parameters such as CV and opening percentage were derived and compared with an ideal curve. Error analysis was performed to account for the tolerance of the measured parameters by the measuring devices. The results show acceptable agreement within the criteria of a reference standard approving the validity of the design method.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.014 | 0.004 |
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".