Valve Characteristics and Their Effect on Transient Surge Pressures in Delivery Terminals
Bibliographic record
Abstract
A hydraulic transient occurs when there is a sudden change in the steady state condition of a fluid in a pipeline. The rapid change in velocity of a fluid causes a pressure wave to travel along the pipeline, potentially causing damage to the equipment and piping. Usually, different scenarios are studied depending on whether the fluid is being injected or delivered from the pipeline into a tank. The most commonly studied causes of sudden fluid velocity change in a pipeline are: closing of a fast acting valve (ESD or control valves which close within seconds) and stopping or starting of pumps. When a pipeline is delivering fluid into a tank, the closure of a valve can completely block the flow and create transient surge pressures that exceed acceptable pressure limits, and require mitigation. Although the closure of a fast acting valve is a commonly analyzed scenario, the closure of a motor operated valve (MOV), which is less commonly analyzed, can also create surge pressures which can put the pipeline at risk. There are three characteristics of an MOV that can significantly impact the surge pressures it creates when it is closed. These valve characteristics are: flow coefficient, valve curve and stroke time. Hydraulic simulations were performed to study the effect of these three valve characteristics on transient responses when delivering from pipelines into tanks. Simulation results show that a faster stroke time leads to higher pressure surges, as well as a valve with a quick closing or linear curve. However, the flow coefficient of the valves will have varying effects on transients depending on the piping system being analyzed. The purpose of this paper is to not only highlight the importance of valve characteristics when modeling transient surge events but also to provide key learnings that can be used to design safer delivery terminals.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".