Making a Smart Pressure Reducing Valve for Reducing Pressure Fluctuation Caused by Water Hammer in Water Distribution Network Using Input-Output Feedback Linearization
Bibliographic record
Abstract
Using pressure reducing valves to reduce the pressure in water distribution systems to the minimum of required value is one of the most effective ways for leakage reduction. Valve opening/closing, switching a pump on/off and water consumption fluctuation by a large consumer cause transient flows. Interference between transient flow and a PRV with constant needle-valve setting may intensify pressure waves in WDS. Applying smart PRVs can limitpressure fluctuation. In this research, Input-output feedback linearization method has been used for smart PRV control. The results of this method have been compared with PRV with CNVS and proportional-integral-derivative controller. A theoretical network taken from references was used to evaluate the proposed methods. Network demands include normal consumers and an industrial large consumer. Water hammer caused by consumption variations, PRV with CNVS operation, IOFL method and PID controllers were modeled in Simulink. PRV outlet head fluctuation in PRV with CNVS is 18 to 28 m in PID controller and in IOFL method are 26 to 28 m. Also, the results showed that the IOFL method has smoother and less fluctuation than PID. Root-mean-square error for PRV outlet head in CNVS, PID controller and IOFL is 1.6, 0.32 and 0.28, respectively. Therefore, IOFL method has less error and better performance than PID. Also, this method has simpler computational operations by converting nonlinear system equations to linear equations.
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 | 0.000 | 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) | 0.001 | 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".