Evaluation of flow-induced vibration suppression performances of magneto-rheological damping pipe clamp using PID algorithm
Bibliographic record
Abstract
In order to suppress the low-frequency flow-induced vibration of fluid conveying pipeline, this paper develops a magnetorheological damping (MRD) pipe clamp due to its simple structure and strong dynamic adjustability. The mechanical dynamic model of MRD pipe clamp is established, and the vibration control algorithm based on PID is simulated. Comparison analysis on the vibration damping performance of the MRD pipe clamp under uncontrol, passive control, and PID control are conducted. The damping performances of MRD pipe clamp are tested under uncontrol, passive control, and PID control algorithm. The experiment results exhibited that the attenuation rate of each axial displacement and acceleration of pipe system using PID algorithm were more than 80% under sinusoidal excitation force of 600 N and excitation frequency of 2.5 Hz. The experiment results have verified the correctness of the simulation analysis in terms of value and trend. This research will provide the guide for the design and engineering application of MRD pipe clamp system.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".