Advancements in FIV-based flow measurement: full-scale experimental and LES-SSI modeling approaches for buried pipelines
Bibliographic record
Abstract
Flow Induced Vibration (FIV)-based measurement offers an economical, non-invasive, and readily implementable approach for monitoring pipeline flow rate and safety. Investigations into FIV-based flow measurement have, however, only been performed for above ground pipelines. The FIV characteristics of buried pipelines under various influence factors remain largely unexplored, potentially introducing significant errors in measurement outcomes. To address these gaps, this study conducts both full-scale experiments and numerical simulations of buried steel pipeline FIV. Experiments are performed characterizing FIV by varying Reynolds number ( Re d ) and Depth of Cover ( DOC ) in proctor compacted soil. A Large-Eddy Simulation coupled with Soil-Structure Interaction (LES-SSI) modeling framework is used to simulate buried steel pipeline FIV. The LES-SSI model is validated through convergence tests and verified against both experimental and benchmark data. The study thoroughly investigates the relationship between FIV and various parameters. Subsequently, an optimal quantity equation for buried steel pipeline flow measurement is developed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".