Easy to build, modular and large scale pipe conveying fluid experimental setup
Bibliographic record
Abstract
The pipe conveying fluid is a classic fluid structure interaction experiment. First studied for industrial applications such as liners and pipelines, it became a "paradigm" of non-linear dynamics in the same way as the vertical rotating shaft. Hundreds of papers studying different pipe instabilities and different phenomena with various numerical and analytical methods have been published in the last decades. However, many studies lack the comparison with experimental data to validate the analytical models and numerical simulations. Indeed, designing and building a pipe conveying fluid experimental setup can prove to be a long and a burdensome process. This paper presents an easy to build pipe conveying fluid experimental setup built in the LM2 laboratory at Polytechnique Montréal. Fig. 1 presents the global architecture of this experimental rig. This large scale setup uses relatively high speed cameras to track the pipe in three dimensions. It does not require heavy construction or major plumbing and electrical work. Moreover, it is removable and can be modified easily to observe different phenomena with various large scale pipes or boundary conditions. Lastly, it is relatively inexpensive as it costs less than 20 000 US dollars including all the sensors and acquisition systems.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".