Fluid structure interaction (FSI) in piston diaphragm pumps
Bibliographic record
Abstract
Piston diaphragm pumps are used worldwide to transport abrasive and/or aggressive slurries against high discharge pressures in the mining, mineral processing, and power industries. The limitation of the strain levels in the elastomer of the diaphragm is of utmost importance for eliminating fatigue failures of the diaphragm and thereby obtaining a high reliability of the piston diaphragm pump. The actual strain levels in the diaphragm are the result of a complex fluid structure interaction mechanism within the pump chamber. Understanding of this fluid structure interaction mechanism has improved in the last decades but is still limited. This paper first describes a detailed dimensional analysis of the fluid structure interaction mechanism and shows how it has been used to evaluate field experiences and how it is currently being used within robust design and selection rules for piston diaphragm pumps. Next, the paper describes the development of a numerical model for modelling the complex fluid structure interaction mechanism which enables the prediction of the resulting diaphragm deformation and strain levels. A novel combination of different immersed boundary approaches is used for modelling the fluid structure interaction phenomena. Furthermore an experimental setup is described whose results are used to validate the results of the numerical model. Some preliminary results of the numerical model and the experiments are shown.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".