Strong fluid–solid interactions with segregated CFD solvers
Bibliographic record
Abstract
Purpose This paper aims to present a fluid-structure coupling partitioned scheme involving rigid bodies supported by spring-damper systems. This scheme can be used with already existing fluid flow solvers without the need to modify them. Design/methodology/approach The scheme is based on a modified Broyden method. It solves the equations of solid body motion in which the external forces coming from the flow are provided by a segregated flow solver used as a black box. The whole scheme is implicit. Findings The proposed partitioned method is stable even in the ultimate case of very strong fluid–solid interactions involving a massless cylinder oscillating with no structural damping. The overhead associated with the coupling scheme represents an execution time increase by a factor of about 2 to 5, depending on the context. The scheme also has the advantage of being able to incorporate turbulence modeling directly through the flow solver. It has been tested successfully with URANS simulations without wall law, thus involving thin high aspect-ratio cells near the wall. Originality/value Such problems are known to be very difficult to solve and previous studies usually rely on monolithic approaches. To the authors' knowledge, this is the first time a partitioned scheme is used to solve fluid–solid interactions involving massless components.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".