Investigation of Numerical Methods for Modal Analysis of a Tube Bundle With Fluid-Structure Interaction
Bibliographic record
Abstract
Seismic analysis of tube bundle is of paramount importance in the safety assessment of nuclear installations. These analyses require in particular the calculation of frequency, mode shape and effective mass of the system eigenmodes. As fluid-structure interaction effects can significantly affect dynamic behaviour of immersed structures, the numerical modeling of the tube bundle has to take into account FSI. A complete modeling of heat exchangers (including pressure vessel, tubes and fluid) is not accessible to the engineer for industrial design studies. In the past decades, homogenization methods have been studies and developed in order to model tubes and fluid through an equivalent continuous media, thus avoiding the tedious task to mesh all structure and fluid sub-domains within the tube bundle. Few of these methods have nonetheless been implemented in industrial finite element codes. In previous papers (Sigrist & Broc, Pressure Vessel and Piping, Vancouver, July 2006), a homogenization method has been developed and applied to an industrial case for the modal analysis of a nuclear rector with internal structures and coupling effects modeling. The present paper aims at investigating the application of the proposed method for the dynamic analysis of tube bundle. The homogenization method is compared with direct and indirect fluid-structure coupled methods for the calculation of eigenmode frequencies, shapes and modal masses.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".