Nonlinear buckling optimization technique to predict critical imperfection wavelength of combined liquid-filled steel conical tanks
Bibliographic record
Abstract
The shape of the imperfection induced by welding has an influence on the buckling resistance of thin shell structures, and many previous studies have come up with various models to estimate the critical imperfection shape. The aim of the current study is to assess the adequacy of three different approaches available in the literature, which consider that the imperfection wavelength matching the first buckling mode of a perfect tank to be the critical one. The first approach is based on buckling formulae calculated using a linear eigenvalue analysis performed on extensive experimental results of buckling of conical shells. The second approach assumes the critical wavelength, in view of the buckling mode profile detected from finite element analysis, as the distance between the inflection points of the elastic curve of the first buckling mode of a perfect tank. The third approach estimates the critical wavelength as double the distance between maximum and minimum points of the elastic curve. To determine the optimum wavelength that would lead to the minimum buckling capacity of the tank, the current study is conducted numerically by coupling a nonlinear finite element model, developed in house, and a direct search optimization technique. The results obtained from this numerical tool show good agreement with the first and the second approaches, which proves the adequacy of these two approaches in estimating the critical wavelength of the governing buckling mode, while the third approach yields a wavelength that overestimates the buckling capacity of the tank.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".