Numerical Investigation on Buckling Response of Cylindrical Steel Storage Tanks Under Static Loading
Bibliographic record
Abstract
Cylindrical steel shells find extensive use in critical structures such as submarines, launch vehicles, and industrial facilities, highlighting the paramount importance of their stability performance.This study delves into the buckling response of these structures, considering various factors including imperfections, loading conditions, and material behavior.Specifically, we investigate the buckling behavior of both perfect steel models and those reinforced with carbon-fiber-reinforced polymers (CFRP) under axial and uniform external pressure.Employing linear and nonlinear analysis techniques using ABAQUS software, we evaluate the structural response and compare results with experimental data and theoretical predictions.Our findings demonstrate good agreement among finite element analysis (FEA), test results, and theoretical calculations.Moreover, we explore the influence of lay-up orientation on buckling resistance, highlighting the significance of fiber arrangement in CFRP reinforcement.Additionally, we analyse the deformation and stress distribution in cylindrical steel tanks subjected to hydrostatic pressure, revealing that maximum stresses occur at the bottom where liquid pressure is highest.These insights contribute to a deeper understanding of cylindrical steel shell behavior and have practical implications for enhancing their buckling capacity and structural integrity.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".