Analyzing joint efficiency in storage tanks: A comparative study of API 650 standard and API 579 using finite element analysis for enhanced reliability
Bibliographic record
Abstract
This study compares two renowned methodologies, API 650 and API 579, focusing on the analysis of joint efficiency at a value of 0.7. Using Finite Element Analysis (FEA), the research suggests that a 35 % increase in filling height might be achievable for a large tank that adhere to the stability criteria outlined by API 650. To support these findings, 337 simulations rigorously examined various parameters. These encompass the design factor (β), bottom constraint, geometric configuration, mesh size, and a newly introduced Local ASME criterion. The latter is specifically introduced to evaluate protection against plastic collapse for Maximum Fill Height (MFH). Additionally, the study advocates elevating the joint efficiency from 0.7 to a range of 0.8–0.87 in API 653. This recommendation is pertinent to storage tanks that are not susceptible to buckling failure mode and possess limited documentation. The outcomes of this research provide significant insights into tank design and have the potential to refine industry standards and practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".