Multi-Scale Overlapping Domain Decomposition to Consider Elasto-Plastic Local Buckling Effects in the Analysis of Pipes
Bibliographic record
Abstract
Elevated pipelines are commonly encountered in petro-chemical and industrial applications. Within these applications, pipelines normally span hundreds of meters and are thus analyzed using one-dimensional (1D) beam-type finite elements when the global behavior of the pipeline is sought at a reasonably low computational cost. Standard beam-type elements, while computationaly economic, are based on the assumption of rigid cross-section. Thus, they are unable to capture the effects of cross-sectional localized deformations. Such effects can be captured through shell-type finite element models. For long pipelines, shell models become prohibitively expensive. Within this context, the present study formulates an efficient numerical modeling which effectively combines the efficiency of beam-type solutions while retaining the accuracy of shell-type solutions. An appealing feature of the model is that it is able to split the global analysis based on simple beam-type elements from the local analysis based on shell-type elements. This is achieved through domain-decomposition procedure within the framework of the Bridging multi-scale method of analysis. Solutions based on the present model are compared to those based on full shell-type analysis. The comparison demonstrates the accuracy and efficiency of the proposed method.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".