Complete Edge Smooth Finite Interpolation Method for Limit Upper Limit Analysis of Axisymmetric Structures
Bibliographic record
Abstract
Axisymmetric structures have applications in various fields such as engineering and architecture. This type of structure exhibits complex stress distribution and failure modes when subjected to ultimate loads, and the importance of the analysis method for its upper limit in the engineering field is self-evident. Its upper limit analysis is mainly used to evaluate the stability and load-bearing capacity of axisymmetric structures under load. The intention of the fully edge smooth finite interpolation method is to improve the accuracy and efficiency of analysis. It improves the interpolation function to maintain smoothness at the boundary, and uses adaptive mesh partitioning and local refinement to make the analysis more accurate and efficient. Therefore, the purpose of this article's in-depth study on the perfect edge smooth finite interpolation method is to introduce smooth functions and finite interpolation techniques, accurately simulate structures, avoid risks, and reduce losses. This article mainly applies numerical simulation and experimental comparison to compare the axisymmetric structures of thick walled cylinders, frustums, and spheres. The ultimate load of each structure is obtained by changing the distortion coefficient and radius ratio. The experimental results show that in cylindrical testing, the performance difference between the two methods is greatest when the radius ratio is 3.5. The error between the perfectly smooth finite interpolation method and the analytical value is smaller, while the direct iteration method has a larger error.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".