Exploring stress intensity factor computation: A parametric study using extended isogeometric analysis (XIGA)
Bibliographic record
Abstract
The permanence and durability of mechanical and structural elements with discontinuities such as cracks and voids require the calculation of SIF (stress intensity factors) with reasonable fidelity. SIF is a crucial parameter that predicts crack growth and failure behavior by quantifying the stress field neighboring the crack tip. Therefore, understanding the sophisticated characteristics of the stress fields in the vicinity of discontinuity requires an effective way of calculating SIFs. Currently, there are numerous methods to calculate SIF, such as FVM (Finite Volume Method), FEM (Finite Element Method), BEM (Boundary Element Method), XFEM (Extended Finite Element Method), Phase field method and Meshfree methods. For an extended period, FEM is one of the leading methods in solving fracture mechanics problems. Though FEM is quite robust in dealing with several engineering problems, it has got its inherent drawbacks to deal with singular fields like discontinuities. Hence to reasonably capture moving discontinuities, finer meshes near the discontinuous field are required that demand more computation effort and time. To alleviate the above drawback of FEM, this study employed Extended Isogeometric Analysis (XIGA) to efficiently and effectively determine the SIFs in the case of fissured plates as benchmarking fissure problems. In this study SIFs in relation to crack length were examined for edge and center cracked plates and results were compared with the theoretical values.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".