Experimental study on the effect of defect curvature on the impactfracture behavior of structures using the real-virtual causticsmethod
Bibliographic record
Abstract
Defects have significant influence on the impact fracture behavior of structures. In this paper, the real-virtual caustics method is used to study the impact fracture behavior of structures with elliptical arc defects under impact loading of the drop hammer, and the impact loading process is simultaneously analyzed. The research results show that impact loading of the drop hammer in this experiment is a multi-period dynamic loading process, and the fracture of specimens under impact loading of the drop hammer is an energy-controlled process. The running crack initiates under impact loading and propagates toward the elliptical arc defect. After reaching the end of the elliptical arc defect, the running crack arrests and accumulates energy, and then it initiates again and propagates toward the loading position. The greater the end curvature of the elliptical arc defect, the shorter the time for the running crack to stagnate and accumulate energy at the defect end, and the earlier the time for the running crack to initiate again at the defect end, the smaller the impact loading stress of the drop hammer and the dynamic stress intensity factor of running crack tip when the running crack initiates again at the defect end.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".