Bending Response and Energy Absorption of Closed-Hat-Section Beams
Bibliographic record
Abstract
Many articles on bending collapse but not limited to closed-hat-section beams have been reported mainly from experimental point of view but less in simulation-based approach. Detailed investigation on critical parameters of closed-hat-section beams to examine their energy absorption capability is also less found in the literature. This paper presents the procedure for development and validation of a finite element (FE) model of a closed-hat-section beam under quasi static three-point bending using an explicit nonlinear FE technique. Developed FE models were validated through comparison with existing and present experiment results. Firstly, the existing models were rebulit via present modeling technique using informations provided in the relevant research report. Simulation results of rebuilt model were compared with existing results for verification and validation. Next, to further validate the present model, actual physical experiment replicating the FE model was set up for comparison of results. Validated models were then used in parametric studies in order to investigate the effect of some critical parameters such as plate thickness, flange and web width, and foam filler. Results show that the wall thickness, web width, and filler have direct effect on bending stiffness. Foam filling indicated significant enhancement on the crush and energy absorption of closed-hat-section beams. This study provides detail procedures and research information which will facilitate improvisation of current design as well as the design of foam filled closed-hat-section beams as energy absorbers in impact applications.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".