Numerical modeling of a dual crush mode welded aluminum crash structure
Bibliographic record
Abstract
The two main types of crush structures that are used to absorb energy during an automotive impact are the so-called axial crush and bending collapse structures. This paper presents results from a numerical study used to assess the crash performance of dual crush mode multi-gauge tailor-welded aluminum alloy crash structures that exhibit both modes of energy absorption. The design of these structures is based on testing of mono-gauge axial crush and s-rail structures, also presented in this paper. The axial crush structures were made from straight tubes, while the bending collapse structures (s-rails) consisted of tubes with two 45° bends that create an ȁsȁ shape. Numerical models were developed from axial crush structures with thinner tube walls welded to thicker gauge s-rails to create multi-gauge dual crush mode structures. A parametric study is presented in which the effects of the thickness ratio between the axial crush and the s-rail sections of the structure are investigated. Another parametric study is presented to examine the effects of varying cross sections of the s-rail section of the structure. Simulations of mono-gauge s-rail structures were used as a basis of comparison to assess the energy absorption performance of the multi-gauge dual crush mode structures. Dual crush mode structures are shown to lower the reaction load during impact and increase the crush displacement, compared to mono-gauge s-rail structures.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".