CAE Based Development of an Ejection Mitigation (FMVSS 226) SABIC using Design for Six Sigma (DFSS) Approach
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">NHTSA issued the FMVSS 226 ruling in 2011. It established test procedures to evaluate countermeasures that can minimize the likelihood of a complete or partial ejection of vehicle occupants through the side windows during rollover or side impact events. One of the countermeasures that may be used for compliance of this safety ruling is the Side Airbag Inflatable Curtain (SABIC). This paper discusses how three key phases of the optimization strategy in the Design for Six Sigma (DFSS), namely, Identify; Optimize and Verify (I_OV), were implemented in CAE to develop an optimized concept SABIC with respect to the FMVSS 226 test requirements. The simulated SABIC is intended for a generic SUV and potentially also for a generic Truck type vehicle. The improved performance included: minimization of the test results variability and the optimization of the ejection mitigation performance of the SABIC.</div><div class="htmlview paragraph">Results from this study show that for generic SUV and Truck type vehicles with hardware similar to that tested in this study; <ul class="list disc"><li class="list-item"><div class="htmlview paragraph">Beltline overlap, loft size and pressure account for 75% of the variability in performance of an ejection mitigation SABIC. These metrics may therefore be considered critical to the design of the SABIC.</div></li><li class="list-item"><div class="htmlview paragraph">The identified key metrics may be used to define nominal ejection mitigation SABIC design best practice for the generic SUV and Truck.</div></li><li class="list-item"><div class="htmlview paragraph">An associated investigation during this study showed that a SABIC may shrink significantly when fully inflated. This shrinkage has the potential to move SABIC lofts away from the intended impact location.</div></li><li class="list-item"><div class="htmlview paragraph">The CAE based DFSS process has the potential to significantly reduce SABIC development time and may potentially enhance product speed to market using virtual tool versus empirically based SABIC development approach.</div></li></ul></div></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".