Simplified CAE Model Technique to Predict Crush Performance of Identical Sized Passenger Vehicle Doors
Bibliographic record
Abstract
This paper highlights a simplified CAE model technique, which can simulate and predict door crush strength performance quickly. Such quick models can be used for DFSS and Design change studies. The proposed method suggests an equivalent sub model technique using only the door beam with tuned stiffness end springs to predict FMVSS214S full vehicle crush performance. Such models can be solved in minutes and hence very useful for DFSS studies during product design. The proposed method can be used to finalize door beam design for identical size of vehicle doors to meet required FMVSS214S crush performance. The paper highlights the door beam end springs tuning for identical size of cars and SUVs. Four vehicles were considered for the study. A single spring F-D (force -displacement) is tuned which correlated well for frond door of all the four vehicles. A separate unique spring F-D was needed which correlated well for rear door of all the 4 vehicles.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 0.001 |
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".