Numerical Modeling of Rear Subframe Under Different Loading Conditions
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">In this paper, finite element methods are used to analyze the rear subframe for Chevrolet Malibu. Plasticity based material model along with dynamic and static analysis is used. Commercial software LS-DYNA is used to model the subframe. Half model for the subframe is used with the corresponding boundary conditions for our simulations. A material model based on power law is used to account for the material behavior in all simulations. Different loading conditions are used to analyze the subframe under normal driving conditions while the crash results are used to analyze the subframe under vehicle crash. This data is used to compare the performance and safety of the original stock car. A parametric study is also conducted to analyze the effect of material response by changing the material hardening properties. Results show that 1018 mild steel is the most suitable material under crash and normal loading conditions.</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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".