Shape Recovery Simulation of Flexible Airdam
Bibliographic record
Abstract
Airdam is an aerodynamic component in automobile and is designed to reduce the drag and increase fuel efficiency. It is also an important styling component. The front airdam below the bumper is to direct the air flow away from the front tires and towards the underbody, where the drag coefficient becomes less. The flexible airdam is made of Santoprene™ - thermoplastic vulcanizates (TPV), which belongs to thermoplastic elastomer (TPE) family. When a vehicle is parked over a parking block, the flexible airdam will be under strain subjected to bending load from the parking block. If the airdam is kept under constant strain for a certain period, a set will occur and the force will decay over a period of time. Due to the force decay, the stress will reduce and this behavior is called as stress relaxation. Once the airdam is removed from the block, it will be in its deformed position and try to recover its original shape which will affect the fuel efficiency and appearance of the vehicle, until it recovers the original shape. This paper explains the material behavior (hyperelastic and viscoelastic) of TPV and its representative material model in Abaqus. This is followed by a simple feasibility study done on a rectangular specimen to verify the force decay over a period of time for TPV followed by comparison of CAE simulation to physical test results. Finally explains about the flexible airdam parking simulation methods and the shape recovery of airdam over a period of time. The prediction of CAE to the lab testing performed on actual airdam results is provided.
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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 | 0.000 | 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) | 0.005 | 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".