Optimum gas tank locating in van vehicle – front and side crash analysis consideration for passenger safety
Bibliographic record
Abstract
In this research, crash test results from CNG locating method optimization approach for crashworthiness and testing its safety are presented. The locating process is based on principal energy considerations inspired from the current design process in passenger vehicle design development. The potential of the vehicle concept to absorb kinetic energy can be estimated at the very beginning of the design process by the free crash lengths in the different areas of the vehicle and estimates of average forces required in the specific segment and parts of the car body at particular crash phases. Based on the basic principle of vehicle crash analysis using the finite element method, a passenger VAN finite element model was selected to simulate the front and side rear collision test of the VAN, therefore the LS-DYNA software is adopted to calculate the deformation of the car and the acceleration time history curves during the crashing process; the anti-impact capability of the vehicle is evaluated from this simulation. It is important to determine appropriate force distributions and the corresponding loads paths through the whole structure for all relevant crash load in dedicated crash test cases. The results demonstrate that the improvement of local structure and location for the required CNG tanks in safe locations in vehicle chassis can promote the crashworthiness of the car, but the further improvement needs a major change of the vehicle structure. The outcomes are interpreted by using LS-PREPOST to analyze the energy absorption characteristics during crash for different cases at a velocity of 50km/h the duration of 12ms. The result analysis was necessary to derive distinct deformation phases characteristic and following that, the essential crash elements are compared with and without CNG tanks installation in each crash case. At last, the conclusion determines the proposed tank locating model in the selected passenger VAN is within the safe range of crash analysis standards.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".