Fuel Tank Strap Fatigue Sensitivity Study under Fuel Level Variation and Payload Variation
Bibliographic record
Abstract
Fuel Tank Straps very often get different durability fatigue test results from different types of durability testing such as shaker table vibration, road test simulator (RTS) vehicle testing and proving ground vehicle durability testing. One test produces good durability results and other may indicate some durability risk. A special study was conducted to address this inconsistency. It was found that fuel level in the tank plays a big role in fuel tank strap durability. Higher fuel levels in a tank produce higher loads in straps and lower fatigue life. This paper will use a CAE fuel tank strap model and acquired proving ground strap load data to study fuel level influence in fuel tank strap durability. The fuel level study includes a full tank of fuel, 3 quarters tank of fuel, a half tank of fuel and one quarter tank of fuel. Based on CAE results of one 32 gallon fuel tank it is observed that fuel tank strap fatigue life improves by one order of magnitude or 10 times for every quarter tank of fuel level reduction. In addition to fuel level, payload influence is also studied. It is observed that vehicle payload has some positive influence. A full payload test condition can improve fuel tank strap fatigue life by 3 times compared with zero payload condition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".