Structural health monitoring of type 4 composite fuel tank based on correlation between ultrasonic attenuation and crack density
Bibliographic record
Abstract
As the number of FCEV vehicles increases, the importance of safe hydrogen fuel storage becomes larger. Hydrogen energy needs a high-pressure composite pressure vessel called a type 4 fuel tank. The type 4 fuel tank consists of a plastic liner and filament winding of composite fibers. During use, the hydrogen fuel tank is repeatedly charged and discharged, which causes damage to the composite region of the fuel tank. Therefore, to ensure the safe use of hydrogen fuel, it is necessary to monitor the condition of the hydrogen fuel tank. This article proposes a new, practical structural health monitoring (SHM) method based on the correlation between ultrasonic attenuation and crack density. The propagating ultrasonic wave attenuates by interaction with the damages in the composite materials of the fuel tank. Hydraulic cycle tests were performed to reproduce repeated damages in the fuel tanks. The micro-CT imaging method was used to verify the actual crack density of the fuel tank. The attenuation of the ultrasonic wave propagation and the crack density of fuel tanks were combined in one equation using the hydraulic test cycle as a parameter. With this practical SHM method, real-time structural health monitoring of type 4 fuel tanks can be done by measuring ultrasonic wave propagation on the fuel tank with the PZT active sensor network, offering hope for improved safety in the automotive and aerospace industries.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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 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".