Risk assessment of <scp> LH <sub>2</sub> </scp> storage handling system using fuzzy failure mode effect criticality analysis
Bibliographic record
Abstract
Abstract The safe handling of liquid hydrogen (LH 2 ) is critical to the advancement of hydrogen‐based technologies, particularly in process industries, the energy sector, and in automobile and aerospace engineering. Due to the extreme properties of hydrogen, including low boiling point, high flammability, and susceptibility to leaks, LH 2 handling systems pose significant safety challenges. A comprehensive risk assessment is essential to identify and mitigate potential hazards associated with these systems. This paper analyzes 40 hydrogen‐based accidents and demonstrates the application of fuzzy failure mode, effects, and criticality analysis (FMECA) to assess risks in LH 2 handling systems. By integrating fuzzy logic into traditional FMECA, the methodology addresses the inherent uncertainties and subjective judgement that are always associated with the failure modes and their impacts. The present study aims to enhance safety and reliability and guides in decision‐making for the design and operation of LH 2 handling systems. The findings also contribute to the broader efforts of promoting safe and sustainable hydrogen infrastructure.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".