Probability Assessment of the Fuel Tank Structural Feature Failures
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">The paper provides an approach to establish compliance with current regulatory standards applicable to lightning protection of the fuel tank structure for Non-Fault Tolerant Feature Failures (NFTFF) through a numerical probability assessment. The proposed procedure is using the criteria defined in the FAA Policy Guidance for fuel tank structural lightning protection and is aligned with the regulatory path described as petitioning for an exemption. Failure modes of structural components for which fault tolerance has been shown to be impractical need to be addressed and the overall likelihood of fuel vapour ignition due to these failure modes must be shown to be extremely improbable. In order to accomplish this, the quantitative assessment of the overall probability of fuel vapour ignition is performed, along with all relevant data to support the probabilities determined for the purpose of this analysis. The relevant non-fault tolerant structural design areas that may contribute to ignition sources inside the fuel tank when a failure occurs must be identified. Each type of non-fault tolerant feature failure must be assessed and the result has to be summed. The set of the potential failure modes regardless of the probability of occurrence is established by performing a complete wing tank survey in pertinent areas related to fuel tank lightning protection. If the failure assessment results in demonstrating that the design is free from ignition sources after an assessment of relevant single failure conditions, then the design is considered to be fault tolerant. If not, then further analysis is required per numerical probability assessment for the Non-Fault Tolerant Feature Failures. The subset of NFTFFs must be defined and the numerical probability assessment has to be performed. The purpose of the assessment is to substantiate that the probability of failures associated with non-fault tolerant features that might lead to a fuel tank explosion belongs to the extremely improbable category. This paper intends to provide a practical method for the fuel tank structure numerical assessment along with a real-life example illustrating a potential application.</div></div>
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 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".