Reliability issues and voltage degradation
Bibliographic record
Abstract
Abstract To date the application of fuel cell systems has focused on operational performance and little attention has been given to the reliability of fuel cells and stacks. This section discusses reliability and system effectiveness as they are related to fuel cell stacks, with a specific focus on proton exchange membrane (PEM) fuel cell stacks. The potential applications of different reliability analysis methodologies to fuel cell stacks is discussed. The potential failure modes and causes of failure experienced in fuel cells stacks are outlined. There are a number of reliability analysis techniques practiced today that would be useful in the further development of fuel cell stacks. This section briefly introduces these techniques and the information and data requirements of each technique. The three basic techniques to be addressed include: failure mode and effects analysis, reliability block diagrams and fault tree analysis. Durability or irreversible degradation of the stack is defined as the ability of a membrane and electrode assembly (MEA) to resist permanent change in performance over time. With respect to fuel cell stacks, the definitions for reliability of the stack include failure modes that can lead to catastrophic failure, as well as performance degradation to below an acceptable level. Stability or reversible degradation are the recoverable phenomena involving voltage or current density decay. The application of reliability terminology for fuel cell stacks is addressed, and different definitions for failure are discussed. Causes of PEM fuel cell failure are likely to be defect propagation, low level contamination, corrosion of the plates leading to increased contact resistance, thermal or hydration cycling leading to mechanical stress, catalyst particle ripening, swelling of polymer materials in the active catalyst layer leading to changes in water removal characteristics, compaction of the gas diffusion layer due to mechanical stresses, degradation of the polymer material and surface chemistry changes in gas diffusion layer.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".