Fatigue and Mechanical Damage Propagation in Automotive PEM Fuel Cells
Bibliographic record
Abstract
Polymer electrolyte membrane (PEM) fuel cells are generally exposed to high magnitude road-induced vibrations and impact loads, frequent humidity-temperature loading cycles, and freeze/thaw stresses when employed in automotive applications. The resultant mechanical stresses can play a significant role in the evolution of mechanical defects in the membrane electrode assembly (MEA). The focus of this research is to investigate fatigue challenges due to humidity-temperature (hygrothermal) cycles and vibrations and their effects on damage evolution in PEM fuel cells. To achieve this goal, this thesis is divided into three parts that provide insight into damage propagation in the MEA under i) hygrothermal cycles, ii) external applied vibrations, and iii) a combination of both to simulate realistic automotive conditions. A finite element damage model based on cohesive zone theory was developed to simulate the propagation of micro-scale defects (cracks and delaminations) in the MEA under fuel cell operating conditions. It was found that the micro-defects can propagate to critical states under start-up and shut-down cycles, prior to reaching the desired lifespan of the fuel cell. The simultaneous presence of hygrothermal cycles and vibrations severely intensified damage propagation and resulted in considerably large defects within 75% of the fuel cell life expectancy. However, the order of generated damage was found to be larger under hygrothermal cycles than vibrations. Under hygrothermal cycles, membrane crack propagation was more severe compared to delamination propagation. Conversely, the degrading influence of vibrations was more significant on delaminations. The presence of an anode/cathode channel offset under the combined loadings lead to a 2.5-fold increase in the delamination length compared to the aligned-channel case. The developed model can be used to investigate the damage behaviour of current materials employed in fuel cells as well as to evaluate the alternative materials for the next generation of fuel cell development.
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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) | 0.001 | 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 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".