Evaluation of Solid Particle Erosion of EB-PVD TBCs under Thermal Cycling Conditions Based on a Stochastic Approach
Bibliographic record
Abstract
The solid particle erosion behavior of electron-beam physical vapor deposition (EB-PVD) thermal barrier coatings (TBCs) was numerically evaluated under thermal cycling conditions. The erosion rates were calculated based on the mechanics-based formulae where the model parameters are fitted to the temperature-process-dependent test data available in the literature. A stochastic approach was applied to simulate the erosion behavior toward service conditions. The mechanics-based formulae were then validated by experimentally measured temperature and sintering-dependent erosion rates from the literature. The pseudoductile erosion behavior is identified for silica particles in the EB-PVD topcoat (TC) erosion system above the intermediate temperatures (~220 °C) due to the softening of partial molten silica particles, thus leading to an increase in the cutting wear and a decrease in deformation wear. The erosion rates are found to decrease versus temperature but increase versus thermal cycles. Such erosion behavior could be attributed to propagation of sintering cracks induced at elevated temperatures. The parametric calculations show that both erosion and thermal cycling parameters have a profound effect on the erosion mechanism of EB-PVD TC. The erosion rate increases at higher solid particle velocity and accumulated mass but displays a pseudoductile erosion behavior versus variation of impacting angles. Two types of erosion mechanisms were evaluated under different thermal cycling conditions. Under the burner cycling test with a short high-temperature dwell period, the erosion mechanism of EB-PVD TBCs is governed by temperature, while under an isothermal cycling test with a high-temperature long dwell period, the erosion is determined by sintering time. The failure mechanisms of EB-PVD TBCs under solid particle erosion processes are discussed combining internal cracking within topcoat and external erosion on the surface of topcoat.
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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 | 0.001 | 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".