Determination of Thermal Barrier Coatings Layers Optimum Thickness via PSO-SA Hybrid Optimization Method concerning Thermal Stress
Bibliographic record
Abstract
Turbine entry temperature of turbo-engines has been increased to improve proficiency. Consequently, protecting the hot section elements experiencing aggressive service conditions necessitates the applying of thermal barrier coatings (TBC). Developing TBC systems and improving performance is an ongoing endeavour to prolong the lifetime. Thus, various studies have been conducted to find the optimum properties and dimensions. In this paper, the optimum thickness of intermediate bond coat (BC) and top coat (TC) have been determined via a novel hybrid particle swarm and simulated annealing stochastic optimization method. The optimum thicknesses have been achieved under the constraint of thermal stress induced by thermal fatigue, creep, and oxidation in the TC while minimizing the weight during twenty cycles. The solutions for BC and TC thicknesses are respectively 50 μm and 450 μm. Plane stress condition has been adopted for theoretical and finite element stress analysis, and the results are successfully compared.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 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".