Perspectives on Thermal Gradients in Porous ZrO2-7–8 wt.% Y2O3 (YSZ) Thermal Barrier Coatings (TBCs) Manufactured by Air Plasma Spray (APS)
Bibliographic record
Abstract
Porous (~10–20%) ZrO2-7–8 wt.% Y2O3 (YSZ) thermal barrier coatings (TBCs) manufactured via air plasma spray (APS) and exhibiting a thickness range of ~250–500 µm, provide thermal insulation from the hot combustion gases to the metallic parts located in the hot stationary sections of gas turbine engines (e.g., combustion chambers of aerospace turbines). The objective of this paper was to measure and report the thermal gradient values in a benchmark porous (~15%) APS YSZ TBC, working within the known acceptable maximum temperature envelop conditions of a TBC/substrate system, i.e., T-ysz ~1300 °C and T-sub ~1000 °C. In order to accomplish this objective, the following steps were performed. A benchmark APS YSZ TBC exhibiting two distinct thicknesses (~260 and ~460 µm) was manufactured. In addition, a thermal gradient laser-rig was employed to generate a temperature drop (ΔT) along the coated coupon, with the target operate within the acceptable maximum temperature capabilities of this type of TBC/substrate architecture. This target was achieved, i.e., T-ysz values were not higher than ~1300 °C while the substrate temperatures did not reach values above ~1000 °C. The ΔTs for the ~260 and ~460 µm YSZ TBCs were ~280 and ~465 °C, respectively. The thermal gradient value for both YSZ TBCs was ~0.90 °C/µm, which falls within those reported in the literature for porous APS YSZ TBCs.
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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 | 0.000 |
| 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 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".