The Influence of Thermal Barrier Coating Surface Roughness on Spark-Ignition Engine Performance and Emissions
Bibliographic record
Abstract
The separate effects on heat transfer of 1) piston crown surface finish and 2) the use of a metal based thermal barrier coating (MTBC) on the piston crown of a spark ignition (SI) engine were quantified through experimental analysis in a single cylinder CFR engine. Measured engine parameters such as power, fuel consumption, emissions and cylinder pressure were used to identify the effects of the coating and its surface finish. Two piston coatings were tested: a baseline copper coating and a metal-based thermal barrier coating. Each coating was tested at multiple surface finishes. Tests showed that reducing surface roughness of both coatings increased in-cylinder temperature and pressure as a result of reduced heat transfer through the piston crown. For both coatings, this resulted in small improvements (∼3%) in power and fuel consumption, while also having a measurable effect on emissions. Oxides of nitrogen emissions increased while total hydrocarbon emissions generally decreased as a result of polishing. The polished coatings were also seen to increase in-cylinder peak pressures and burn rates. Improvements attributed to the TBC were found to be small, but statistically significant. At an equivalent surface finish, the MTBC-coated piston produced slightly higher power output and peak pressures. Hydrocarbon emissions were also seen to be significantly higher for the MTBC-coated piston due to its porosity. The effectiveness of the coating was found to be highly dependent on surface finish.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".