Development of Alternative Thermal Barrier Coatings for Diesel Engines
Bibliographic record
Abstract
The use of thermal barrier coatings (TBCs) to increase the combustion temperature in diesel engines has been pursued for over 20 years. Increased combustion temperature can increase the efficiency of the engine, decrease the CO and (possibly) the NOx emission rate. However, TBCs have not yet met with wide success in diesel engine applications. The most common TBC system is Yttria Partially Stabilized Zirconia (Y-PSZ) which has shown good performance in turbine blade coatings where temperatures approach 1100°C. To reach the desirable temperature of 850-900°C in the combustion chamber from the current temperature of 350-400°C, a coating with a thickness of order 1mm is required, significantly thicker than turbine blade coatings which are on the order of 100μm thick. This results in different temperature and stress profiles in the coating during service than in the case of turbine coatings, and different failure mechanisms. Recent advances in the control of coating structure have led to the development of new PSZ coating microstructures containing grains and pores with sizes in the range of 100-300 nm. Thermal-mechanical properties of such coatings have been characterized and compared with coatings exhibiting the traditional thermal spray structure to asses their suitability as thick TBC for diesel engine applications. Selected coatings have been subjected to engine testing in an instrumented single cylinder diesel test rig. Among possible alternative materials, one of the most promising is mullite. Mullite has excellent thermo-mechanical behavior; however its low coefficient of thermal expansion creates a large mismatch with the substrate. To address this problem, multilayer systems have been developed which minimize the thermal expansion mismatch stresses while maintaining chemical and phase stability. The design considerations for such multilayer systems are discussed.
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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) | 0.001 | 0.001 |
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".