The Impact of Formation of Oxide Layer on the Piston Crown Using Micro - Arc Oxidation on the Characteristics of the Spark Ignition Engine
Bibliographic record
Abstract
In the present study, experiments were conducted to compare the effect of oxide layer formation on the piston crown coated using Micro-Arc Oxidation (MAO) with uncoated piston on the combustion and emission characteristics of the port injected Spark Ignition engine fueled by gasoline. The micro-arc oxidation (MAO) coating technique is the modern process to form a ceramic oxide layer on the reactive metal substrate (base metal) by electrochemical and electro-thermal oxidation in an alkaline electrolytic solution. Using MAO technique, an oxide layer of thickness 72 μm was formed on the piston crown. This oxide layer acts as a thermal barrier to reduce the in-cylinder heat rejection and increase the durability of the piston by withstanding high temperature and pressure produced during combustion. Combustion flames have been captured using the AVL combustion analyzer to analyze the development and propagation of flames within the engine cylinder. From the flame images, it was observed that propagation of flame was faster in MAO coated piston compared to uncoated piston. This is because of higher local temperature inside the combustion chamber that was resulted due to low thermal conductivity of MAO layer. It was also found that carbon monoxide (CO) and hydrocarbon (HC) emissions were reduced as a result of efficient fuel combustion, while NOx emissions increased because of increased combustion temperatures for MAO coated pistons. Keywords: Electro-thermal oxidation, Flame propagation, Micro-arc oxidation, Piston crown, Thermal barrie
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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 | 0.000 |
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".