Spark Plug Simulation with the Use of Three Types of Fuels in Direct Injection Engines for the Evaluation of Polluting Factors
Bibliographic record
Abstract
The present work has as main objective the use of a biofuel (Ecopaís) in a direct injection vehicle, it is an option to reduce damage to health and the environment, for this a static thermal simulation will be done in the spark plug, to compare the results of the aforementioned software using On Board tests, in a 1500 cc engine. The measurements of the emission factors of CO, HC and NOx gases will be considered in a route established in the city of Quito from 2399 to 2870 meters above sea level. The interaction of the element is carried out in the ANSYS Academic program which is 14977 nodes and 7523 elements to be studied with automatic meshing, obtaining that the Ecopaís and Ecopaís + Ferox fuels have the highest heat flow with a 5% divergence compared to the Extra fuel + Ferox. There is a significant reduction in pollutant emissions of 3% of CO with the use of Ecopaís in comparison to Extra + Ferox fuel, in the case of HC, Ecopaís and Ecopaís + Ferox fuels with 3% lower emissions compared to Extra fuel + Ferox, and in NOx, fuels that have Extra + Ferox and Ecopaís + Ferox solid additives are 3 and 3.5% lower compared to Ecopaís fuel, respectively. Keywords: biofuel, termal, on board, ferox, emission factors. Resumen El presente trabajo tiene como objetivo fundamental la utilización de un biocombustible (Ecopaís) en un vehículo de inyección directa, es una opción para disminuir daños a la salud y al medio ambiente, para ello se hará una simulación térmica estática en la bujía de encendido, para comparar los resultados del mencionado software mediante pruebas On Board, en un motor de 1500 cc. Las mediciones de los factores de emisión de gases de CO, HC y NOx, se contemplará en una ruta establecida en la ciudad de Quito de 2399 hasta 2870 m.s.n.m. La interacción del elemento se realiza en el programa ANSYS Academic que es de 14977 nodos y 7523 elementos a estudiar con el mallado automático, obteniendo que los combustibles Ecopaís y Ecopaís+Ferox tienen el mayor flujo de calor con una divergencia del 5% en comparación del combustible Extra + Ferox. Se evidencia una reducción significativa de emisiones contaminantes del 2.5% del CO con el uso del Ecopaís en comparación del combustible Extra + Ferox, en el caso de HC los combustibles Ecopaís y Ecopaís + Ferox con un 1% menor en emisiones en comparación al combustible Extra + Ferox, y en el NOx los combustibles que tienen aditivo sólido Extra+Ferox y Ecopaís+Ferox son menores en un 6 y 4% con respecto al combustible Ecopaís respectivamente. Palabras clave: biocombustible, térmica, on board, ferox, factores de emisiones.
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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) | 0.001 | 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) | 0.002 | 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".