A Study on the Refinement of Turbulence Intensity Prediction for the Estimation of In-Cylinder Pressure in a Spark-Ignited Engine Seoul National University

Format:

Conference/Event

Author/Creator:

Kim, Kim, author.

Contributor:

Choi, Hoimyung

Kim, Joohan

Ko, Insuk

Min, Kyoungdoug

Conference Name:

WCX 17: SAE World Congress Experience (2017-04-04 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2017

Summary:

AbstractThe role of 1D simulation tool is growing as the engine system is becoming more complex with the adoption of a variety of new technologies. For the reliability of the 1D simulation results, it is necessary to improve the accuracy and applicability of the combustion model implemented in the 1D simulation tool. Since the combustion process in SI engine is mainly determined by the turbulence, many models have been concentrating on the prediction of the evolution of in-cylinder turbulence intensity.In this study, two turbulence models which can resemble the turbulence intensity close to that of 3D CFD tool were utilized. The first model is dedicated to predicting the evolution of turbulence intensity during intake and compression strokes so that the turbulence intensity at the spark timing can be estimated properly. The second model is responsible for predicting the turbulence intensity of burned and unburned zone during the combustion process.The refined information on turbulence intensity was used for the estimation of flame propagation speed, burn rate, and heat transfer rate. The in-cylinder pressure prediction was then conducted and compared with the experimental results. From the simulation results, it was possible to confirm that the utilization of turbulence models, which can resemble the 3D CFD results enabled accurate prediction of in-cylinder pressure under engine speed of 1500, 2000, and 2500 rpm, part load and full load with various spark timings without the case dependent tuning of the model constants

Notes:

Vendor supplied data

Publisher Number:

2017-01-0525

Access Restriction:

Restricted for use by site license