A universally applicable 0D turbulence model based on the physical analysis of fundamental tumble behaviors in spark-ignition engines. (May 2023)

• Record Type: Journal Article
• Authors: Kim, Myoungsoo; Song, Han Ho
• Is Part Of: International journal of engine research. Volume 24:Number 5(2023)
• Page Start: 2051
• Page End: 2071
• Publication Date: 2023-05
• Subjects: 0D simulation -- spark-ignition engine -- turbulence model -- tumble -- spinning up -- vortex breakdown; Engines -- Periodicals; 629.25
• DOI: 10.1177/14680874221109776
• Languages: English
• ISSNs: 1468-0874

Abstract

Predicting turbulence in a zero-dimensional (0D) simulation is an undeniably challenging task due to the complexity of in-cylinder charge motion. In that respect, a physics-based understanding of in-cylinder flow phenomena is of vital importance for establishing high-fidelity 0D turbulence models under diverse engine conditions. The proposed 0D model is hence built upon the kinetic energy analyses of tumble, evidenced by three-dimensional computational fluid dynamics. Specifically, the overall 0D turbulence model consists of an intake model, a spinning up model, and a turbulence production model. The major difference between this model and the existing 0D turbulence models is that this model is grounded on a kinetic energy perspective of tumble, as opposed to an angular momentum perspective of tumble. That is, the behaviors of tumble, such as spinning up and vortex breakdown, are interpreted and modeled based on the changes in the kinetic energy of tumble. This enables the proposed model to secure wider applicability at various engine conditions, which were otherwise difficult to achieve. Therefore, the 0D simulation in this study was able to predict turbulent intensities at the conditions, differing in valve strategy, engine geometry, and engine operation, without changing any validation constants. Furthermore, along with the validation points used in this study, better predictions of turbulent intensity were achieved using the proposed model compared to the existing state-of-the-art model.