This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Development of a RANS-Based Knock Model to Infer the Knock Probability in a Research Spark-Ignition Engine
- Alessandro D'Adamo - Universita degli Studi di Modena ,
- Sebastiano Breda - Universita degli Studi di Modena ,
- Salvatore Iaccarino - Universita degli Studi di Modena ,
- Fabio Berni - Universita degli Studi di Modena ,
- Stefano Fontanesi - Universita degli Studi di Modena ,
- Barbara Zardin - Universita degli Studi di Modena ,
- Massimo Borghi - Universita degli Studi di Modena ,
- Adrian Irimescu - Istituto Motori CNR ,
- Simona Merola - Istituto Motori CNR
ISSN: 1946-3936, e-ISSN: 1946-3944
Published March 28, 2017 by SAE International in United States
Citation: D'Adamo, A., Breda, S., Iaccarino, S., Berni, F. et al., "Development of a RANS-Based Knock Model to Infer the Knock Probability in a Research Spark-Ignition Engine," SAE Int. J. Engines 10(3):722-739, 2017, https://doi.org/10.4271/2017-01-0551.
Engine knock is one of the most limiting factors for modern Spark-Ignition (SI) engines to achieve high efficiency targets. The stochastic nature of knock in SI units hinders the predictive capability of RANS knock models, which are based on ensemble averaged quantities.
To this aim, a knock model grounded in statistics was recently developed in the RANS formalism. The model is able to infer a presumed log-normal distribution of knocking cycles from a single RANS simulation by means of transport equations for variances and turbulence-derived probability density functions (PDFs) for physical quantities. As a main advantage, the model is able to estimate the earliest knock severity experienced when moving the operating condition into the knocking regime.
In this paper, improvements are introduced in the model, which is then applied to simulate the knock signature of a single-cylinder 400cm3 direct-injection SI unit with optical access; the engine is operated with two spark timings, under knock-safe and knocking conditions respectively. The statistical prediction of knock resulting from the presented knock model is compared to the experimental evidence for both investigated conditions.
The agreement between the predicted and the measured knock distributions validates the proposed knock model. Finally, limitations and some unprecedented possibilities given by the model are critically discussed, with particular emphasis on the meaning of RANS knock prediction.