Prediction of knock intensity and validation in an optical SI engine

Jiabo Zhang, Hao Shi, Minh Bau Luong, Qinglong Tang, Kalim Uddeen, Gaetano Magnotti, James W. G. Turner, Hong G. Im

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


To provide fundamental insights into the underlying mechanisms of knock occurrence in a spark-ignition (SI) engine, optical diagnostic measurements were performed at advanced spark timing under boosted conditions. Employing high-speed imaging, deflagration-to-detonation transition (DDT) processes were recorded. The states and locations of the unburned mixture more susceptible to autoignition were identified by statistically analyzing the recorded images of the spatial distribution of flame propagation and end-gas autoignition under distinct combustion modes. The relationships between the unburned mixture fraction (UMF) and two metrics associated with the knock intensity levels, namely the maximum amplitude of pressure oscillation (MAPO) and the peak in-cylinder pressure, Pmax, were examined. In contrast to common beliefs, we found that the knock intensity represented by MAPO is not strongly correlated with UMF because UMF alone is inherently insufficient to represent the thermo-chemical properties of the bulk mixture inhomogeneities and its chemical reactivity at the onset of end-gas autoignition occurrence. Instead, a much stronger correlation was found between UMF and a newly proposed normalized-peak-pressure metric, allowing an a prior prediction of Pmax, which is a critical parameter associated with the propensity of engine failure if Pmax exceeds the strength limit of the engine. To quantitatively predict MAPO under SI-engine conditions, a refined model was proposed by imposing the pressure and temperature traces into the zero-dimensional (0-D) reactor model, such that the thermochemical properties of the transient mixture state at the onset of end-gas autoignition are properly incorporated into the predictive criteria. The model was validated against a large experimental dataset consisting of 619 cycles under various operating conditions. The results show that the model can reliably predict the end-gas autoignition and the knock intensity levels dictated by MAPO regardless of the stochastic nature of the knock development process.
Original languageEnglish (US)
Pages (from-to)112854
JournalCombustion and Flame
StatePublished - May 26 2023

Bibliographical note

KAUST Repository Item: Exported on 2023-05-29
Acknowledged KAUST grant number(s): URF/1/3710-01-01
Acknowledgements: This work was sponsored by competitive research funding (URF/1/3710-01-01) from the King Abdullah University of Science and Technology (KAUST) and used the resources of the KAUST Supercomputing Laboratory (KSL).

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • General Physics and Astronomy
  • General Chemical Engineering
  • General Chemistry
  • Fuel Technology


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