Title: Analysis of Real-World Preignition Data Using Neural Networks

Journal Article · · SAE Technical Paper Series
ORCiD logo [1]; ORCiD logo [1];  [2];  [3]
  1. Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
  2. Southwest Research Institute (SWRI), San Antonio, TX (United States)
  3. Lubrizol Corporation, Cleveland, OH (United States)
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Increasing adoption of downsized, boosted, spark-ignition engines has improved vehicle fuel economy, and continued improvement is desirable to reduce carbon emissions in the near-term. However, this strategy is limited by damaging preignition events which can cause hardware failure. Research to date has shed light on various contributing factors related to fuel and lubricant properties as well as calibration strategies, but the causal factors behind an individual preignition cycle remain elusive. If actionable precursors could be identified, mitigation through active control strategies would be possible. This paper uses artificial neural networks to search for identifiable precursors in the cylinder pressure data from a large real-world data set containing many preignition cycles. It is found that while follow-up preignition cycles in clusters can be readily predicted, the initial preignition cycle is not predictable based on features of the cylinder pressure. Further, this indicates that the alternating pattern of preignition cycles within clusters is influenced by the thermodynamic state as reflected in the pressure, but that the trigger for the initial preignition cycle is not thermodynamic in nature, but more likely tied to a critical threshold in the chemistry of the fuel/lubricant mixture in the upper crevice or other factors related to the presence of an ignition source.

Research Organization:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Sustainable Transportation. Vehicle Technologies Office (VTO)
Grant/Contract Number:
AC05-00OR22725
OSTI ID:
2217731
Journal Information:
SAE Technical Paper Series, Journal Name: SAE Technical Paper Series Journal Issue: 01 Vol. 2023; ISSN 0148-7191
Publisher:
SAE InternationalCopyright Statement
Country of Publication:
United States
Language:
English

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