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Title: Using Machine Learning to Analyze Factors Determining Cycle-to-Cycle Variation in a Spark-Ignited Gasoline Engine

Journal Article · · Journal of Energy Resources Technology
DOI: https://doi.org/10.1115/1.4040062 · OSTI ID:1487207
 [1];  [1];  [1];  [1]
  1. Argonne National Lab. (ANL), Argonne, IL (United States)
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In this work, we have applied a machine learning (ML) technique to provide insights into the causes of cycle-to-cycle variation (CCV) in a gasoline spark-ignited (SI) engine. The analysis was performed on a set of large eddy simulation (LES) calculations of a single cylinder of a four-cylinder port-fueled SI engine. The operating condition was stoichiometric, without significant knock, at a load of 16 bar brake mean effective pressure (BMEP), at an engine speed of 2500 revolutions per minute. A total of 123 cycles was simulated. Of these, 49 were run in sequence, while 74 were run in parallel. For the parallel approach, each cycle is initialized with its own synthetic turbulent field to generate CCV, as part of another work performed by us. In the current work, we used 3D information from all 123 cycles to compute flame topology and pre-ignition flow-field metrics. We then evaluated correlations between these metrics, and peak cylinder pressure (PCP) employing an ML technique called random forest. The computed metrics form the inputs to the random forest model, and PCP is the output. This model captures the effect of all inputs, as well as interactions between them owing to its decision-tree structure. The goal of this work is to demonstrate (as a first step) that ML models can implicitly learn complex relationships between pre-ignition flow-fields, flame shapes, and the eventual outcome of the cycle (whether a cycle will be a high or a low cycle).

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
1487207
Journal Information:
Journal of Energy Resources Technology, Vol. 140, Issue 10; ISSN 0195-0738
Publisher:
ASMECopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 15 works
Citation information provided by
Web of Science

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  • Scarcelli, Riccardo; Sevik, James; Wallner, Thomas
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Cited By (2)

Prediction of Cyclic Variability and Knock-Limited Spark Advance in a Spark-Ignition Engine journal April 2019
Machine learning–based analysis of in-cylinder flow fields to predict combustion engine performance journal March 2019

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Related Subjects

42 ENGINEERING
CFD
Internal Combustion Engine
Machine Learning