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Research Papers: Fuel Combustion

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

[+] Author and Article Information
Janardhan Kodavasal, Ahmed Abdul Moiz, Muhsin Ameen, Sibendu Som

Argonne National Laboratory,
Energy Systems Division,
Argonne, IL 60439

Contributed by the Internal Combustion Engine Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received February 15, 2018; final manuscript received March 13, 2018; published online May 15, 2018. Editor: Hameed Metghalchi.

J. Energy Resour. Technol 140(10), 102204 (May 15, 2018) (9 pages) Paper No: JERT-18-1132; doi: 10.1115/1.4040062 History: Received February 15, 2018; Revised March 13, 2018

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 rpm. 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 a part of another work performed by us. In this 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 the pre-ignition flow-fields, the flame shapes, and the eventual outcome of the cycle (whether a cycle will be a high or a low cycle).

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Figures

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Fig. 1

Experimental cycles (left), simulated consecutive cycles (middle), simulated parallel cycles (right)

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Fig. 2

Snapshot showing axes orientation with respect to the CFD model

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Fig. 3

A single pair of one low cycle and one high cycle at 722 deg. CA showing similar physical behavior when simulated using the parallel and consecutive approaches.

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Fig. 11

Predictions on the 15% test set considering two pre-ignition flow metrics—U5.5 mm, and W5.5 mm

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Fig. 4

A single pair of one low cycle and one high cycle at a common mass percent burned of 3% showing similar physical behavior when simulated using the parallel and consecutive approaches (similar to the behavior observed with them at the same CA)

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Fig. 5

Pearson correlation between ten metrics chosen to represent flame topologies, pre-ignition velocity flow-fields, and PCP

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Fig. 6

Sample decision tree showing the partitioning of the data based on a random subset of four features of the ten input features (4 = √10 rounded up) representing flame shape and pre-ignition velocities, using an if/else structure; the random forest is an ensemble of multiple decision trees

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Fig. 7

KNIME workflow for building a random forest model

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Fig. 8

Feature (or input) importance for predicting PCP using random forest

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Fig. 9

Linear fit of PCP to high-ranking inputs, with scatter around the fit shown

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Fig. 10

Predictions on the 15% test set considering four metrics—sphericity, U5.5 mm, W5.5 mm, and COMoX

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