Location of the peak cylinder pressure and the crank angle associated with half of the energy releases during the combustion process are generally used to define the engine combustion phasing and control the engine efficiency. To accelerate the optimization of a natural gas spark ignition internal combustion engine, this study proposes a black box modeling approach that will reduce the experimental or computational time needed to estimate the high efficient operating conditions at a particular engine speed and load via combustion phasing information. Specifically, a k-nearest neighbors (KNN) algorithm predicted the combustion phasing applied key engine operating variables as inputs such as the spark timing, air-fuel ratio, and engine speed. After properly training the correlative model, the selected variables produced the acceptable errors for most operating conditions investigated, with R-squared value higher than 0.9. Moreover, a smaller number of neighbors considered in the algorithm reduced the prediction error in the sparse-distributed region. In addition, the results showed the KNN modeling performance would benefit from a more uniformly distributed training dataset.

I added the abstract twice because the 400-word minimum is excessive for a preiminary abstract, in my opinion. 

Location of the peak cylinder pressure and the crank angle associated with half of the energy releases during the combustion process are generally used to define the engine combustion phasing and control the engine efficiency. To accelerate the optimization of a natural gas spark ignition internal combustion engine, this study proposes a black box modeling approach that will reduce the experimental or computational time needed to estimate the high efficient operating conditions at a particular engine speed and load via combustion phasing information. Specifically, a k-nearest neighbors (KNN) algorithm predicted the combustion phasing applied key engine operating variables as inputs such as the spark timing, air-fuel ratio, and engine speed. After properly training the correlative model, the selected variables produced the acceptable errors for most operating conditions investigated, with R-squared value higher than 0.9. Moreover, a smaller number of neighbors considered in the algorithm reduced the prediction error in the sparse-distributed region. In addition, the results showed the KNN modeling performance would benefit from a more uniformly distributed training dataset.

Location of the peak cylinder pressure and the crank angle associated with half of the energy releases during the combustion process are generally used to define the engine combustion phasing and control the engine efficiency. To accelerate the optimization of a natural gas spark ignition internal combustion engine, this study proposes a black box modeling approach that will reduce the experimental or computational time needed to estimate the high efficient operating conditions at a particular engine speed and load via combustion phasing information. Specifically, a k-nearest neighbors (KNN) algorithm predicted the combustion phasing applied key engine operating variables as inputs such as the spark timing, air-fuel ratio, and engine speed. After properly training the correlative model, the selected variables produced the acceptable errors for most operating conditions investigated, with R-squared value higher than 0.9. Moreover, a smaller number of neighbors considered in the algorithm reduced the prediction error in the sparse-distributed region. In addition, the results showed the KNN modeling performance would benefit from a more uniformly distributed training dataset.


Predicting the Combustion Phasing of a Natural Gas Spark Ignition Engine Using the K-Nearest Neighbors Algorithm

Division / Sponsors: 
Aerospace, Bioengineering, Applied Mechanics, Design Engineering Division, Advanced Energy Systems, Manufacturing Engineering Division, Noise Control and Acoustics, Nondestructive Evaluation, Diagnosis, and Prognosis (NDPD), Nuclear Engineering Division, Process Industries Division, Fluids Engineering, Heat Transfer, Materials, Microelectromechanical Systems, Safety Engineering and Risk Analysis, Computers & Information in Engineering


Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2020/volume/84478

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