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

2021 ◽  
Author(s):  
Jinlong Liu ◽  
Christopher Ulishney ◽  
Cosmin Dumitrescu
Keyword(s):  
Natural Gas ◽  
Nearest Neighbors ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
K Nearest Neighbors

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

2020 ◽  
Author(s):  
Jinlong Liu ◽  
Christopher Ulishney ◽  
Cosmin E. Dumitrescu
Keyword(s):  
Natural Gas ◽  
Energy Release ◽  
Engine Speed ◽  
Nearest Neighbors ◽  
Spark Ignition ◽  
Operating Conditions ◽  
Computational Time ◽  
Cylinder Pressure ◽  
K Nearest Neighbors ◽  
Peak Cylinder Pressure

Abstract 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 applied key engine operating variables such as the spark timing, air-fuel ratio, and engine speed as inputs to predict combustion phasing parameters such as the crank angles associated with peak cylinder pressure and 50% energy release. After training the correlative model, the selected engine variables produced acceptable errors for most operating conditions investigated. The results showed that the KNN algorithm predicted much better the location of the peak pressure than the location of the 50% energy release, as evidenced by the larger R2 values and smaller prediction errors. In addition, the regression model built in this study produced larger errors in the sparse-distributed region. Therefore, a more uniformly distributed training dataset is suggested for KNN algorithm, at least for the situations investigated in this research.

Laminar flame speed correlations for methane, ethane, propane and their mixtures, and natural gas and gasoline for spark-ignition engine simulations

2017 ◽  
Vol 18 (9) ◽  
pp. 951-970 ◽  
Author(s):  
Riccardo Amirante ◽  
Elia Distaso ◽  
Paolo Tamburrano ◽  
Rolf D Reitz
Keyword(s):  
Natural Gas ◽  
Laminar Flame ◽  
Spark Ignition ◽  
Flame Speed ◽  
Operating Conditions ◽  
Spark Ignition Engine ◽  
Computational Time ◽  
Laminar Flame Speed ◽  
Spark Ignition Engines ◽  
Empirical Correlations

The laminar flame speed plays an important role in spark-ignition engines, as well as in many other combustion applications, such as in designing burners and predicting explosions. For this reason, it has been object of extensive research. Analytical correlations that allow it to be calculated have been developed and are used in engine simulations. They are usually preferred to detailed chemical kinetic models for saving computational time. Therefore, an accurate as possible formulation for such expressions is needed for successful simulations. However, many previous empirical correlations have been based on a limited set of experimental measurements, which have been often carried out over a limited range of operating conditions. Thus, it can result in low accuracy and usability. In this study, measurements of laminar flame speeds obtained by several workers are collected, compared and critically analyzed with the aim to develop more accurate empirical correlations for laminar flame speeds as a function of equivalence ratio and unburned mixture temperature and pressure over a wide range of operating conditions, namely [Formula: see text], [Formula: see text] and [Formula: see text]. The purpose is to provide simple and workable expressions for modeling the laminar flame speed of practical fuels used in spark-ignition engines. Pure compounds, such as methane and propane and binary mixtures of methane/ethane and methane/propane, as well as more complex fuels including natural gas and gasoline, are considered. A comparison with available empirical correlations in the literature is also provided.

Strategies to improve the performance of a spark ignition engine using fuel blends of biogas with natural gas, propane and hydrogen

2018 ◽  
Vol 43 (46) ◽  
pp. 21592-21602 ◽  
Author(s):  
Juan P. Gómez Montoya ◽  
Andrés A. Amell ◽  
Daniel B. Olsen ◽  
German J. Amador Diaz
Keyword(s):  
Natural Gas ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Fuel Blends

Comparison of Random Forest and Neural Network in Modelling the Performance and Emissions of a Natural Gas Spark Ignition Engine

2021 ◽  
pp. 1-20
Author(s):  
Jinlong Liu ◽  
Qiao Huang ◽  
Christopher Ulishney ◽  
Cosmin E. Dumitrescu
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Natural Gas ◽  
Internal Combustion Engines ◽  
Engine Performance ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Data Driven ◽  
Ann Model ◽  
Mean Square Errors

Abstract Machine learning (ML) models can accelerate the development of efficient internal combustion engines. This study assessed the feasibility of data-driven methods towards predicting the performance of a diesel engine modified to natural gas spark ignition, based on a limited number of experiments. As the best ML technique cannot be chosen a priori, the applicability of different ML algorithms for such an engine application was evaluated. Specifically, the performance of two widely used ML algorithms, the random forest (RF) and the artificial neural network (ANN), in forecasting engine responses related to in-cylinder combustion phenomena was compared. The results indicated that both algorithms with spark timing, mixture equivalence ratio, and engine speed as model inputs produced acceptable results with respect to predicting engine performance, combustion phasing, and engine-out emissions. Despite requiring more effort in hyperparameter optimization, the ANN model performed better than the RF model, especially for engine emissions, as evidenced by the larger R-squared, smaller root-mean-square errors, and more realistic predictions of the effects of key engine control variables on the engine performance. However, in applications where the combustion behavior knowledge is limited, it is recommended to use a RF model to quickly determine the appropriate number of model inputs. Consequently, using the RF model to define the model structure and then employing the ANN model to improve the model's predictive capability can help to rapidly build data-driven engine combustion models.

Scavenged Pre-Chamber on a Gas Engine for Light Duty Truck

2016 ◽  
Author(s):  
Jiří Vávra ◽  
Zbyněk Syrovátka ◽  
Michal Takáts ◽  
Eduardo Barrientos
Keyword(s):  
Natural Gas ◽  
Thermal Loading ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Validation Data ◽  
Automotive Engine ◽  
Light Duty ◽  
Full Load Operation ◽  
Gas Fueled ◽  
Light Duty Truck

This work presents an experimental investigation of advanced combustion of extremely lean natural gas / air mixture in a gas fueled automotive engine with a scavenged pre-chamber. The pre-chamber, which was designed and manufactured in-house, is scavenged with natural gas and is installed into a modified cylinder head of a gas fueled engine for a light duty truck. For initial pre-chamber ignition tests and optimizations, the engine is modified into a single cylinder one. The pre-chamber is equipped with a spark plug, fuel supply and a miniature pressure transducer. This arrangement allows a simultaneous crank angle resolved pressure measurement in the pre-chamber and in the main combustion chamber and provides important validation data for computational fluid dynamics (CFD) simulations. The results of the tests and initial optimizations show that the pre-chamber engine is able to operate within a significantly wider range of mixture composition than the conventional spark ignition engine. Full load operation of the pre-chamber engine is feasible with stoichiometric mixture (compatible with a three-way catalyst), without excessive thermal loading of components. At low load operation, the results show low NOx emissions with a high potential to fulfil current and future NOx limits without lean NOx exhaust gas after-treatment. The scavenged pre-chamber helps to increase the combustion rate mainly in the initial phase of combustion. However, significant unburned hydrocarbons emissions due to incomplete combustion need further optimizations. Thermal efficiency of lean operation of the engine with the pre-chamber compared to the conventional spark ignition system operated in stoichiometric conditions shows approximately 13% improvement.

Development of the Control and Acquisition System for a Natural-Gas Spark-Ignition Engine Test Bench

2019 ◽  
Author(s):  
Lorenzo Gasbarro ◽  
Jinlong Liu ◽  
Christopher Ulishney ◽  
Cosmin E. Dumitrescu ◽  
Luca Ambrogi ◽  
...  
Keyword(s):  
Natural Gas ◽  
Internal Combustion Engines ◽  
Test Bench ◽  
Spark Ignition ◽  
Operating Conditions ◽  
Spark Ignition Engine ◽  
Acquisition System ◽  
Combustion Engines ◽  
Engine Test ◽  
New Sensors

Abstract Investigations using laboratory test benches are the most common way to find the technological solutions that will increase the efficiency of internal combustion engines and curtail their emissions. In addition, the collected experimental data are used by the CFD community to develop engine models that reduce the time-to-market. This paper describes the steps made to increase the reliability of engine experiments performed in a heavy-duty natural-gas spark-ignition engine test-cell such as the design of the control and data acquisition system based on Modbus TCP communication protocol. Specifically, new sensors and a new dynamometer controller were installed. The operation of the improved test bench was investigated at several operating conditions, with data obtained at both high- and low-sampling rates. The results indicated a stable test bench operation.

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