Prediction of Optimum Ignition Timing in a Natural Gas-Fueled Spark Ignition Engine Using Neural Network

2006 ◽  
Author(s):  
Ahmed E. Hassaneen
Keyword(s):  
Neural Network ◽  
Natural Gas ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Ignition Timing ◽  
Gas Fueled

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.

Influence of exhaust residuals on combustion phases, exhaust toxic emission and fuel consumption from a natural gas fueled spark-ignition engine

2018 ◽  
Vol 165 ◽  
pp. 440-446 ◽  
Author(s):  
Stanislaw Szwaja ◽  
Ehsan Ansari ◽  
Sandesh Rao ◽  
Magdalena Szwaja ◽  
Karol Grab-Rogalinski ◽  
...  
Keyword(s):  
Natural Gas ◽  
Fuel Consumption ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Toxic Emission ◽  
Gas Fueled

scholarly journals Effect of the Factors of Combustion Chamber on the Performance and Exhaust Emissions in a Natural Gas Fueled Spark Ignition Engine.

1998 ◽  
Vol 64 (621) ◽  
pp. 1574-1580
Author(s):  
Katsumi KATAOKA ◽  
Yoshitaka ATSUMI ◽  
Daisuke SEGAWA ◽  
Toshikazu KADOTA ◽  
Yukiyoshi FUKANO
Keyword(s):  
Natural Gas ◽  
Combustion Chamber ◽  
Exhaust Emissions ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Gas Fueled

On the use of artificial neural networks to model the performance and emissions of a heavy-duty natural gas spark ignition engine

2021 ◽  
pp. 146808742110344
Author(s):  
Qiao Huang ◽  
Jinlong Liu ◽  
Christopher Ulishney ◽  
Cosmin E Dumitrescu
Keyword(s):  
Neural Network ◽  
Natural Gas ◽  
Combustion Process ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Heavy Duty ◽  
Network Algorithm ◽  
Performance And Emissions ◽  
Neural Network Algorithm ◽  
Artificial Neural

The use of computational models for internal combustion engine development is ubiquitous. Numerical simulations using simpler to complex physical models can predict engine’s performance and emissions, but they require large computational capabilities. By comparison, statistical methodologies are more economical tools in terms of time and resources. This paper investigated the use of an artificial neural network algorithm to simulate the nonlinear combustion process inside the cylinder. Three engine control variables (i.e. spark timing, mixture equivalence ratio, and engine speed) were set as the model inputs. Outputs included peak cylinder pressure and its location, maximum pressure rise rate, indicated mean effective pressure, ignition lag, combustion phasing, burn duration, exhaust temperature, and engine-out emissions (i.e. nitrogen oxides, carbon monoxide, and unburned hydrocarbons). Eighty percent of the experimental data from a heavy-duty natural gas spark ignition engine were utilized to train the model. The perceptions accurately learned the combustion characteristics and predicted engine responses with acceptable errors, evidenced by close-to-unity coefficient of determination and close-to-zero root-mean-square error. Moreover, the regressors captured the effect of key operating variables on the engine response, suggesting the well-trained models successfully identified the complex relationships and can help assist engine analysis. Overall, the neural network algorithm was appropriate for the application investigated in this study.

scholarly journals Effect of ignition timing on engine characteristics of a single cylinder spark ignition engine with CNG fueled

2017 ◽  
Vol 20 (K6) ◽  
pp. 79-86
Author(s):  
Quoc Dang Tran
Keyword(s):  
Natural Gas ◽  
Thermal Efficiency ◽  
Engine Speed ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Ignition Timing ◽  
Compressed Natural Gas ◽  
Brake Thermal Efficiency ◽  
Timing System ◽  
Cng Engine

This article shows an investigated research on Compressed Natural Gas (CNG) engine with a port injection when varying ignition timing. The obtained results from simulating study have indicated that both of brake thermal efficiency and torque have a similar trend when varying ignition timing. The effect of ignition timing on the value of brake thermal efficiency is stronger in comparison with torque, however, the increase in engine speed or lambda value have to adjust the ignition timing more early. To reach the maximum break torque at each engine speed, the ignition timing should be adjusted IT = 14 - 32 bTDC, and this is also basic value to design the ignition timing system using CNG engine with port injection.

Sign in / Sign up

Export Citation Format

Share Document