Research engines with optical access can assist traditional engine development and optimization by providing first-hand information of in-cylinder combustion process. However, the fragility of the optical engine ecomponents (e.g., the see-thru windows are usually just fused silica) limit the engine operating conditions such as the maximum in-cylinder pressure and pressure rise rate. To make it easier to determine if a particular engine operating condition can be used for optical investigations, a back-propagation artificial neural network model was built to provide the values of parameters of interest for engine safety. The training data came from steady-state engine experiments that changed spark timing, mixture equivalence ratio, engine speed, and intake pressure, but using the non-optical configuration of the engine to widen the testing conditions. The comparison between model predictions and experimental data indicated the well-trained articicial neural network model can provide fast and consistent results, making it an easy-to-use tool for future optical engine investigations.

I repeated the abstract twice more because the 400-word minimum is excessive for a preliminary abstract, in my opinion.

Research engines with optical access can assist traditional engine development and optimization by providing first-hand information of in-cylinder combustion process. However, the fragility of the optical engine ecomponents (e.g., the see-thru windows are usually just fused silica) limit the engine operating conditions such as the maximum in-cylinder pressure and pressure rise rate. To make it easier to determine if a particular engine operating condition can be used for optical investigations, a back-propagation artificial neural network model was built to provide the values of parameters of interest for engine safety. The training data came from steady-state engine experiments that changed spark timing, mixture equivalence ratio, engine speed, and intake pressure, but using the non-optical configuration of the engine to widen the testing conditions. The comparison between model predictions and experimental data indicated the well-trained articicial neural network model can provide fast and consistent results, making it an easy-to-use tool for future optical engine investigations.

Research engines with optical access can assist traditional engine development and optimization by providing first-hand information of in-cylinder combustion process. However, the fragility of the optical engine ecomponents (e.g., the see-thru windows are usually just fused silica) limit the engine operating conditions such as the maximum in-cylinder pressure and pressure rise rate. To make it easier to determine if a particular engine operating condition can be used for optical investigations, a back-propagation artificial neural network model was built to provide the values of parameters of interest for engine safety. The training data came from steady-state engine experiments that changed spark timing, mixture equivalence ratio, engine speed, and intake pressure, but using the non-optical configuration of the engine to widen the testing conditions. The comparison between model predictions and experimental data indicated the well-trained articicial neural network model can provide fast and consistent results, making it an easy-to-use tool for future optical engine investigations.


Prediction of Efficient Operating Conditions Inside a Heavy-Duty Natural Gas Spark Ignition Engine Using Artificial Neural Networks

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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