Journal
JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME
Volume 143, Issue 2, Pages -Publisher
ASME
DOI: 10.1115/1.4047978
Keywords
machine learning; internal combustion engine; optimization
Categories
Funding
- Transport Technologies Division at Saudi Aramco RDC
- Aramco Services Company
- U.S. Department of Energy (DOE) Office of Science Laboratory [DE-AC02-06CH11357]
- U.S. DOE Office of Vehicle Technologies, Office of Energy Efficiency and Renewable Energy [DE-AC02-06CH11357]
Ask authors/readers for more resources
Gasoline compression ignition (GCI) engines offer an attractive alternative to traditional engines, and a Machine Learning-Grid Gradient Ascent (ML-GGA) approach was developed to optimize their performance. The ML-GGA model improved accuracy and robustness through detailed investigations of optimization solver parameters and variable limit extension. This approach yielded >2% improvements in the merit function for a heavy-duty diesel engine running on gasoline fuel, showcasing its potential to significantly reduce optimization time without sacrificing accuracy.
Gasoline compression ignition (GCI) engines are considered an attractive alternative to traditional spark-ignition and diesel engines. In this work, a Machine Learning-Grid Gradient Ascent (ML-GGA) approach was developed to optimize the performance of internal combustion engines. ML offers a pathway to transform complex physical processes that occur in a combustion engine into compact informational processes. The developed ML-GGA model was compared with a recently developed Machine Learning-Genetic Algorithm (ML-GA). Detailed investigations of optimization solver parameters and variable limit extension were performed in the present ML-GGA model to improve the accuracy and robustness of the optimization process. Detailed descriptions of the different procedures, optimization tools, and criteria that must be followed for a successful output are provided here. The developed ML-GGA approach was used to optimize the operating conditions (case 1) and the piston bowl design (case 2) of a heavy-duty diesel engine running on a gasoline fuel with a research octane number (RON) of 80. The ML-GGA approach yielded >2% improvements in the merit function, compared with the optimum obtained from a thorough computational fluid dynamics (CFD) guided system optimization. The predictions from the ML-GGA approach were validated with engine CFD simulations. This study demonstrates the potential of ML-GGA to significantly reduce the time needed for optimization problems, without loss in accuracy compared with traditional approaches.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available