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    Engine combustion system optimization using CFD and machine learning: A methodological approach

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    ICEF2019-7238 (1).pdf
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    1.690Mb
    Format:
    PDF
    Description:
    Accepted manuscript
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    Type
    Conference Paper
    Authors
    Badra, Jihad
    KHALED, Fethi cc
    Tang, Meng
    Pei, Yuanjiang
    Kodavasal, Janardhan
    Pal, Pinaki
    Owoyele, Opeoluwa
    Fuetterer, Carsten
    Brenner, Mattia
    Farooq, Aamir cc
    KAUST Department
    Chemical Kinetics & Laser Sensors Laboratory
    Clean Combustion Research Center
    Mechanical Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2019-12-09
    Permanent link to this record
    http://hdl.handle.net/10754/661872
    
    Metadata
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    Abstract
    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 Algorithm (ML-GGA) approach was developed to optimize the performance of internal combustion engines. Machine learning (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 (MLGA). Detailed investigations of optimization solver parameters and variables limits 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 MLGGA 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 to 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 to traditional approaches.
    Citation
    Badra, J., Khaled, F., Tang, M., Pei, Y., Kodavasal, J., Pal, P., … Farooq, A. (2019). Engine Combustion System Optimization Using CFD and Machine Learning: A Methodological Approach. ASME 2019 Internal Combustion Engine Division Fall Technical Conference. doi:10.1115/icef2019-7238
    Sponsors
    This work has been supported by the Fuel Technology Division at Saudi Aramco R&DC. We would also like to thank Aramco Services Company for their support with the computing cluster at the Aramco Research Center Houston. The submitted manuscript was created partly by UChicago Argonne, LLC, Operator of Argonne National Laboratory. Argonne, a US Department of Energy (DOE) Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357. This research was partly funded by the US DOE Office of Vehicle Technologies, Office of Energy Efficiency and Renewable Energy under Contract No. DE-AC02-06CH11357. Blues High Performance LCRC cluster facilities at Argonne National Laboratory were used for some of the simulations.
    Publisher
    American Society of Mechanical Engineers
    Conference/Event name
    ASME 2019 Internal Combustion Engine Division Fall Technical Conference, ICEF 2019
    DOI
    10.1115/ICEF2019-7238
    Additional Links
    https://asmedigitalcollection.asme.org/ICEF/proceedings/ICEF2019/59346/Chicago,%20Illinois,%20USA/1071655
    ae974a485f413a2113503eed53cd6c53
    10.1115/ICEF2019-7238
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Mechanical Engineering Program; Clean Combustion Research Center

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