• Login
    View Item 
    •   Home
    • Research
    • Conference Papers
    • View Item
    •   Home
    • Research
    • Conference Papers
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    Combustion System Optimization of a Light-Duty GCI Engine Using CFD and Machine Learning

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Type
    Conference Paper
    Authors
    Badra, Jihad
    KHALED, Fethi cc
    Sim, Jaeheon
    Pei, Yuanjiang
    Viollet, Yoann
    Pal, Pinaki
    Futterer, Carsten
    Brenner, Mattia
    Som, Sibendu
    Farooq, Aamir cc
    Chang, Junseok
    KAUST Department
    Chemical Kinetics & Laser Sensors Laboratory
    Clean Combustion Research Center
    Mechanical Engineering Program
    Physical Science and Engineering (PSE) Division
    Date
    2020-04-14
    Embargo End Date
    2020-10-14
    Permanent link to this record
    http://hdl.handle.net/10754/662953
    
    Metadata
    Show full item record
    Abstract
    In this study, the combustion system of a light-duty compression ignition engine running on a market gasoline fuel with Research Octane Number (RON) of 91 was optimized using computational fluid dynamics (CFD) and Machine Learning (ML). This work was focused on optimizing the piston bowl geometry at two compression ratios (CR) (17 and 18:1) and this exercise was carried out at full-load conditions (20 bar indicated mean effective pressure, IMEP). First, a limited manual piston design optimization was performed for CR 17:1, where a couple of pistons were designed and tested. Thereafter, a CFD design of experiments (DoE) optimization was performed where CAESES, a commercial software tool, was used to automatically perturb key bowl design parameters and CONVERGE software was utilized to perform the CFD simulations. At each compression ratio, 128 piston bowl designs were evaluated. Subsequently, a Machine Learning-Grid Gradient Algorithm (ML-GGA) approach was developed to further optimize the piston bowl design. This extensive optimization exercise yielded significant improvements in the engine performance and emissions compared to the baseline piston bowl designs. Up to 15% savings in indicated specific fuel consumption (ISFC) were obtained. Similarly, the optimized piston bowl geometries produced significantly lower emissions compared to the baseline. Emissions reductions up to 90% were obtained from this optimization exercise. The performances of the optimized piston bowl geometries were further validated at different operating conditions at the high-load point and at part-load conditions (6 bar IMEP) and compared with those of the baseline designs. The dependence of the engine performance on the piston bowl geometry at part-loads was lower than that at high-loads because injections normally occurred earlier (-60 to-20 CAD after top dead center (aTDC)) where minimal interactions between the spray and piston were anticipated. The interactions between late injections (-3 to 3 CAD aTDC) and piston geometry at high-loads significantly affected, fuel-air mixing, droplet breakup, combustion and emissions. It was also observed that heat losses, dictated by the interactions between the flame and piston surface, significantly affected the performance of the engine.
    Citation
    Badra, J., khaled, F., Sim, J., Pei, Y., Viollet, Y., Pal, P., … Chang, J. (2020). Combustion System Optimization of a Light-Duty GCI Engine Using CFD and Machine Learning. SAE Technical Paper Series. doi:10.4271/2020-01-1313
    Publisher
    SAE International
    Conference/Event name
    SAE 2020 World Congress Experience, WCX 2020
    DOI
    10.4271/2020-01-1313
    Additional Links
    https://www.sae.org/content/2020-01-1313/
    ae974a485f413a2113503eed53cd6c53
    10.4271/2020-01-1313
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Mechanical Engineering Program; Clean Combustion Research Center

    entitlement

     
    DSpace software copyright © 2002-2023  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.