• 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 LibguidePlumX LibguideSubmit an Item

    Statistics

    Display statistics

    Large eddy simulation with flamelet progress variable approach combined with artificial neural network acceleration

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Type
    Conference Paper
    Authors
    Angelilli, Lorenzo cc
    Ciottoli, Pietro Paolo
    Malpica Galassi, Riccardo
    Hernandez Perez, Francisco
    Soldan, Mattia
    Lu, Zhen
    Valorani, Mauro
    Im, Hong G. cc
    KAUST Department
    Mechanical Engineering
    Physical Science and Engineering (PSE) Division
    King Abdullah University of Science and Technology
    Clean Combustion Research Center
    Electrical Engineering
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Mechanical Engineering Program
    Date
    2021-01-04
    Permanent link to this record
    http://hdl.handle.net/10754/667594
    
    Metadata
    Show full item record
    Abstract
    In the context of large eddy simulation of turbulent reacting flows, flamelet-based models are key to affordable simulations of large and complex systems. However, as the complexity of the problem increases, higher-dimensional look-up tables are required, rendering the conventional look-up procedure too demanding. This work focuses on accelerating the estimation of flamelet- based data for the flamelet/progress variable model via an artificial neural network. The neural network hyper-parameters are defined by a Bayesian optimization and two different architectures are selected for comparison against the classical look-up procedure on the well known Sandia flame D. The performance in terms of execution time and accuracy are analyzed, showing that the neural network model reduces the computational time by 30%, as compared to the traditional table look-up, while retaining comparable accuracy.
    Citation
    Angelilli, L., Ciottoli, P. P., Malpica Galassi, R., Hernandez Perez, F. E., Soldan, M., Lu, Z., … Im, H. G. (2021). Large eddy simulation with flamelet progress variable approach combined with artificial neural network acceleration. AIAA Scitech 2021 Forum. doi:10.2514/6.2021-0412
    Sponsors
    The authors acknowledge the support of King Abdullah University of Science and Technology (KAUST). Computational resources were provided by the KAUST Supercomputing Laboratory (KSL). This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 682383).
    Publisher
    American Institute of Aeronautics and Astronautics
    ISBN
    9781624106095
    DOI
    10.2514/6.2021-0412
    Additional Links
    https://arc.aiaa.org/doi/10.2514/6.2021-0412
    ae974a485f413a2113503eed53cd6c53
    10.2514/6.2021-0412
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Mechanical Engineering Program; Clean Combustion Research Center; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2021  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    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.