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    Uncertainty quantification analysis of RANS of spray jets

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    Name:
    EnergyPropulsion2020_Angelilli-2.pdf
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    Type
    Conference Paper
    Authors
    Ciottoli, Pietro Paolo
    Petrocchi, Andrea
    Angelilli, Lorenzo cc
    Hernandez Perez, Francisco
    Malpica Galassi, Riccardo
    Picano, Francesco
    Valorani, Mauro
    Im, Hong G. cc
    KAUST Department
    Clean Combustion Research Center
    Computational Reacting Flow Laboratory (CRFL)
    Mechanical Engineering
    Mechanical Engineering Program
    Physical Science and Engineering (PSE) Division
    KAUST Grant Number
    OSR-2019-CCF-1975-35
    Date
    2020-08-17
    Online Publication Date
    2020-08-17
    Print Publication Date
    2020-08-24
    Permanent link to this record
    http://hdl.handle.net/10754/664796
    
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    Abstract
    Parametric uncertainty is propagated through Reynolds-averaged Navier-Stokes (RANS) computations of a prototypical acetone/air aerosol stream flowing in a dry air environment. Two parameters are considered as uncertain: the inflow velocity dissipation and a coefficient that blends the discrete random walk and the gradient-based dispersion models. A Bayesian setting is employed to represent the degree of belief about the parameters of interest in terms of probability theory, such that uncertainty is described with probability density functions. Random variables are represented by means of polynomial chaos expansions. The sensitivity of mean axial velocity and mean vapor mass fraction to the uncertain parameters is discussed.
    Citation
    Ciottoli, P. P., Petrocchi, A., Angelilli, L., Hernandez Perez, F. E., Malpica Galassi, R., Picano, F., … Im, H. G. (2020). Uncertainty quantification analysis of RANS of spray jets. AIAA Propulsion and Energy 2020 Forum. doi:10.2514/6.2020-3882
    Sponsors
    The authors acknowledge the support of the Italian Ministry of University and Research (MIUR) and King Abdullah University of Science and Technology OSR-2019-CCF-1975-35 Subaward Agreement. Computational resources were provided by the KAUST Supercomputing Laboratory (KSL).
    Publisher
    American Institute of Aeronautics and Astronautics
    ISBN
    9781624106026
    DOI
    10.2514/6.2020-3882
    Additional Links
    https://arc.aiaa.org/doi/10.2514/6.2020-3882
    ae974a485f413a2113503eed53cd6c53
    10.2514/6.2020-3882
    Scopus Count
    Collections
    Conference Papers; Physical Science and Engineering (PSE) Division; Mechanical Engineering Program; Clean Combustion Research Center

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