Uncertainty quantification analysis of RANS of spray jets

Pietro Paolo Ciottoli; Andrea Petrocchi; Lorenzo Angelilli; Francisco Hernandez Perez; Riccardo Malpica Galassi; Francesco Picano; Mauro Valorani; Hong G. Im

doi:10.2514/6.2020-3882

Uncertainty quantification analysis of RANS of spray jets

Pietro Paolo Ciottoli, Andrea Petrocchi, Lorenzo Angelilli, Francisco Hernandez Perez, Riccardo Malpica Galassi, Francesco Picano, Mauro Valorani, Hong G. Im

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

5 Scopus citations

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.

Original language	English (US)
Title of host publication	AIAA Propulsion and Energy 2020 Forum
Publisher	American Institute of Aeronautics and Astronautics
ISBN (Print)	9781624106026
DOIs	https://doi.org/10.2514/6.2020-3882
State	Published - Aug 17 2020

Access to Document

10.2514/6.2020-3882

Cite this

@inproceedings{b28a7ba68957459f86ba4030a6c2b8b3,

title = "Uncertainty quantification analysis of RANS of spray jets",

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.",

author = "Ciottoli, {Pietro Paolo} and Andrea Petrocchi and Lorenzo Angelilli and {Hernandez Perez}, Francisco and {Malpica Galassi}, Riccardo and Francesco Picano and Mauro Valorani and Im, {Hong G.}",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): OSR-2019-CCF-1975-35 Acknowledgements: 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).",

year = "2020",

month = aug,

day = "17",

doi = "10.2514/6.2020-3882",

language = "English (US)",

isbn = "9781624106026",

booktitle = "AIAA Propulsion and Energy 2020 Forum",

publisher = "American Institute of Aeronautics and Astronautics",

}

TY - GEN

T1 - Uncertainty quantification analysis of RANS of spray jets

AU - Ciottoli, Pietro Paolo

AU - Petrocchi, Andrea

AU - Angelilli, Lorenzo

AU - Hernandez Perez, Francisco

AU - Malpica Galassi, Riccardo

AU - Picano, Francesco

AU - Valorani, Mauro

AU - Im, Hong G.

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): OSR-2019-CCF-1975-35 Acknowledgements: 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).

PY - 2020/8/17

Y1 - 2020/8/17

N2 - 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.

AB - 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.

UR - http://hdl.handle.net/10754/664796

UR - https://arc.aiaa.org/doi/10.2514/6.2020-3882

U2 - 10.2514/6.2020-3882

DO - 10.2514/6.2020-3882

M3 - Conference contribution

SN - 9781624106026

BT - AIAA Propulsion and Energy 2020 Forum

PB - American Institute of Aeronautics and Astronautics

ER -

Uncertainty quantification analysis of RANS of spray jets

Abstract

Access to Document

Other files and links

Fingerprint

Cite this