Name:
EnergyPropulsion2020_Angelilli-2.pdf
Size:
834.6Kb
Format:
PDF
Description:
Accepted Manuscript
Type
Conference PaperAuthors
Ciottoli, Pietro PaoloPetrocchi, Andrea
Angelilli, Lorenzo

Hernandez Perez, Francisco
Malpica Galassi, Riccardo
Picano, Francesco
Valorani, Mauro
Im, Hong G.

KAUST Department
Clean Combustion Research CenterComputational Reacting Flow Laboratory (CRFL)
Mechanical Engineering
Mechanical Engineering Program
Physical Science and Engineering (PSE) Division
KAUST Grant Number
OSR-2019-CCF-1975-35Date
2020-08-17Online Publication Date
2020-08-17Print Publication Date
2020-08-24Permanent link to this record
http://hdl.handle.net/10754/664796
Metadata
Show full item recordAbstract
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:10.2514/6.2020-3882Sponsors
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).ISBN
9781624106026Additional Links
https://arc.aiaa.org/doi/10.2514/6.2020-3882ae974a485f413a2113503eed53cd6c53
10.2514/6.2020-3882