Stochastic modeling of atomizing spray in a complex swirl injector using large eddy simulation

2009 ◽  
Vol 32 (2) ◽  
pp. 2257-2266 ◽  
Author(s):  
Sourabh V. Apte ◽  
Krishnan Mahesh ◽  
Michael Gorokhovski ◽  
Parviz Moin
Keyword(s):  
Large Eddy Simulation ◽  
Stochastic Modeling ◽  
Eddy Simulation ◽  
Large Eddy ◽  
Swirl Injector

Jet noise prediction via coupling large eddy simulation and stochastic modeling

2017 ◽  
Vol 142 (4) ◽  
pp. 2491-2491
Author(s):  
Joshua D. Blake ◽  
VASILEIOS SASSANIS ◽  
David Thompson ◽  
Adrian Sescu ◽  
Yuji Hattori
Keyword(s):  
Large Eddy Simulation ◽  
Stochastic Modeling ◽  
Jet Noise ◽  
Noise Prediction ◽  
Eddy Simulation ◽  
Large Eddy

scholarly journals Large-eddy simulation and stochastic modeling of Lagrangian particles for footprint determination in the stable boundary layer

2016 ◽  
Vol 9 (9) ◽  
pp. 2925-2949 ◽  
Author(s):  
Andrey Glazunov ◽  
Üllar Rannik ◽  
Victor Stepanenko ◽  
Vasily Lykosov ◽  
Mikko Auvinen ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Large Eddy Simulation ◽  
Stochastic Modeling ◽  
Stochastic Models ◽  
Stable Boundary Layer ◽  
Particle Dispersion ◽  
Small Scale ◽  
Stable Boundary ◽  
Eddy Simulation ◽  
Large Eddy

Abstract. Large-eddy simulation (LES) and Lagrangian stochastic modeling of passive particle dispersion were applied to the scalar flux footprint determination in the stable atmospheric boundary layer. The sensitivity of the LES results to the spatial resolution and to the parameterizations of small-scale turbulence was investigated. It was shown that the resolved and partially resolved (“subfilter-scale”) eddies are mainly responsible for particle dispersion in LES, implying that substantial improvement may be achieved by using recovering of small-scale velocity fluctuations. In LES with the explicit filtering, this recovering consists of the application of the known inverse filter operator. The footprint functions obtained in LES were compared with the functions calculated with the use of first-order single-particle Lagrangian stochastic models (LSMs) and zeroth-order Lagrangian stochastic models – the random displacement models (RDMs). According to the presented LES, the source area and footprints in the stable boundary layer can be substantially more extended than those predicted by the modern LSMs.

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