scholarly journals Solving the population balance equation for non-inertial particles dynamics using probability density function and neural networks: Application to a sooting flame

2021 ◽  
Vol 33 (1) ◽  
pp. 013311
Author(s):  
Andrea Seltz ◽  
Pascale Domingo ◽  
Luc Vervisch
Keyword(s):  
Neural Networks ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Balance Equation ◽  
Population Balance ◽  
Population Balance Equation ◽  
Inertial Particles ◽  
Particles Dynamics

scholarly journals A Lagrangian probability-density-function model for collisional turbulent fluid–particle flows

2019 ◽  
Vol 862 ◽  
pp. 449-489 ◽  
Author(s):  
A. Innocenti ◽  
R. O. Fox ◽  
M. V. Salvetti ◽  
S. Chibbaro
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Turbulent Flows ◽  
Isotropic Turbulence ◽  
Fluid Phase ◽  
Stochastic Modelling ◽  
Inertial Particles ◽  
Particle Phase ◽  
Dense Particle

Inertial particles in turbulent flows are characterised by preferential concentration and segregation and, at sufficient mass loading, dense particle clusters may spontaneously arise due to momentum coupling between the phases. These clusters, in turn, can generate and sustain turbulence in the fluid phase, which we refer to as cluster-induced turbulence (CIT). In the present work, we tackle the problem of developing a framework for the stochastic modelling of moderately dense particle-laden flows, based on a Lagrangian probability-density-function formalism. This framework includes the Eulerian approach, and hence can be useful also for the development of two-fluid models. A rigorous formalism and a general model have been put forward focusing, in particular, on the two ingredients that are key in moderately dense flows, namely, two-way coupling in the carrier phase, and the decomposition of the particle-phase velocity into its spatially correlated and uncorrelated components. Specifically, this last contribution allows us to identify in the stochastic model the contributions due to the correlated fluctuating energy and to the granular temperature of the particle phase, which determine the time scale for particle–particle collisions. The model is then validated and assessed against direct-numerical-simulation data for homogeneous configurations of increasing difficulty: (i) homogeneous isotropic turbulence, (ii) decaying and shear turbulence and (iii) CIT.

Rate of Convergence in Density Estimation Using Neural Networks

1996 ◽  
Vol 8 (5) ◽  
pp. 1107-1122 ◽  
Author(s):  
Dharmendra S. Modha ◽  
Elias Masry
Keyword(s):  
Neural Networks ◽  
Probability Density Function ◽  
Probability Density ◽  
Rate Of Convergence ◽  
Density Function ◽  
Density Estimation ◽  
Exponential Family ◽  
Density Estimator ◽  
Estimation Scheme ◽  
Hidden Layer

Given N i.i.d. observations {Xi}Ni=1 taking values in a compact subset of Rd, such that p* denotes their common probability density function, we estimate p* from an exponential family of densities based on single hidden layer sigmoidal networks using a certain minimum complexity density estimation scheme. Assuming that p* possesses a certain exponential representation, we establish a rate of convergence, independent of the dimension d, for the expected Hellinger distance between the proposed minimum complexity density estimator and the true underlying density p*.

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