A three-equation model for the prediction of soot emissions in LES of gas turbines

Franzelli, Benedetta; Vié, Aymeric; Darabiha, Nasser

doi:10.1016/j.proci.2018.05.061

Cited by 30 publications

(17 citation statements)

References 26 publications

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“…Nonetheless, both LES-CMC and ISRN methods show very promising potential in capturing soot location even with a simplified soot model. These methods are comparable with approaches of other researchers using different turbulent combustion closure methods with more advanced soot models [4,[28][29][30][31]. In terms of magnitude, however, both simulations over-predict the total soot volume fraction, as also observed with other studies using semi-empirical two-equation soot models [32,33] in this burner.…”

Section: Resultssupporting

confidence: 86%

Incompletely Stirred Reactor Network Modeling of a Model Gas Turbine Combustor

Gkantonas

Giusti

Mastorakos

2020

AIAA Scitech 2020 Forum

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The prediction of soot emissions is a requirement for the development of gas turbine combustors. Apart from global quantities related to soot mass, future regulations also call for the control of particle number. Therefore, theoretical models for soot from combustion devices must include various nucleation, growth, and oxidation mechanisms and aerosol physics in order to predict the soot particle number distribution. This paper introduces an approach based on Incompletely Stirred Reactor Network (ISRN) modeling that simplifies calculations and facilitates parametric analyses with very complex soot models. The method is based on the Conditional Moment Closure (CMC) combustion model and Incompletely Stirred Reactor (ISR) theory, which is here extended to a reactor network formulation. An ISR is a volume that is inhomogeneous in terms of mixture fraction but is characterized by homogeneous conditional averages, such as temperature conditioned on the mixture fraction having a particular value. A network of ISRs can then be deployed to separately capture soot production and oxidation regions exhibiting different degrees of micro-mixing rates and residence times, as typically observed in practical combustors. The ISRN approach is demonstrated in this paper for a model aero-engine combustor for which detailed CFD and experimental data exist, and it is found that reasonable accuracy is produced at a significantly reduced computational cost.

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Section: Resultssupporting

confidence: 86%

Incompletely Stirred Reactor Network Modeling of a Model Gas Turbine Combustor

Gkantonas

Giusti

Mastorakos

2020

AIAA Scitech 2020 Forum

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“…Background and comprehensive reviews on these subjects may be found in Ramkrishna (2000); Fox (2003); Marchisio & Fox (2007) and references therein. Along these lines, a large variety of numerical approaches have been discussed in the literature to simulate crystallisation in liquid (Qamar et al, 2007), carbon and soot formation in flames (Leung et al, 1991;Balthasar & Kraft, 2003;Ma et al, 2005;Lindstedt & Louloudi, 2005;Zucca et al, 2006;Patterson & Kraft, 2007;Eberle et al, 2017;Sewerin & Rigopoulos, 2017;Rodrigues et al, 2018;Aubagnac-Karkar et al, 2018;Schiener & Lindstedt, 2019;Franzelli et al, 2019) and many other chemical engineering applications with noninertial particles. Works have focused on numerical methods for the direct solving of the particle size distribution after discretisation of the phenomena driving its time evolution (Gelbard & Seinfeld, 1978;Hounslow et al, 1988;Lister et al, 1995;Kumar & Ramkrishna, 1996a,b, 1997Rigopoulos & Jones, 2003;Filbet & Laurenot, 2004;Park & Rogak, 2004;Qamar et al, 2007;Nguyen et al, 2016;Sewerin & Rigopoulos, 2017), while others adress the problems from moments of the distribution (Frenklach, 2002;Mueller et al, 2009;Salenbauch et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A hybrid stochastic/fixed-sectional method for solving the population balance equation

Bouaniche

Domingo

2019

Chemical Engineering Science

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The dynamics of flowing non-inertial particles undergoing nucleation, surface growth/loss, agglomeration and sometimes breakage, is usually characterised by the particle size distribution function. This distribution evolves according to a population balance equation. A novel approach combining Monte Carlo and fixed-sectional methods is proposed to minimise the discretisation errors when solving the surface growth/loss term of the population balance equation. The approach relies on a fixed number of stochastic particles and sections, with a numerical algorithm organised to minimise errors even for a moderate number of stochastic particles and sections. Canonical test cases featuring nucleation, agglomeration, and surface growth/loss are simulated. Results against the analytical solutions confirm the improvement in accuracy of the novel approach compared with fixed-sectional methods for the same computational effort. The hybrid method is thus of particular interest for simulating problems where surface growth/loss dominates the particles physics.

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“…Neural networks for solving population balance equation bustion systems. [17][18][19] To progress in this direction, this paper reports an attempt in which the effort made in grid size resolution and precision of PSD solving, serves to train a set of neural networks for solving the population balance equation. Neural networks are subsequently coupled with the flow solution, thus allowing for a significant reduction of the computing cost, still preserving the accuracy provided by the numerical method generating the training database.…”

Section: Please Cite This Article As Doi:101063/50031144mentioning

confidence: 99%

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

Seltz

Domingo

2021

Physics of Fluids

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Numerical modeling of non-inertial particles dynamics is usually addressed by solving a population balance equation (PBE). In addition to space and time, a discretisation is required also in the particle-size space, covering a large range of variation controlled by strongly nonlinear phenomena. A novel approach is presented in which a hybrid stochastic/fixed-sectional method solving the PBE is used to train a combination of an artificial neural network (ANN) with a convolutional neural network (CNN) and recurrent long short-term memory artificial neural layers (LSTM). The hybrid stochastic/fixed-sectional method decomposes the problem into the total number density and the probability density function (PDF) of sizes, allowing for an accurate treatment of surface growth/loss. After solving for the transport of species and temperature, the input of the ANN is composed of the thermochemical parameters controling the particle physics and of the increment in time. The input of the CNN is the shape of the particle size distribution (PSD) discretised in sections of size. From these inputs, in a flow simulation the ANN-CNN returns the PSD shape for the subsequent time step or a source term for the Eulerian transport of the particle size density. The method is evaluated in a canonical laminar premixed sooting flame of the literature and for a given level of accuracy (i.e., a given discretisation of the size space), a significant computing cost reduction is achieved (6 times faster compared to a sectional method with 10 sections and 30 times faster for 100 sections).

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A three-equation model for the prediction of soot emissions in LES of gas turbines

Cited by 30 publications

References 26 publications

Incompletely Stirred Reactor Network Modeling of a Model Gas Turbine Combustor

Incompletely Stirred Reactor Network Modeling of a Model Gas Turbine Combustor

A hybrid stochastic/fixed-sectional method for solving the population balance equation

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

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