Prediction of Pollutant Emissions from Lean Premixed Gas Turbine Combustor Using Chemical Reactor Network

Park, Jungkyu; Nguyen, Truc Huu; Lee, Min Chul; Chung, Jae-Wha

doi:10.3795/ksme-b.2012.36.2.225

Cited by 1 publication

(1 citation statement)

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“…This modelling technique has been applied to industrial furnaces (Faravelli et al, 2001) and burners (de Toni et al, 2013), showing its versatility towards different configurations. It has also been successfully applied to premixed combustion in gas turbines (Lee et al, 2011;Park et al, 2013). In this framework, several works (Hao, 2014;Lee et al, 2011;Nguyen, 2019;Nguyen et al, 2017;Novosselov et al, 2006) focused on the manual design of small-sized CRN of gas turbine combustors from CFD simulations carried out with simplified kinetics, in order to reduce the associated computational cost.…”

Section: Introductionmentioning

confidence: 99%

Automatic extraction of Chemical Reactor Networks from CFD data via advanced clustering algorithms

Savarese¹,

Cuoci²,

Paepe³

et al. 2022

Proceedings of Global Power &Amp; Propulsion Society

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The design of cleaner and more sustainable combustion technologies represents nowadays a key task. Reliable numerical models, able to cope with a large variety of configurations, combustion processes and fueling mixtures are needed, especially for future applications in combustion monitoring and control, for thermal and environmental performances, which are of critical importance. In this work, alternative, low-computational cost modelling tools for pollutants and thermal efficiency predictions, represented by Chemical Reactor Networks (CRN), are designed from Computational Fluid Dynamic (CFD) simulations assessing a novel methodology, by exploring new possibilities offered by Machine Learning (ML) algorithms. In particular, unsupervised learning approaches are employed, in order to extract the key features of the system flow-field, adopting advanced clustering algorithms, such as Local Principal Component Analysis (LPCA) and K-Means, thus providing an efficient and automatic identification of similar thermo-chemical state compartments in the computational domain. The identified zones are modelled in a post-processing phase as a network of interconnected chemical reactors, and detailed kinetic mechanisms are employed for low concentration pollutants predictions. The case study, a quasi-industrial, flameless-capable combustion furnace, fed with methane-hydrogen mixtures in different compositions at a nominal power of 15 kW, has been investigated numerically by performing 2D CFD simulations with reduced chemistry and subsequently CRN simulations has been carried out with detailed kinetics, adopting the aforementioned approach. Results are validated upon experimental data, in order to provide a novel methodology for CRN design applications, which can be suited for future GTs applications.

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