The paper presents a multi-objective optimization procedure as applied to the design of the injection system of a Lean Pre-mixed Pre-vaporized combustion chamber. The optimizer drives an Artificial Neural Network in a repeated analysis scheme in order to simultaneously reduce NOX and CO pollutant emissions. The ANN is trained with a few three-dimensional high resolution reactive viscous flow simulations, car ried out with a reliable and robust CFD code. Results, obtained in a four-dimensional state space, demonstrate the validity of the overall procedure with truly moderate computational costs.
A multi-objective design optimization strategy as applied to pre-mixedpre-vaporized injection systems for low emission combustors / M., Laraia; Manna, Marcello; S., Colantuoni; P., Di Martino. - In: COMBUSTION THEORY AND MODELLING. - ISSN 1364-7830. - STAMPA. - 14:2(2010), pp. 203-233. [10.1080/13647831003746187]
A multi-objective design optimization strategy as applied to pre-mixedpre-vaporized injection systems for low emission combustors
MANNA, MARCELLO
;
2010
Abstract
The paper presents a multi-objective optimization procedure as applied to the design of the injection system of a Lean Pre-mixed Pre-vaporized combustion chamber. The optimizer drives an Artificial Neural Network in a repeated analysis scheme in order to simultaneously reduce NOX and CO pollutant emissions. The ANN is trained with a few three-dimensional high resolution reactive viscous flow simulations, car ried out with a reliable and robust CFD code. Results, obtained in a four-dimensional state space, demonstrate the validity of the overall procedure with truly moderate computational costs.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
