In this work, the multidisciplinary design optimization (MDO) methodology is applied to a case arising in the automotive engineering in which the design optimization of mechanical and control features of a system are simultaneously carried out with an evolutionary algorithm based method. The system under study is the regulator of the injection pressure of an innovative Common Rail system for Compressed Natural Gas (CNG) automotive engines, whose engineering design includes several practical and numerical difficulties. To tackle such a situation, this paper proposes a constrained multi-objective optimization method, that pursues the Pareto-optimality on the basis of fitness functions that capture domain specific design aspects as well as static and dynamic objectives. The proposed scheme provides ways to incorporate the designers specific knowledge, from interactive actions to simulation based analysis or surrogate-assisted evolution. The computational experiments show the ability of the method for finding a relevant and satisfactory set of efficient solutions.
Multidisciplinary design optimization of a pressure controller for CNG injection systems / Dellino, G.; Lino, Paolo; Meloni, Carlo; Rizzo, A.. - (2006), pp. 2689-2694. (Intervento presentato al convegno The 2006 IEEE Conference on Computer Aided Control Systems Design - CACSD 2006 tenutosi a Munich, Germany nel October 4-6, 2006) [10.1109/CACSD.2006.285531].
Multidisciplinary design optimization of a pressure controller for CNG injection systems
LINO, Paolo;MELONI, Carlo;
2006-01-01
Abstract
In this work, the multidisciplinary design optimization (MDO) methodology is applied to a case arising in the automotive engineering in which the design optimization of mechanical and control features of a system are simultaneously carried out with an evolutionary algorithm based method. The system under study is the regulator of the injection pressure of an innovative Common Rail system for Compressed Natural Gas (CNG) automotive engines, whose engineering design includes several practical and numerical difficulties. To tackle such a situation, this paper proposes a constrained multi-objective optimization method, that pursues the Pareto-optimality on the basis of fitness functions that capture domain specific design aspects as well as static and dynamic objectives. The proposed scheme provides ways to incorporate the designers specific knowledge, from interactive actions to simulation based analysis or surrogate-assisted evolution. The computational experiments show the ability of the method for finding a relevant and satisfactory set of efficient solutions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
