This paper presents the development of an optimization process applied to a simplified aeroservoelastic aircraft model. More specifically, a concentrated parameter aeroservoelastic model is described and integrated within an optimization process, which although based on a model with low physical fidelity, has the main objectives and constraints of aeroelastic aircraft design and allows various design strategies to be explored quickly and effectively. A multi-objective genetic optimization algorithm allows for finding designs that aim at weight reduction and increasing lift-to-drag ratio. Next, specific fuel consumption is used as a criterion for choosing among solutions lying on the Pareto frontier. The solutions of the optimization process are first obtained by imposing only static aeroelastic constraints and then enhanced with flutter constraints. The use and optimization of control laws–including the topology of control surfaces enables the easing of aeroelastic constraints by allowing greater exploration of the design space with feaseble solutions.
Multi-disciplinary optimization for an aeroservoelastic simplified model / Colella, Marta; Saltari, Francesco; Mastroddi, Franco; Vetrano, Fabio. - (2022). (Intervento presentato al convegno International Forum on Aeroelasticity and Structural Dynamics, IFASD 2022 tenutosi a Madrid,Spain).
Multi-disciplinary optimization for an aeroservoelastic simplified model
Marta ColellaPrimo
;Francesco SaltariSecondo
;Franco MastroddiPenultimo
;
2022
Abstract
This paper presents the development of an optimization process applied to a simplified aeroservoelastic aircraft model. More specifically, a concentrated parameter aeroservoelastic model is described and integrated within an optimization process, which although based on a model with low physical fidelity, has the main objectives and constraints of aeroelastic aircraft design and allows various design strategies to be explored quickly and effectively. A multi-objective genetic optimization algorithm allows for finding designs that aim at weight reduction and increasing lift-to-drag ratio. Next, specific fuel consumption is used as a criterion for choosing among solutions lying on the Pareto frontier. The solutions of the optimization process are first obtained by imposing only static aeroelastic constraints and then enhanced with flutter constraints. The use and optimization of control laws–including the topology of control surfaces enables the easing of aeroelastic constraints by allowing greater exploration of the design space with feaseble solutions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.