TY - GEN
T1 - Optimization of a chain of nonlinear resonators for vibration mitigation
AU - Ahmadisoleymani, Seyed Saeed
AU - Missoum, Samy
N1 - Publisher Copyright:
© 2018, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
PY - 2018
Y1 - 2018
N2 - Chains of resonators in the form of spring-mass systems have long been known to exhibiting interesting properties such as band gaps. Such features can be leveraged to manipulate the propagation of waves such as the filtering of specific frequencies or, more generally, mitigate vibrations and impact. Adding nonlinearities to the system can also provide further avenues to manipulate the propagation of waves and enhance vibration mitigation. This work proposes to optimally design such a chain of nonlinear resonators to mitigate vibrations in a robust manner by accounting for various sources of uncertainties. The stochastic optimization algorithm explicitly accounts for the non-smoothness of the nonlinear response of the system due, for instance, to its amplitude-dependent behavior. In addition, the approach introduces a formulation based on a field representation of the design variables, thus making the optimization approach scalable.
AB - Chains of resonators in the form of spring-mass systems have long been known to exhibiting interesting properties such as band gaps. Such features can be leveraged to manipulate the propagation of waves such as the filtering of specific frequencies or, more generally, mitigate vibrations and impact. Adding nonlinearities to the system can also provide further avenues to manipulate the propagation of waves and enhance vibration mitigation. This work proposes to optimally design such a chain of nonlinear resonators to mitigate vibrations in a robust manner by accounting for various sources of uncertainties. The stochastic optimization algorithm explicitly accounts for the non-smoothness of the nonlinear response of the system due, for instance, to its amplitude-dependent behavior. In addition, the approach introduces a formulation based on a field representation of the design variables, thus making the optimization approach scalable.
UR - http://www.scopus.com/inward/record.url?scp=85051679907&partnerID=8YFLogxK
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U2 - 10.2514/6.2018-3105
DO - 10.2514/6.2018-3105
M3 - Conference contribution
AN - SCOPUS:85051679907
SN - 9781624105500
T3 - 2018 Multidisciplinary Analysis and Optimization Conference
BT - 2018 Multidisciplinary Analysis and Optimization Conference
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - 19th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2018
Y2 - 25 June 2018 through 29 June 2018
ER -