Stochastic optimization of nonlinear energy sinks using resonance-based clustering

Ethan Boroson, Samy Missoum

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Nonlinear energy sinks (NESs) are promising devices for achieving passive vibration mitigation. Unlike traditional tuned mass dampers (TMDs), NESs, characterized by nonlinear stiffness properties, are not tuned to specific frequencies and absorb energy over a wider range of frequencies. NES efficiency is achieved through time-limited resonances, leading to the capture and dissipation of energy. However, the efficiency with which a NES dissipates energy is highly dependent on design parameters and loading conditions. In fact, it has been shown that a NES can exhibit a near-discontinuous efficiency. Thus, NES optimal design must account for uncertainty. The premise of the stochastic optimization method proposed is the segregation of efficiency regions separated by discontinuities in potentially high dimensional space. Clustering, support vector machine classification, and dedicated adaptive sampling constitute the basic techniques for maximizing the expected value of NES efficiency. Previous works depended solely on the ratio of energy dissipated by the NES for clustering. This work also includes information about the type of m:p resonances present. Three examples of optimization for the maximization of the expected value of efficiency for NESs subjected to transient loading are presented. The optimization accounts for both design variables with uncertainty and aleatory variables to characterize loading.

Original languageEnglish (US)
Title of host publicationASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
PublisherAmerican Society of Mechanical Engineers (ASME)
Volume4B
ISBN (Electronic)9780791850558
DOIs
StatePublished - 2016
EventASME 2016 International Mechanical Engineering Congress and Exposition, IMECE 2016 - Phoenix, United States
Duration: Nov 11 2016Nov 17 2016

Other

OtherASME 2016 International Mechanical Engineering Congress and Exposition, IMECE 2016
CountryUnited States
CityPhoenix
Period11/11/1611/17/16

Fingerprint

Support vector machines
Stiffness
Sampling
Uncertainty
Optimal design

ASJC Scopus subject areas

  • Mechanical Engineering

Cite this

Boroson, E., & Missoum, S. (2016). Stochastic optimization of nonlinear energy sinks using resonance-based clustering. In ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE) (Vol. 4B). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/IMECE201667115

Stochastic optimization of nonlinear energy sinks using resonance-based clustering. / Boroson, Ethan; Missoum, Samy.

ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE). Vol. 4B American Society of Mechanical Engineers (ASME), 2016.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Boroson, E & Missoum, S 2016, Stochastic optimization of nonlinear energy sinks using resonance-based clustering. in ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE). vol. 4B, American Society of Mechanical Engineers (ASME), ASME 2016 International Mechanical Engineering Congress and Exposition, IMECE 2016, Phoenix, United States, 11/11/16. https://doi.org/10.1115/IMECE201667115
Boroson E, Missoum S. Stochastic optimization of nonlinear energy sinks using resonance-based clustering. In ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE). Vol. 4B. American Society of Mechanical Engineers (ASME). 2016 https://doi.org/10.1115/IMECE201667115
Boroson, Ethan ; Missoum, Samy. / Stochastic optimization of nonlinear energy sinks using resonance-based clustering. ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE). Vol. 4B American Society of Mechanical Engineers (ASME), 2016.
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