Tag: nonlinear dynamics
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Substructuring techniques and pROMs for the optimization of a MEMS voice recognition device
This thesis aims to develop strategies to alleviate the computational burden of optimising a novel, purely mechanical voice recognition device. The device is composed of many repeated cells, each slightly varied through parametric optimisation of a high-fidelity finite element model with millions of degrees of freedom. Direct simulation of such models is computationally prohibitive, making
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Optimization of systems with Internal Resonances
MEMS design is usually done following linear vibration theory, however, their real behavior often turns out to be nonlinear. To address this issue, our group developed optimization algorithms to target backbone curves, which are striclty connected to the nonlinearity of the vibration modes. As of now, the backbone computation can target only a single mode.
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ROM for large rotations in a MEMS micromirror
MEMS micromirrors are micro components used to deflect light rays in many applications, such as to build projectors. Mainly due to their large rotations, these devices exhibit a nonlinear behavior that challenges the construction of traditional nonlinear Reduced Order Models. In this thesis, the candidate will investigate the subject of large rotations (moving on from
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Parameter continuation of SSM for parametrized defects
In this project we aim to develop an analytic formulation for Spectral Submanifolds (SSMs) depending on a parameter. In particular, the focus is on a formulation for shape defects previously studied in Marconi et al., where a different MOR strategy was used. Indeed, the problem of shape defects is very important in the MEMS field industry,
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Adjoint formulation for backbone curve computation at arbitrary order
We have recently proposed a method to tailor the nonlinear response of a structure using Spectral Submanifolds (SSM, or Lyapunov Subcenter Manifolds – LSM) and an optimization procedure1. By defining a set of frequency-amplitude pairs we can fit the backbone curve of the system to these points. The computation of the sensitivities of the LSM,