Failure assessment of layered composites subject to impact loadings: a finite element, sigma-point Kalman filter approach

We present a coupled finite element, Kalman filter approach to foresee impactinduced delamination of layered composites when mechanical properties are partially unknown. Since direct numerical simulations, which require all the constitutive parameters to be assigned, cannot be run in such cases, an inverse problem is formulated to allow for modeling as well as constitutive uncertainties. Upon space discretization through finite elements and time integration through the explicit ®¡method, the resulting nonlinear stochastic state model, wherein nonlinearities are due to delamination growth, is attacked with sigma-point Kalman filtering. Comparison with experimental data available in the literature and concerning inter-laminar failure of layered composites subject to low-velocity impacts, shows that the proposed procedure leads to: an accurate description of the failure mode; converged estimates of inter-laminar strength and toughness in good agreement with experimental data

Analysis of the Molecules of Knowledge Model with the BioPepa Eclipse Plugin

In this brief technical report, we investigate the opportunity of exploiting biochemical processes simulation tools – in particular, BioPepa – to experiment about parameter tuning in chemical-like coordination models—in particular, MoK

On-Line Instruction-checking in Pipelined Microprocessors

Microprocessors performances have increased by more than five orders of magnitude in the last three decades. As technology scales down, these components become inherently unreliable posing major design and test challenges. This paper proposes an instruction-checking architecture to detect erroneous instruction executions caused by both permanent and transient errors in the internal logic of a microprocessor. Monitoring the correct activation sequence of a set of predefined microprocessor control/status signals allow distinguishing between correctly and not correctly executed instruction

Uncertainty quantification of microstructure-governed properties of polysilicon MEMS

In this paper, we investigate the stochastic effects of the microstructure of polysilicon films on the overall response of microelectromechanical systems (MEMS). A device for on-chip testing has been purposely designed so as to maximize, in compliance with the production process, its sensitivity to fluctuations of the microstructural properties; as a side effect, its sensitivity to geometrical imperfections linked to the etching process has also been enhanced. A reduced-order, coupled electromechanical model of the device is developed and an identification procedure, based on a genetic algorithm, is finally adopted to tune the parameters ruling microstructural and geometrical uncertainties. Besides an initial geometrical imperfection that can be considered specimen-dependent due to its scattering, the proposed procedure has allowed identifying an average value of the effective polysilicon Young's modulus amounting to 140 GPa, and of the over-etch depth with respect to the target geometry layout amounting to O = -0.09 µm. The procedure has been therefore shown to be able to assess how the studied stochastic effects are linked to the scattering of the measured input-output transfer function of the device under standard working conditions. With a continuous trend in miniaturization induced by the mass production of MEMS, this study can provide information on how to handle the foreseen growth of such scattering