Correlating low energy impact damage with changes in modal parameters: a preliminary study on composite beams

This paper is an experimental study of the effects of multi-site damage on the vibration response of a composite beam damaged by low energy impact. The variation of the modal parameters with different levels of impact energy and density of impact is studied. Specimens are impacted symmetrically in order to induce a global rate of damage. A damage detection tool Damage Index is introduced in order to verify the estimation of damping ratios. Design of Experiments is used to establish the sensitivity of both energy of impact and density of damage. The DOE analysis results (using natural frequency only) indicate that impact energy for 2nd, 3rd and 4th bending modes is the most significant factor contributing to the changes in the modal parameters for this kind of symmetrical dynamic test

Structural Material Property Tailoring Using Deep Neural Networks

Advances in robotics, artificial intelligence, and machine learning are ushering in a new age of automation, as machines match or outperform human performance. Machine intelligence can enable businesses to improve performance by reducing errors, improving sensitivity, quality and speed, and in some cases achieving outcomes that go beyond current resource capabilities. Relevant applications include new product architecture design, rapid material characterization, and life-cycle management tied with a digital strategy that will enable efficient development of products from cradle to grave. In addition, there are also challenges to overcome that must be addressed through a major, sustained research effort that is based solidly on both inferential and computational principles applied to design tailoring of functionally optimized structures. Current applications of structural materials in the aerospace industry demand the highest quality control of material microstructure, especially for advanced rotational turbomachinery in aircraft engines in order to have the best tailored material property. In this paper, deep convolutional neural networks were developed to accurately predict processing-structure-property relations from materials microstructures images, surpassing current best practices and modeling efforts. The models automatically learn critical features, without the need for manual specification and/or subjective and expensive image analysis. Further, in combination with generative deep learning models, a framework is proposed to enable rapid material design space exploration and property identification and optimization. The implementation must take account of real-time decision cycles and the trade-offs between speed and accuracy

Study of Three-Particle-One-Hole States in 14-C with the 11-B(a,p)14-C Reaction at 120 MeV

This work was supported by the National Science Foundation Grant NSF PHY 81-14339 and by Indiana Universit

Hybrid fiber reinforcement and crack formation in cementitious composite materials

The use of different types of fibers simultaneously for reinforcing cementitious matrices is motivated by the concept of a multi-scale nature of the crack propagation process. Fibers with different geometrical and mechanical properties are used to bridge cracks of different sizes from the micro- to the macroscale. In this study, the performance of different fiber reinforced cementitious composites is assessed in terms of their tensile stress-crack opening behavior. The results obtained from this investigation allow a direct quantitative comparison of the behavior obtained from the different fiber reinforcement systems. The research described in this paper shows that the multi-scale conception of cracking and the use of hybrid fiber reinforcements do not necessarily result in an improved tensile behavior of the composite. Particular material design requirements may nevertheless justify the use of hybrid fiber reinforcements.Fundação para a Ciência e a Tecnologia (FCT) - SFRH / BD / 36515 / 200

Desingularization of vortices for the Euler equation

We study the existence of stationary classical solutions of the incompressible Euler equation in the plane that approximate singular stationnary solutions of this equation. The construction is performed by studying the asymptotics of equation -\eps^2 \Delta u^\eps=(u^\eps-q-\frac{\kappa}{2\pi} \log \frac{1}{\eps})_+^p with Dirichlet boundary conditions and $q$ a given function. We also study the desingularization of pairs of vortices by minimal energy nodal solutions and the desingularization of rotating vortices.Comment: 40 page

Resolution of dark matter problem in f(T) gravity

In this paper, we attempt to resolve the dark matter problem in f(T) gravity. Specifically, from our model we successfully obtain the flat rotation curves of galaxies containing dark matter. Further, we obtain the density profile of dark matter in galaxies. Comparison of our analytical results shows that our torsion-based toy model for dark matter is in good agreement with empirical data-based models. It shows that we can address the dark matter as an effect of torsion of the space.Comment: 14 pages, 3 figure

Teleparallel Gravity and Dimensional Reductions of Noncommutative Gauge Theory

We study dimensional reductions of noncommutative electrodynamics on flat space which lead to gauge theories of gravitation. For a general class of such reductions, we show that the noncommutative gauge fields naturally yield a Weitzenbock geometry on spacetime and that the induced diffeomorphism invariant field theory can be made equivalent to a teleparallel formulation of gravity which macroscopically describes general relativity. The Planck length is determined in this setting by the Yang-Mills coupling constant and the noncommutativity scale. The effective field theory can also contain higher-curvature and non-local terms which are characteristic of string theory. Some applications to D-brane dynamics and generalizations to include the coupling of ordinary Yang-Mills theory to gravity are also described.Comment: 31 pages LaTeX; References adde

Pre-M Phase-promoting Factor Associates with Annulate Lamellae in Xenopus Oocytes and Egg Extracts

We have used complementary biochemical and in vivo approaches to study the compartmentalization of M phase-promoting factor (MPF) in prophase Xenopus eggs and oocytes. We first examined the distribution of MPF (Cdc2/CyclinB2) and membranous organelles in high-speed extracts of Xenopus eggs made during mitotic prophase. These extracts were found to lack mitochondria, Golgi membranes, and most endoplasmic reticulum (ER) but to contain the bulk of the pre-MPF pool. This pre-MPF could be pelleted by further centrifugation along with components necessary to activate it. On activation, Cdc2/CyclinB2 moved into the soluble fraction. Electron microscopy and Western blot analysis showed that the pre-MPF pellet contained a specific ER subdomain comprising "annulate lamellae" (AL): stacked ER membranes highly enriched in nuclear pores. Colocalization of pre-MPF with AL was demonstrated by anti-CyclinB2 immunofluorescence in prophase oocytes, in which AL are positioned close to the vegetal surface. Green fluorescent protein-CyclinB2 expressed in oocytes also localized at AL. These data suggest that inactive MPF associates with nuclear envelope components just before activation. This association may explain why nuclei and centrosomes stimulate MPF activation and provide a mechanism for targeting of MPF to some of its key substrates