10 research outputs found

    Nondestructive monitoring of ageing of Alkali resistant Glass fiber reinforced cement (GRC)

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    Glass fiber reinforced cement (GRC) is a composite material made of portland cement mortar and alkali resistant (AR) fibers. AR fibers are added to portland cement to give the material additional flexural strength and toughness. However, ageing deteriorates the fibers and as a result the improvement in the mechanical properties resulted from the fiber addition disappears as the structure becomes old. The aim of this paper is monitoring GRC ageing by nondestructive evaluation (NDE) techniques. Two different NDE techniques (1) nonlinear impact resonant acoustic spectroscopy analysis and (2) propagating ultrasonic guided waves are used for this purpose. Both techniques revealed a reduction of the nonlinear behavior in the GRC material with ageing. Specimens are then loaded to failure to obtain their strength and stiffness. Compared to the un-aged specimens, the aged specimens are found to exhibit more linear behavior, have more stiffness but less toughness. Finally, undisturbed fragments on the fracture surface from mechanical tests are inspected under the electron microscope, to understand the fundamental mechanisms that cause the change in the GRC behavior with ageing.The authors want to acknowledge the financial support of the Ministerio de Ciencia e Innovacion MICINN, Spain, and FEDER funding (Ondacem Project: BIA 2010-19933) and BES-2011-044624. Also thanks to PAID-02-11 Program from Universitat Politecnica de Valencia.Eiras Fernández, JN.; Kundu, T.; Bonilla Salvador, MM.; Paya Bernabeu, JJ. (2013). Nondestructive monitoring of ageing of Alkali resistant Glass fiber reinforced cement (GRC). Journal of Nondestructive Evaluation - NDT and E International. 32:300-314. https://doi.org/10.1007/s10921-013-0183-yS30031432Bentur, A., Fibre, M.S.: Reinforced Cementitious Composites, 2nd edn. Taylor and Francis, New York (2007)Purnell, P., Short, N.R., Page, C.L.: A static fatigue model for the durability of glass fibre reinforced cement. J. 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    Paleomagnetism of the Amazonian Craton and its role in paleocontinents

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    Enhanced performance in fusion plasmas through turbulence suppression by megaelectronvolt ions

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    © 2022, The Author(s), under exclusive licence to Springer Nature Limited.Alpha particles with energies on the order of megaelectronvolts will be the main source of plasma heating in future magnetic confinement fusion reactors. Instead of heating fuel ions, most of the energy of alpha particles is transferred to electrons in the plasma. Furthermore, alpha particles can also excite Alfvénic instabilities, which were previously considered to be detrimental to the performance of the fusion device. Here we report improved thermal ion confinement in the presence of megaelectronvolts ions and strong fast ion-driven Alfvénic instabilities in recent experiments on the Joint European Torus. Detailed transport analysis of these experiments reveals turbulence suppression through a complex multi-scale mechanism that generates large-scale zonal flows. This holds promise for more economical operation of fusion reactors with dominant alpha particle heating and ultimately cheaper fusion electricity.N

    Disruption prediction with artificial intelligence techniques in tokamak plasmas

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    In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100 million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape of a torus. Light nuclei, such as deuterium and tritium, undergo a fusion reaction that releases energy, making fusion a promising option for a sustainable and clean energy source. Tokamak plasmas, however, are prone to disruptions as a result of a sudden collapse of the system terminating the fusion reactions. As disruptions lead to an abrupt loss of confinement, they can cause irreversible damage to present-day fusion devices and are expected to have a more devastating effect in future devices. Disruptions expected in the next-generation tokamak, ITER, for example, could cause electromagnetic forces larger than the weight of an Airbus A380. Furthermore, the thermal loads in such an event could exceed the melting threshold of the most resistant state-of-the-art materials by more than an order of magnitude. To prevent disruptions or at least mitigate their detrimental effects, empirical models obtained with artificial intelligence methods, of which an overview is given here, are commonly employed to predict their occurrence—and ideally give enough time to introduce counteracting measures
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