Non-local dynamic behavior of linear fiber reinforced materials

International audienceThis article deals with the effective dynamic behavior of elastic materials periodically reinforced by stiff linear slender elastic inclusions. By assuming a small scale ratio ε between the period section size and the characteristic size of the system global strain, and by weighing the constituents stiffness contrast by powers of ε, the dynamic macroscopic behavior at the leading order is derived through the asymptotic homogenization method of periodic media considering different frequency ranges. A two order stiffness contrast (μm/μp=O(ε^2)) is shown to lead to a dynamic macroscopic behavior spatially non-local in the transverse direction, where the system behaves as a generalized inner bending continuum, and temporally non-local in the axial direction, where the system behaves, at higher frequency, as a metamaterial in which internal resonance phenomena take place. The consequences of such non-localities on the reinforced medium modes are examined. The system axial and transverse modes are shown to be significantly different from those of usual composites

Network-based analysis of seismo-volcanic tremors

International audienceVolcanic tremors represent one of the most important class of seismo-volcanic signals due to their abundant presence in seismic records, their wealth of information regarding magmatic systems, their use as a tool for monitoring the state of volcanoes and their potential as precursor signals to eruptions. These signals have been analyzed for several decades with single station approaches, from which empirical inferences can be made regarding their sources, generation mechanism and scaling relations with eruptions parameters. Modernisation and densification of instrumentation networks coupled with sophistication of analysis methods and enhanced computation capacities, allow to switch from single-station to full seismic network based methods. We introduce in this chapter the interstation cross-correlations methods, the estimation of the network covariance matrix and the study of its eigenvalues and eigenvectors. Such advanced methods enable to measure temporal, spatial and spectral tremor properties. They are contained in the CovSeisNet Python package which has been used for characterizing various tremor episodes, including two examples from Kilauea volcano, Hawaii and Klyuchevskoy Volcanic Group, Kamchatka presented in this chapter. These application examples illustrate the complexity of tremors and emphasize the need to continue the development of new algorithms aimed at the exploration of network covariances to better constrain the different tremor generation processes that can be multiple, simultaneous and interacting. In particular, the combination of network-based analysis with polarization and machine learning approaches may represent a new step in our understanding of the underlying phenomena. In turn, this enhanced discernment of the involved processes and the links with the properties of the volcanic system can lead to a more effective monitoring and ultimately a better apprehension of volcanic system destabilizations and anticipation of the associated unrests

Milieux à double gradient pour matériaux renforcés. Modélisation théorique et expérimentale

On étudie des milieux élastiques souples périodiquement renforcés d'inclusions élancées beaucoup plus rigides. Par homogénéisation et analyse dimensionnelle, on établit les conditions induisant un couplage entre les comportements de poutre des inclusions et de cisaillement de la matrice. Contrairement au milieu de Cauchy usuel pour les composites, le modèle macroscopique intègre un effet de second gradient (flexion interne) à l'ordre dominant et diffère d'un milieu de Cosserat (malgré la rotation interne des poutres). On conclut par une validation expérimentale de ces résultats théoriques

Network-based analysis of seismo-volcanic tremors

International audienceVolcanic tremors represent one of the most important class of seismo-volcanic signals due to their abundant presence in seismic records, their wealth of information regarding magmatic systems, their use as a tool for monitoring the state of volcanoes and their potential as precursor signals to eruptions. These signals have been analyzed for several decades with single station approaches, from which empirical inferences can be made regarding their sources, generation mechanism and scaling relations with eruptions parameters. Modernisation and densification of instrumentation networks coupled with sophistication of analysis methods and enhanced computation capacities, allow to switch from single-station to full seismic network based methods. We introduce in this chapter the interstation cross-correlations methods, the estimation of the network covariance matrix and the study of its eigenvalues and eigenvectors. Such advanced methods enable to measure temporal, spatial and spectral tremor properties. They are contained in the CovSeisNet Python package which has been used for characterizing various tremor episodes, including two examples from Kilauea volcano, Hawaii and Klyuchevskoy Volcanic Group, Kamchatka presented in this chapter. These application examples illustrate the complexity of tremors and emphasize the need to continue the development of new algorithms aimed at the exploration of network covariances to better constrain the different tremor generation processes that can be multiple, simultaneous and interacting. In particular, the combination of network-based analysis with polarization and machine learning approaches may represent a new step in our understanding of the underlying phenomena. In turn, this enhanced discernment of the involved processes and the links with the properties of the volcanic system can lead to a more effective monitoring and ultimately a better apprehension of volcanic system destabilizations and anticipation of the associated unrests

Network-based analysis of seismo-volcanic tremors

International audienceVolcanic tremors represent one of the most important class of seismo-volcanic signals due to their abundant presence in seismic records, their wealth of information regarding magmatic systems, their use as a tool for monitoring the state of volcanoes and their potential as precursor signals to eruptions. These signals have been analyzed for several decades with single station approaches, from which empirical inferences can be made regarding their sources, generation mechanism and scaling relations with eruptions parameters. Modernisation and densification of instrumentation networks coupled with sophistication of analysis methods and enhanced computation capacities, allow to switch from single-station to full seismic network based methods. We introduce in this chapter the interstation cross-correlations methods, the estimation of the network covariance matrix and the study of its eigenvalues and eigenvectors. Such advanced methods enable to measure temporal, spatial and spectral tremor properties. They are contained in the CovSeisNet Python package which has been used for characterizing various tremor episodes, including two examples from Kilauea volcano, Hawaii and Klyuchevskoy Volcanic Group, Kamchatka presented in this chapter. These application examples illustrate the complexity of tremors and emphasize the need to continue the development of new algorithms aimed at the exploration of network covariances to better constrain the different tremor generation processes that can be multiple, simultaneous and interacting. In particular, the combination of network-based analysis with polarization and machine learning approaches may represent a new step in our understanding of the underlying phenomena. In turn, this enhanced discernment of the involved processes and the links with the properties of the volcanic system can lead to a more effective monitoring and ultimately a better apprehension of volcanic system destabilizations and anticipation of the associated unrests