The Experts below are selected from a list of 260913 Experts worldwide ranked by ideXlab platform
Bruno Lombard - One of the best experts on this subject based on the ideXlab platform.
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Time domain Numerical Modeling of wave propagation in 2D heterogeneous porous media
Journal of Computational Physics, 2011Co-Authors: Guillaume Chiavassa, Bruno LombardAbstract:This paper deals with the Numerical Modeling of wave propagation in porous media described by Biot's theory. The viscous efforts between the fluid and the elastic skeleton are assumed to be a linear function of the relative velocity, which is valid in the low-frequency range. The coexistence of propagating fast compressional wave and shear wave, and of a diffusive slow compressional wave, makes Numerical Modeling tricky. To avoid restrictions on the time step, the Biot's system is splitted into two parts: the propagative part is discretized by a fourth-order ADER scheme, while the diffusive part is solved analytically. Near the material interfaces, a space-time mesh refinement is implemented to capture the small spatial scales related to the slow compressional wave. The jump conditions along the interfaces are discretized by an immersed interface method. Numerical experiments and comparisons with exact solutions confirm the accuracy of the Numerical Modeling. The efficiency of the approach is illustrated by simulations of multiple scattering.
V Schulze - One of the best experts on this subject based on the ideXlab platform.
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experimental determination of process parameters and material data for Numerical Modeling of induction hardening
Journal of Materials Engineering and Performance, 2013Co-Authors: Maximilian Schwenk, Jennifer Hoffmeister, V SchulzeAbstract:Induction surface hardening is a widely used manufacturing process to improve the mechanical properties of components. However, better process understanding as well as process development requires Numerical Modeling. The Modeling itself depends on the input data in terms of process parameters and the material behavior. Data acquisition is a rather difficult task due to very short processing times, as seen in contour hardening of gears. The article will give an overview over critical aspects regarding the acquisition of input data. A short presentation of the Numerical model used to compare experimental and Numerical results shall promote better understanding for improving the Modeling or reducing the model complexity necessary for good predictability.
Mathias Obrebski - One of the best experts on this subject based on the ideXlab platform.
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detection of microseismic compressional p body waves aided by Numerical Modeling of oceanic noise sources
Journal of Geophysical Research, 2013Co-Authors: Mathias Obrebski, Fabrice Ardhuin, E Stutzmann, Martin SchimmelAbstract:[1] Among the different types of waves embedded in seismic noise, body waves present appealing properties but are still challenging to extract. Here we first validate recent improvements in Numerical Modeling of microseismic compressional (P) body waves and then show how this tool allows fast detection and location of their sources. We compute sources at ~0.2 Hz within typical P teleseismic distances (30–90°) from the Southern California Seismic Network and analyze the most significant discrete sources. The locations and relative strengths of the computed sources are validated by the good agreement with beam-forming analysis. These 54 noise sources exhibit a highly heterogeneous distribution, and cluster along the usual storm tracks in the Pacific and Atlantic oceans. They are mostly induced in the open ocean, at or near water depths of 2800 and 5600 km, most likely within storms or where ocean waves propagating as swell meet another swell or wind sea. We then emphasize two particularly strong storms to describe how they generate noise sources in their wake. We also use these two specific noise bursts to illustrate the differences between microseismic body and surface waves in terms of source distribution and resulting recordable ground motion. The different patterns between body and surface waves result from distinctive amplification of ocean wave-induced pressure perturbation and different seismic attenuation. Our study demonstrates the potential of Numerical Modeling to provide fast and accurate constraints on where and when to expect microseismic body waves, with implications for seismic imaging and climate studies.
Guillaume Chiavassa - One of the best experts on this subject based on the ideXlab platform.
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Time domain Numerical Modeling of wave propagation in 2D heterogeneous porous media
Journal of Computational Physics, 2011Co-Authors: Guillaume Chiavassa, Bruno LombardAbstract:This paper deals with the Numerical Modeling of wave propagation in porous media described by Biot's theory. The viscous efforts between the fluid and the elastic skeleton are assumed to be a linear function of the relative velocity, which is valid in the low-frequency range. The coexistence of propagating fast compressional wave and shear wave, and of a diffusive slow compressional wave, makes Numerical Modeling tricky. To avoid restrictions on the time step, the Biot's system is splitted into two parts: the propagative part is discretized by a fourth-order ADER scheme, while the diffusive part is solved analytically. Near the material interfaces, a space-time mesh refinement is implemented to capture the small spatial scales related to the slow compressional wave. The jump conditions along the interfaces are discretized by an immersed interface method. Numerical experiments and comparisons with exact solutions confirm the accuracy of the Numerical Modeling. The efficiency of the approach is illustrated by simulations of multiple scattering.
Maximilian Schwenk - One of the best experts on this subject based on the ideXlab platform.
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experimental determination of process parameters and material data for Numerical Modeling of induction hardening
Journal of Materials Engineering and Performance, 2013Co-Authors: Maximilian Schwenk, Jennifer Hoffmeister, V SchulzeAbstract:Induction surface hardening is a widely used manufacturing process to improve the mechanical properties of components. However, better process understanding as well as process development requires Numerical Modeling. The Modeling itself depends on the input data in terms of process parameters and the material behavior. Data acquisition is a rather difficult task due to very short processing times, as seen in contour hardening of gears. The article will give an overview over critical aspects regarding the acquisition of input data. A short presentation of the Numerical model used to compare experimental and Numerical results shall promote better understanding for improving the Modeling or reducing the model complexity necessary for good predictability.
