M
Mohamed Fellah
Researcher at University of Science and Technology Houari Boumediene
Publications - 96
Citations - 545
Mohamed Fellah is an academic researcher from University of Science and Technology Houari Boumediene. The author has contributed to research in topics: Porous medium & Pairing. The author has an hindex of 12, co-authored 96 publications receiving 473 citations.
Papers
More filters
Journal ArticleDOI
Transient wave propagation in inhomogeneous porous materials: application of fractional derivatives
TL;DR: A wave-splitting technique is presented formally for the description of the dynamic equation of the reflection scattering operator and a generalized hyperbolic fractional equation for transient sound wave propagation in inhomogeneous material is established.
Journal ArticleDOI
Measuring permeability of porous materials at low frequency range via acoustic transmitted waves.
Zine El Abiddine Fellah,Mohamed Fellah,Farid G. Mitri,Naima Sebaa,Claude Depollier,Walter Lauriks +5 more
TL;DR: An acoustical transmission method is proposed for measuring permeability of porous materials having rigid frame based on a temporal model of the direct and inverse scattering problem for the diffusion of transient low frequency waves in a homogeneous isotropic slab of porous material having a rigid frame.
Journal ArticleDOI
An approach to direct and inverse time-domain scattering of acoustic waves from rigid porous materials by a fractional calculus based method
Journal ArticleDOI
Cluster decay investigation within a modified Woods–Saxon potential
TL;DR: In this paper, a unified fission model with the modified Woods-Saxon (MWS) nuclear potential was used to evaluate the cluster decay half-lives of heavy nuclei.
Journal ArticleDOI
Bayesian inference for the ultrasonic characterization of rigid porous materials using reflected waves by the first interface.
Rémi Roncen,Zine El Abiddine Fellah,Frank Simon,Estelle Piot,Mohamed Fellah,Erick Ogam,C. Depollier +6 more
TL;DR: The authors propose to solve the inverse problem numerically with a first level Bayesian inference method, summarizing the authors' knowledge on the inferred parameters in the form of posterior probability densities, exploring these densities using a Markov-Chain Monte-Carlo approach.