Stochastic analysis of a non-local fractional viscoelastic bar forced by Gaussian white noise

Gioacchino Alotta, Francesco Paolo Pinnola, Francesco P. Pinnola, Alotta, Giuseppe Failla, Giuseppe Failla

    Risultato della ricerca: Articlepeer review

    15 Citazioni (Scopus)

    Abstract

    Recently, a displacement-based nonlocal bar model has been developed. The model is based on the assumption that nonlocal forces can be modeled as viscoelastic (VE) longrange interactions mutually exerted by nonadjacent bar segments due to their relative motion; the classical local stress resultants are also present in the model. A finite element (FE) formulation with closed-form expressions of the elastic and viscoelastic matrices has also been obtained. Specifically, Caputo’s fractional derivative has been used in order to model viscoelastic long-range interaction. The static and quasi-static response has been already investigated. This work investigates the stochastic response of the nonlocal fractional viscoelastic bar introduced in previous papers, discretized with the FEM, forced by a Gaussian white noise. Since the bar is forced by a Gaussian white noise, dynamical effects cannot be neglected. The system of coupled fractional differential equations ruling the bar motion can be decoupled only by means of the fractional order state variable expansion. It is shown that following this approach Monte Carlo simulation can be performed very efficiently. For simplicity, here the work is limited to the axial response, but can be easily extended to transverse motion.
    Lingua originaleEnglish
    Numero di pagine10
    RivistaASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS. PART B. MECHANICAL ENGINEERING
    Volume3
    Stato di pubblicazionePublished - 2017

    All Science Journal Classification (ASJC) codes

    • Mechanical Engineering
    • Safety, Risk, Reliability and Quality
    • Safety Research

    Fingerprint Entra nei temi di ricerca di 'Stochastic analysis of a non-local fractional viscoelastic bar forced by Gaussian white noise'. Insieme formano una fingerprint unica.

    Cita questo