Improved pseudo-force approach for Monte Carlo Simulation of non-linear fractional oscillators under stochastic excitation. (January 2023)

• Record Type: Journal Article
• Authors: Sofi, Alba; Muscolino, Giuseppe
• Is Part Of: Probabilistic engineering mechanics. Volume 71(2023)
• Publication Date: 2023-01
• Subjects: Fractional differential equations -- Stochastic processes -- Step-by-step integration -- Pseudo-force -- Non-linearities -- Monte Carlo simulation; Engineering -- Statistical methods -- Periodicals; Mechanics, Applied -- Statistical methods -- Periodicals; Probabilities -- Periodicals; Ingénierie -- Méthodes statistiques -- Périodiques; Mécanique appliquée -- Méthodes statistiques -- Périodiques; Probabilités -- Périodiques; 620.100727
• DOI: 10.1016/j.probengmech.2022.103403
• Languages: English
• ISSNs: 0266-8920

Abstract

Abstract: This paper presents a step-by-step procedure for the numerical integration of the fractional differential equation governing the response of single-degree-of-freedom (SDOF) non-linear systems endowed with fractional derivatives subjected to stochastic excitation. The procedure, labeled improved pseudo-force method ( IPFM ), is developed by extending a step-by-step integration scheme proposed by the second author for the numerical solution of classical differential equations. The IPFM relies on the following main steps: i) to use the Grünwald–Letnikov ( GL ) approximation of the fractional derivative; ii) to treat terms depending on the unknown values of the response, which result from the GL approximation as well as from the non-linear restoring forces, as pseudo-forces ; iii) to handle non-linearities by performing iterations at each time step. The IPFM provides accurate solutions by using time steps of larger size compared to classical step-by-step integration schemes. In this paper, the IPFM is applied within the framework of classical Monte Carlo Simulation ( MCS ) to evaluate the time domain dynamic response of non-linear fractional systems subjected to the generic sample of a stochastic excitation. Highlights: A numerical method for the solution of fractional differential equations is proposed. Non-linear fractional oscillators under stochastic excitation are analyzed. The Grünwald–Letnikov approximation of the fractional derivative is adopted. Terms depending on the unknown values of the response are treated as pseudo-forces . The method is able to reduce the computational effort of Monte Carlo Simulation.