Abstract
In a companion paper (Nair, P.B., and Keane, A.J., 'New Developments in Computaitonal Stochastic Mechanics, Part I: Theory', AIAA-2000-1827), stochastic reduced bsis approximation (SRBA) methods were presented for analysis of systems governed by stochastic partial differential equations (PDEs). The fundamental idea proposed was to use the terms of the Neumann expansion series as stochastic basis vectors along with undertermined coefficients for representing the response process. Solution procedures based on variants of the stochastic Bubnov-Galerkin scheme were developed for determining the coefficients of the reduced basis representation. This paper presents detailed numerical studies for two example problems from the domain of stochastic structural mechanics. The main objective here is to study the numerical characteristics of SRBA methods, and to compare the results with the Neumann expansion scheme. It is demonstrated that the SRBA methods give significantly better results as compared to the Neumann expansion scheme, particularly for large stochastic variations in the random system parameters.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
